Performance comparison of optimal fractional order hybrid fuzzy PID controllers for handling oscillatory fractional order processes with dead time.

PubMed

Das, Saptarshi; Pan, Indranil; Das, Shantanu

2013-07-01

Fuzzy logic based PID controllers have been studied in this paper, considering several combinations of hybrid controllers by grouping the proportional, integral and derivative actions with fuzzy inferencing in different forms. Fractional order (FO) rate of error signal and FO integral of control signal have been used in the design of a family of decomposed hybrid FO fuzzy PID controllers. The input and output scaling factors (SF) along with the integro-differential operators are tuned with real coded genetic algorithm (GA) to produce optimum closed loop performance by simultaneous consideration of the control loop error index and the control signal. Three different classes of fractional order oscillatory processes with various levels of relative dominance between time constant and time delay have been used to test the comparative merits of the proposed family of hybrid fractional order fuzzy PID controllers. Performance comparison of the different FO fuzzy PID controller structures has been done in terms of optimal set-point tracking, load disturbance rejection and minimal variation of manipulated variable or smaller actuator requirement etc. In addition, multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) has been used to study the Pareto optimal trade-offs between the set point tracking and control signal, and the set point tracking and load disturbance performance for each of the controller structure to handle the three different types of processes. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Telerobotic control of a mobile coordinated robotic server. M.S. Thesis Annual Technical Report

NASA Technical Reports Server (NTRS)

Lee, Gordon

1993-01-01

The annual report on telerobotic control of a mobile coordinated robotic server is presented. The goal of this effort is to develop advanced control methods for flexible space manipulator systems. As such, an adaptive fuzzy logic controller was developed in which model structure as well as parameter constraints are not required for compensation. The work builds upon previous work on fuzzy logic controllers. Fuzzy logic controllers have been growing in importance in the field of automatic feedback control. Hardware controllers using fuzzy logic have become available as an alternative to the traditional PID controllers. Software has also been introduced to aid in the development of fuzzy logic rule-bases. The advantages of using fuzzy logic controllers include the ability to merge the experience and intuition of expert operators into the rule-base and that a model of the system is not required to construct the controller. A drawback of the classical fuzzy logic controller, however, is the many parameters needed to be turned off-line prior to application in the closed-loop. In this report, an adaptive fuzzy logic controller is developed requiring no system model or model structure. The rule-base is defined to approximate a state-feedback controller while a second fuzzy logic algorithm varies, on-line, parameters of the defining controller. Results indicate the approach is viable for on-line adaptive control of systems when the model is too complex or uncertain for application of other more classical control techniques.

Development of Fuzzy Logic Controller for Quanser Bench-Top Helicopter

NASA Astrophysics Data System (ADS)

Jafri, M. H.; Mansor, H.; Gunawan, T. S.

2017-11-01

Bench-top helicopter is a laboratory scale helicopter that usually used as a testing bench of the real helicopter behavior. This helicopter is a 3 Degree of Freedom (DOF) helicopter which works by three different axes wshich are elevation, pitch and travel. Thus, fuzzy logic controller has been proposed to be implemented into Quanser bench-top helicopter because of its ability to work with non-linear system. The objective for this project is to design and apply fuzzy logic controller for Quanser bench-top helicopter. Other than that, fuzzy logic controller performance system has been simulated to analyze and verify its behavior over existing PID controller by using Matlab & Simulink software. In this research, fuzzy logic controller has been designed to control the elevation angle. After simulation has been performed, it can be seen that simulation result shows that fuzzy logic elevation control is working for 4°, 5° and 6°. These three angles produce zero steady state error and has a fast response. Other than that, performance comparisons have been performed between fuzzy logic controller and PID controller. Fuzzy logic elevation control has a better performance compared to PID controller where lower percentage overshoot and faster settling time have been achieved in 4°, 5° and 6° step response test. Both controller are have zero steady state error but fuzzy logic controller is managed to produce a better performance in term of settling time and percentage overshoot which make the proposed controller is reliable compared to the existing PID controller.

ADCS controllers comparison for small satellitess in Low Earth Orbit

NASA Astrophysics Data System (ADS)

Calvo, Daniel; Laverón-Simavilla, Ana; Lapuerta, Victoria

2016-07-01

Fuzzy logic controllers are flexible and simple, suitable for small satellites Attitude Determination and Control Subsystems (ADCS). In a previous work, a tailored Fuzzy controller was designed for a nanosatellite. Its performance and efficiency were compared with a traditional Proportional Integrative Derivative (PID) controller within the same specific mission. The orbit height varied along the mission from injection at around 380 km down to 200 km height, and the mission required pointing accuracy over the whole time. Due to both, the requirements imposed by such a low orbit, and the limitations in the power available for the attitude control, an efficient ADCS is required. Both methodologies, fuzzy and PID, were fine-tuned using an automated procedure to grant maximum efficiency with fixed performances. The simulations showed that the Fuzzy controller is much more efficient (up to 65% less power required) in single manoeuvres, achieving similar, or even better, precision than the PID. The accuracy and efficiency improvement of the Fuzzy controller increase with orbit height because the environmental disturbances decrease, approaching the ideal scenario. However, the controllers are meant to be used in a vast range of situations and configurations which exceed those used in the calibration process carried out in the previous work. To assess the suitability and performance of both controllers in a wider framework, parametric and statistical methods have been applied using the Monte Carlo technique. Several parameters have been modified randomly at the beginning of each simulation: the moments of inertia of the whole satellite and of the momentum wheel, the residual magnetic dipole and the initial conditions of the test. These parameters have been chosen because they are the main source of uncertainty during the design phase. The variables used for the analysis are the error (critical for science) and the operation cost (which impacts the mission lifetime and outcome). The analysis of the simulations has shown that, in overall, the PID error is over twice the Fuzzy error and the PID cost is over 40% bigger than the Fuzzy cost. This suggests that a Fuzzy controller may be a better solution in a wider range of configurations than other classical solutions like the PID.

Research on pressure control of pressurizer in pressurized water reactor nuclear power plant

NASA Astrophysics Data System (ADS)

Dai, Ling; Yang, Xuhong; Liu, Gang; Ye, Jianhua; Qian, Hong; Xue, Yang

2010-07-01

Pressurizer is one of the most important components in the nuclear reactor system. Its function is to keep the pressure of the primary circuit. It can prevent shutdown of the system from the reactor accident under the normal transient state while keeping the setting value in the normal run-time. This paper is mainly research on the pressure system which is running in the Daya Bay Nuclear Power Plant. A conventional PID controller and a fuzzy controller are designed through analyzing the dynamic characteristics and calculating the transfer function. Then a fuzzy PID controller is designed by analyzing the results of two controllers. The fuzzy PID controller achieves the optimal control system finally.

WITHDRAWN: A novel robust adaptive sliding mode control using fuzzy self-tuning PID controller for 3 DOF planar robot manipulators.

PubMed

Amer, Ahmed Foad; Sallam, Elsayed Abdelhameed; Elawady, Wael Mohammed

2011-02-22

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy. Copyright © 2011. Published by Elsevier Ltd.. All rights reserved.

Development of a GA-Fuzzy-Immune PID Controller with Incomplete Derivation for Robot Dexterous Hand

PubMed Central

Liu, Xin-hua; Chen, Xiao-hu; Zheng, Xian-hua; Li, Sheng-peng; Wang, Zhong-bin

2014-01-01

In order to improve the performance of robot dexterous hand, a controller based on GA-fuzzy-immune PID was designed. The control system of a robot dexterous hand and mathematical model of an index finger were presented. Moreover, immune mechanism was applied to the controller design and an improved approach through integration of GA and fuzzy inference was proposed to realize parameters' optimization. Finally, a simulation example was provided and the designed controller was proved ideal. PMID:25097881

Fuzzy attitude control for a nanosatellite in leo orbit

NASA Astrophysics Data System (ADS)

Calvo, Daniel; Laverón-Simavilla, Ana; Lapuerta, Victoria; Aviles, Taisir

Fuzzy logic controllers are flexible and simple, suitable for small satellites Attitude Determination and Control Subsystems (ADCS). In this work, a tailored fuzzy controller is designed for a nanosatellite and is compared with a traditional Proportional Integrative Derivative (PID) controller. Both control methodologies are compared within the same specific mission. The orbit height varies along the mission from injection at around 380 km down to a 200 km height orbit, and the mission requires pointing accuracy over the whole time. Due to both the requirements imposed by such a low orbit, and the limitations in the power available for the attitude control, a robust and efficient ADCS is required. For these reasons a fuzzy logic controller is implemented as the brain of the ADCS and its performance and efficiency are compared to a traditional PID. The fuzzy controller is designed in three separated controllers, each one acting on one of the Euler angles of the satellite in an orbital frame. The fuzzy memberships are constructed taking into account the mission requirements, the physical properties of the satellite and the expected performances. Both methodologies, fuzzy and PID, are fine-tuned using an automated procedure to grant maximum efficiency with fixed performances. Finally both methods are probed in different environments to test their characteristics. The simulations show that the fuzzy controller is much more efficient (up to 65% less power required) in single maneuvers, achieving similar, or even better, precision than the PID. The accuracy and efficiency improvement of the fuzzy controller increase with orbit height because the environmental disturbances decrease, approaching the ideal scenario. A brief mission description is depicted as well as the design process of both ADCS controllers. Finally the validation process and the results obtained during the simulations are described. Those results show that the fuzzy logic methodology is valid for small satellites' missions benefiting from a well-developed artificial intelligence theory.

Performance and robustness of optimal fractional fuzzy PID controllers for pitch control of a wind turbine using chaotic optimization algorithms.

PubMed

Asgharnia, Amirhossein; Shahnazi, Reza; Jamali, Ali

2018-05-11

The most studied controller for pitch control of wind turbines is proportional-integral-derivative (PID) controller. However, due to uncertainties in wind turbine modeling and wind speed profiles, the need for more effective controllers is inevitable. On the other hand, the parameters of PID controller usually are unknown and should be selected by the designer which is neither a straightforward task nor optimal. To cope with these drawbacks, in this paper, two advanced controllers called fuzzy PID (FPID) and fractional-order fuzzy PID (FOFPID) are proposed to improve the pitch control performance. Meanwhile, to find the parameters of the controllers the chaotic evolutionary optimization methods are used. Using evolutionary optimization methods not only gives us the unknown parameters of the controllers but also guarantees the optimality based on the chosen objective function. To improve the performance of the evolutionary algorithms chaotic maps are used. All the optimization procedures are applied to the 2-mass model of 5-MW wind turbine model. The proposed optimal controllers are validated using simulator FAST developed by NREL. Simulation results demonstrate that the FOFPID controller can reach to better performance and robustness while guaranteeing fewer fatigue damages in different wind speeds in comparison to FPID, fractional-order PID (FOPID) and gain-scheduling PID (GSPID) controllers. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Research on frequency control strategy of interconnected region based on fuzzy PID

NASA Astrophysics Data System (ADS)

Zhang, Yan; Li, Chunlan

2018-05-01

In order to improve the frequency control performance of the interconnected power grid, overcome the problems of poor robustness and slow adjustment of traditional regulation, the paper puts forward a frequency control method based on fuzzy PID. The method takes the frequency deviation and tie-line deviation of each area as the control objective, takes the regional frequency deviation and its deviation as input, and uses fuzzy mathematics theory, adjusts PID control parameters online. By establishing the regional frequency control model of water-fire complementary power generation in MATLAB, the regional frequency control strategy is given, and three control modes (TBC-FTC, FTC-FTC, FFC-FTC) are simulated and analyzed. The simulation and experimental results show that, this method has better control performance compared with the traditional regional frequency regulation.

Neuro-Fuzzy Computational Technique to Control Load Frequency in Hydro-Thermal Interconnected Power System

NASA Astrophysics Data System (ADS)

Prakash, S.; Sinha, S. K.

2015-09-01

In this research work, two areas hydro-thermal power system connected through tie-lines is considered. The perturbation of frequencies at the areas and resulting tie line power flows arise due to unpredictable load variations that cause mismatch between the generated and demanded powers. Due to rising and falling power demand, the real and reactive power balance is harmed; hence frequency and voltage get deviated from nominal value. This necessitates designing of an accurate and fast controller to maintain the system parameters at nominal value. The main purpose of system generation control is to balance the system generation against the load and losses so that the desired frequency and power interchange between neighboring systems are maintained. The intelligent controllers like fuzzy logic, artificial neural network (ANN) and hybrid fuzzy neural network approaches are used for automatic generation control for the two area interconnected power systems. Area 1 consists of thermal reheat power plant whereas area 2 consists of hydro power plant with electric governor. Performance evaluation is carried out by using intelligent (ANFIS, ANN and fuzzy) control and conventional PI and PID control approaches. To enhance the performance of controller sliding surface i.e. variable structure control is included. The model of interconnected power system has been developed with all five types of said controllers and simulated using MATLAB/SIMULINK package. The performance of the intelligent controllers has been compared with the conventional PI and PID controllers for the interconnected power system. A comparison of ANFIS, ANN, Fuzzy and PI, PID based approaches shows the superiority of proposed ANFIS over ANN, fuzzy and PI, PID. Thus the hybrid fuzzy neural network controller has better dynamic response i.e., quick in operation, reduced error magnitude and minimized frequency transients.

Simulation comparison of proportional integral derivative and fuzzy logic in controlling AC-DC buck boost converter

NASA Astrophysics Data System (ADS)

Faisal, A.; Hasan, S.; Suherman

2018-03-01

AC-DC converter is widely used in the commercial industry even for daily purposes. The AC-DC converter is used to convert AC voltage into DC. In order to obtain the desired output voltage, the converter usually has a controllable regulator. This paper discusses buck boost regulator with a power MOSFET as switching component which is adjusted based on the duty cycle of pulse width modulation (PWM). The main problems of the buck boost converter at start up are the high overshoot, the long peak time and rise time. This paper compares the effectiveness of two control techniques: proportional integral derivative (PID) and fuzzy logic control in controlling the buck boost converter through simulations. The results show that the PID is more sensitive to voltage change than fuzzy logic. However, PID generates higher overshoot, long peak time and rise time. On the other hand, fuzzy logic generates no overshoot and shorter rise time.

Fuzzy attitude control of solar sail via linear matrix inequalities

NASA Astrophysics Data System (ADS)

Baculi, Joshua; Ayoubi, Mohammad A.

2017-09-01

This study presents a fuzzy tracking controller based on the Takagi-Sugeno (T-S) fuzzy model of the solar sail. First, the T-S fuzzy model is constructed by linearizing the existing nonlinear equations of motion of the solar sail. Then, the T-S fuzzy model is used to derive the state feedback controller gains for the Twin Parallel Distributed Compensation (TPDC) technique. The TPDC tracks and stabilizes the attitude of the solar sail to any desired state in the presence of parameter uncertainties and external disturbances while satisfying actuator constraints. The performance of the TPDC is compared to a PID controller that is tuned using the Ziegler-Nichols method. Numerical simulation shows the TPDC outperforms the PID controller when stabilizing the solar sail to a desired state.

A fuzzy logic sliding mode controlled electronic differential for a direct wheel drive EV

NASA Astrophysics Data System (ADS)

Ozkop, Emre; Altas, Ismail H.; Okumus, H. Ibrahim; Sharaf, Adel M.

2015-11-01

In this study, a direct wheel drive electric vehicle based on an electronic differential system with a fuzzy logic sliding mode controller (FLSMC) is studied. The conventional sliding surface is modified using a fuzzy rule base to obtain fuzzy dynamic sliding surfaces by changing its slopes using the global error and its derivative in a fuzzy logic inference system. The controller is compared with proportional-integral-derivative (PID) and sliding mode controllers (SMCs), which are usually preferred to be used in industry. The proposed controller provides robustness and flexibility to direct wheel drive electric vehicles. The fuzzy logic sliding mode controller, electronic differential system and the overall electrical vehicle mechanism are modelled and digitally simulated by using the Matlab software. Simulation results show that the system with FLSMC has better efficiency and performance compared to those of PID and SMCs.

Load Frequency Control of AC Microgrid Interconnected Thermal Power System

NASA Astrophysics Data System (ADS)

Lal, Deepak Kumar; Barisal, Ajit Kumar

2017-08-01

In this paper, a microgrid (MG) power generation system is interconnected with a single area reheat thermal power system for load frequency control study. A new meta-heuristic optimization algorithm i.e. Moth-Flame Optimization (MFO) algorithm is applied to evaluate optimal gains of the fuzzy based proportional, integral and derivative (PID) controllers. The system dynamic performance is studied by comparing the results with MFO optimized classical PI/PID controllers. Also the system performance is investigated with fuzzy PID controller optimized by recently developed grey wolf optimizer (GWO) algorithm, which has proven its superiority over other previously developed algorithm in many interconnected power systems.

Design issues for a reinforcement-based self-learning fuzzy controller

NASA Technical Reports Server (NTRS)

Yen, John; Wang, Haojin; Dauherity, Walter

1993-01-01

Fuzzy logic controllers have some often cited advantages over conventional techniques such as PID control: easy implementation, its accommodation to natural language, the ability to cover wider range of operating conditions and others. One major obstacle that hinders its broader application is the lack of a systematic way to develop and modify its rules and as result the creation and modification of fuzzy rules often depends on try-error or pure experimentation. One of the proposed approaches to address this issue is self-learning fuzzy logic controllers (SFLC) that use reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of self-learning fuzzy controller is highly contingent on the design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for the application to chemical process are discussed and its performance is compared with that of PID and self-tuning fuzzy logic controller.

Closed-loop controller for chest compressions based on coronary perfusion pressure: a computer simulation study.

PubMed

Wang, Chunfei; Zhang, Guang; Wu, Taihu; Zhan, Ningbo; Wang, Yaling

2016-03-01

High-quality cardiopulmonary resuscitation contributes to cardiac arrest survival. The traditional chest compression (CC) standard, which neglects individual differences, uses unified standards for compression depth and compression rate in practice. In this study, an effective and personalized CC method for automatic mechanical compression devices is provided. We rebuild Charles F. Babbs' human circulation model with a coronary perfusion pressure (CPP) simulation module and propose a closed-loop controller based on a fuzzy control algorithm for CCs, which adjusts the CC depth according to the CPP. Compared with a traditional proportion-integration-differentiation (PID) controller, the performance of the fuzzy controller is evaluated in computer simulation studies. The simulation results demonstrate that the fuzzy closed-loop controller results in shorter regulation time, fewer oscillations and smaller overshoot than traditional PID controllers and outperforms the traditional PID controller for CPP regulation and maintenance.

Design issues of a reinforcement-based self-learning fuzzy controller for petrochemical process control

NASA Technical Reports Server (NTRS)

Yen, John; Wang, Haojin; Daugherity, Walter C.

1992-01-01

Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.

PID self tuning control based on Mamdani fuzzy logic control for quadrotor stabilization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Priyambodo, Tri Kuntoro, E-mail: mastri@ugm.ac.id; Putra, Agfianto Eko; Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta

Quadrotor as one type of UAV have the ability to perform Vertical Take Off and Landing (VTOL). It allows the Quadrotor to be stationary hovering in the air. PID (Proportional Integral Derivative) control system is one of the control methods that are commonly used. It is usually used to optimize the Quadrotor stabilization at least based on the three Eulerian angles (roll, pitch, and yaw) as input parameters for the control system. The three constants of PID can be obtained in various methods. The simplest method is tuning manually. This method has several weaknesses. For example if the three constantsmore » are not exact, the resulting response will deviate from the desired result. By combining the methods of PID with fuzzy logic systems where human expertise is implemented into the machine language is expected to further optimize the control system.« less

Performance Analysis of Fuzzy-PID Controller for Blood Glucose Regulation in Type-1 Diabetic Patients.

PubMed

Yadav, Jyoti; Rani, Asha; Singh, Vijander

2016-12-01

This paper presents Fuzzy-PID (FPID) control scheme for a blood glucose control of type 1 diabetic subjects. A new metaheuristic Cuckoo Search Algorithm (CSA) is utilized to optimize the gains of FPID controller. CSA provides fast convergence and is capable of handling global optimization of continuous nonlinear systems. The proposed controller is an amalgamation of fuzzy logic and optimization which may provide an efficient solution for complex problems like blood glucose control. The task is to maintain normal glucose levels in the shortest possible time with minimum insulin dose. The glucose control is achieved by tuning the PID (Proportional Integral Derivative) and FPID controller with the help of Genetic Algorithm and CSA for comparative analysis. The designed controllers are tested on Bergman minimal model to control the blood glucose level in the facets of parameter uncertainties, meal disturbances and sensor noise. The results reveal that the performance of CSA-FPID controller is superior as compared to other designed controllers.

Fuzzy control of power converters based on quasilinear modelling

NASA Astrophysics Data System (ADS)

Li, C. K.; Lee, W. L.; Chou, Y. W.

1995-03-01

Unlike feedback control by the fuzzy PID method, a new fuzzy control algorithm based on quasilinear modelling of the DC-DC converter is proposed. Investigation is carried out using a buck-boost converter. Simulation results demonstrated that the converter can be regulated with improved performance even when subjected to input disturbance and load variation.

Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO.

PubMed

Pan, Indranil; Das, Saptarshi

2016-05-01

This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Trajectory following and stabilization control of fully actuated AUV using inverse kinematics and self-tuning fuzzy PID.

PubMed

Hammad, Mohanad M; Elshenawy, Ahmed K; El Singaby, M I

2017-01-01

In this work a design for self-tuning non-linear Fuzzy Proportional Integral Derivative (FPID) controller is presented to control position and speed of Multiple Input Multiple Output (MIMO) fully-actuated Autonomous Underwater Vehicles (AUV) to follow desired trajectories. Non-linearity that results from the hydrodynamics and the coupled AUV dynamics makes the design of a stable controller a very difficult task. In this study, the control scheme in a simulation environment is validated using dynamic and kinematic equations for the AUV model and hydrodynamic damping equations. An AUV configuration with eight thrusters and an inverse kinematic model from a previous work is utilized in the simulation. In the proposed controller, Mamdani fuzzy rules are used to tune the parameters of the PID. Nonlinear fuzzy Gaussian membership functions are selected to give better performance and response in the non-linear system. A control architecture with two feedback loops is designed such that the inner loop is for velocity control and outer loop is for position control. Several test scenarios are executed to validate the controller performance including different complex trajectories with and without injection of ocean current disturbances. A comparison between the proposed FPID controller and the conventional PID controller is studied and shows that the FPID controller has a faster response to the reference signal and more stable behavior in a disturbed non-linear environment.

Trajectory following and stabilization control of fully actuated AUV using inverse kinematics and self-tuning fuzzy PID

PubMed Central

Elshenawy, Ahmed K.; El Singaby, M.I.

2017-01-01

In this work a design for self-tuning non-linear Fuzzy Proportional Integral Derivative (FPID) controller is presented to control position and speed of Multiple Input Multiple Output (MIMO) fully-actuated Autonomous Underwater Vehicles (AUV) to follow desired trajectories. Non-linearity that results from the hydrodynamics and the coupled AUV dynamics makes the design of a stable controller a very difficult task. In this study, the control scheme in a simulation environment is validated using dynamic and kinematic equations for the AUV model and hydrodynamic damping equations. An AUV configuration with eight thrusters and an inverse kinematic model from a previous work is utilized in the simulation. In the proposed controller, Mamdani fuzzy rules are used to tune the parameters of the PID. Nonlinear fuzzy Gaussian membership functions are selected to give better performance and response in the non-linear system. A control architecture with two feedback loops is designed such that the inner loop is for velocity control and outer loop is for position control. Several test scenarios are executed to validate the controller performance including different complex trajectories with and without injection of ocean current disturbances. A comparison between the proposed FPID controller and the conventional PID controller is studied and shows that the FPID controller has a faster response to the reference signal and more stable behavior in a disturbed non-linear environment. PMID:28683071

Feedback control system based on a remote operated PID controller implemented using mbed NXP LPC1768 development board

NASA Astrophysics Data System (ADS)

Pricop, Emil; Zamfir, Florin; Paraschiv, Nicolae

2015-11-01

Process control is a challenging research topic for both academia and industry for a long time. Controllers evolved from the classical SISO approach to modern fuzzy or neuro-fuzzy embedded devices with networking capabilities, however PID algorithms are still used in the most industrial control loops. In this paper, we focus on the implementation of a PID controller using mbed NXP LPC1768 development board. This board integrates a powerful ARM Cortex- M3 core and has networking capabilities. The implemented controller can be remotely operated by using an Internet connection and a standard Web browser. The main advantages of the proposed embedded system are customizability, easy operation and very low power consumption. The experimental results obtained by using a simulated process are analysed and shows that the implementation can be done with success in industrial applications.

Ultra-precise tracking control of piezoelectric actuators via a fuzzy hysteresis model.

PubMed

Li, Pengzhi; Yan, Feng; Ge, Chuan; Zhang, Mingchao

2012-08-01

In this paper, a novel Takagi-Sugeno (T-S) fuzzy system based model is proposed for hysteresis in piezoelectric actuators. The antecedent and consequent structures of the fuzzy hysteresis model (FHM) can be, respectively, identified on-line through uniform partition approach and recursive least squares (RLS) algorithm. With respect to controller design, the inverse of FHM is used to develop a feedforward controller to cancel out the hysteresis effect. Then a hybrid controller is designed for high-performance tracking. It combines the feedforward controller with a proportional integral differential (PID) controller favourable for stabilization and disturbance compensation. To achieve nanometer-scale tracking precision, the enhanced adaptive hybrid controller is further developed. It uses real-time input and output data to update FHM, thus changing the feedforward controller to suit the on-site hysteresis character of the piezoelectric actuator. Finally, as to 3 cases of 50 Hz sinusoidal, multiple frequency sinusoidal and 50 Hz triangular trajectories tracking, experimental results demonstrate the efficiency of the proposed controllers. Especially, being only 0.35% of the maximum desired displacement, the maximum error of 50 Hz sinusoidal tracking is greatly reduced to 5.8 nm, which clearly shows the ultra-precise nanometer-scale tracking performance of the developed adaptive hybrid controller.

Design of an iterative auto-tuning algorithm for a fuzzy PID controller

NASA Astrophysics Data System (ADS)

Saeed, Bakhtiar I.; Mehrdadi, B.

2012-05-01

Since the first application of fuzzy logic in the field of control engineering, it has been extensively employed in controlling a wide range of applications. The human knowledge on controlling complex and non-linear processes can be incorporated into a controller in the form of linguistic terms. However, with the lack of analytical design study it is becoming more difficult to auto-tune controller parameters. Fuzzy logic controller has several parameters that can be adjusted, such as: membership functions, rule-base and scaling gains. Furthermore, it is not always easy to find the relation between the type of membership functions or rule-base and the controller performance. This study proposes a new systematic auto-tuning algorithm to fine tune fuzzy logic controller gains. A fuzzy PID controller is proposed and applied to several second order systems. The relationship between the closed-loop response and the controller parameters is analysed to devise an auto-tuning method. The results show that the proposed method is highly effective and produces zero overshoot with enhanced transient response. In addition, the robustness of the controller is investigated in the case of parameter changes and the results show a satisfactory performance.

Asymmetric Fuzzy Control of a Positive and Negative Pneumatic Pressure Servo System

NASA Astrophysics Data System (ADS)

Yang, Gang; Du, Jing-Min; Fu, Xiao-Yun; Li, Bao-Ren

2017-11-01

The pneumatic pressure control systems have been used in some fields. However, the researches on pneumatic pressure control mainly focus on constant pressure regulation. Poor dynamic characteristics and strong nonlinearity of such systems limit its application in the field of pressure tracking control. In order to meet the demand of generating dynamic pressure signal in the application of the hardware-in-the-loop simulation of aerospace engineering, a positive and negative pneumatic pressure servo system is provided to implement dynamic adjustment of sealed chamber pressure. A mathematical model is established with simulation and experiment being implemented afterwards to discuss the characteristics of the system, which shows serious asymmetry in the process of charging and discharging. Based on the analysis of the system dynamics, a fuzzy proportional integral derivative (PID) controller with asymmetric fuzzy compensator is proposed. Different from conventional adjusting mechanisms employing the error and change in error of the controlled variable as input parameters, the current chamber pressure and charging or discharging state are chosen as inputs of the compensator, which improves adaptability. To verify the effectiveness and performance of the proposed controller, the comparison experiments tracking sinusoidal and square wave commands are conducted. Experimental results show that the proposed controller can obtain better dynamic performance and relatively consistent control performance across the scope of work (2-140 kPa). The research proposes a fuzzy control method to overcome asymmetry and enhance adaptability for the positive and negative pneumatic pressure servo system.

Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization.

PubMed

Chen, Zhihuan; Yuan, Yanbin; Yuan, Xiaohui; Huang, Yuehua; Li, Xianshan; Li, Wenwu

2015-05-01

A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

A novel optimized hybrid fuzzy logic intelligent PID controller for an interconnected multi-area power system with physical constraints and boiler dynamics.

PubMed

Gomaa Haroun, A H; Li, Yin-Ya

2017-11-01

In the fast developing world nowadays, load frequency control (LFC) is considered to be a most significant role for providing the power supply with good quality in the power system. To deliver a reliable power, LFC system requires highly competent and intelligent control technique. Hence, in this article, a novel hybrid fuzzy logic intelligent proportional-integral-derivative (FLiPID) controller has been proposed for LFC of interconnected multi-area power systems. A four-area interconnected thermal power system incorporated with physical constraints and boiler dynamics is considered and the adjustable parameters of the FLiPID controller are optimized using particle swarm optimization (PSO) scheme employing an integral square error (ISE) criterion. The proposed method has been established to enhance the power system performances as well as to reduce the oscillations of uncertainties due to variations in the system parameters and load perturbations. The supremacy of the suggested method is demonstrated by comparing the simulation results with some recently reported heuristic methods such as fuzzy logic proportional-integral (FLPI) and intelligent proportional-integral-derivative (PID) controllers for the same electrical power system. the investigations showed that the FLiPID controller provides a better dynamic performance and outperform compared to the other approaches in terms of the settling time, and minimum undershoots of the frequency as well as tie-line power flow deviations following a perturbation, in addition to perform appropriate settlement of integral absolute error (IAE). Finally, the sensitivity analysis of the plant is inspected by varying the system parameters and operating load conditions from their nominal values. It is observed that the suggested controller based optimization algorithm is robust and perform satisfactorily with the variations in operating load condition, system parameters and load pattern. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Single axis control of ball position in magnetic levitation system using fuzzy logic control

NASA Astrophysics Data System (ADS)

Sahoo, Narayan; Tripathy, Ashis; Sharma, Priyaranjan

2018-03-01

This paper presents the design and real time implementation of Fuzzy logic control(FLC) for the control of the position of a ferromagnetic ball by manipulating the current flowing in an electromagnet that changes the magnetic field acting on the ball. This system is highly nonlinear and open loop unstable. Many un-measurable disturbances are also acting on the system, making the control of it highly complex but interesting for any researcher in control system domain. First the system is modelled using the fundamental laws, which gives a nonlinear equation. The nonlinear model is then linearized at an operating point. Fuzzy logic controller is designed after studying the system in closed loop under PID control action. The controller is then implemented in real time using Simulink real time environment. The controller is tuned manually to get a stable and robust performance. The set point tracking performance of FLC and PID controllers were compared and analyzed.

Virtual reality simulation of fuzzy-logic control during underwater dynamic positioning

NASA Astrophysics Data System (ADS)

Thekkedan, Midhin Das; Chin, Cheng Siong; Woo, Wai Lok

2015-03-01

In this paper, graphical-user-interface (GUI) software for simulation and fuzzy-logic control of a remotely operated vehicle (ROV) using MATLAB™ GUI Designing Environment is proposed. The proposed ROV's GUI platform allows the controller such as fuzzy-logic control systems design to be compared with other controllers such as proportional-integral-derivative (PID) and sliding-mode controller (SMC) systematically and interactively. External disturbance such as sea current can be added to improve the modelling in actual underwater environment. The simulated results showed the position responses of the fuzzy-logic control exhibit reasonable performance under the sea current disturbance.

Vector control of wind turbine on the basis of the fuzzy selective neural net*

NASA Astrophysics Data System (ADS)

Engel, E. A.; Kovalev, I. V.; Engel, N. E.

2016-04-01

An article describes vector control of wind turbine based on fuzzy selective neural net. Based on the wind turbine system’s state, the fuzzy selective neural net tracks an maximum power point under random perturbations. Numerical simulations are accomplished to clarify the applicability and advantages of the proposed vector wind turbine’s control on the basis of the fuzzy selective neuronet. The simulation results show that the proposed intelligent control of wind turbine achieves real-time control speed and competitive performance, as compared to a classical control model with PID controllers based on traditional maximum torque control strategy.

A Study on Application of Fuzzy Adaptive Unscented Kalman Filter to Nonlinear Turbojet Engine Control

NASA Astrophysics Data System (ADS)

Han, Dongju

2018-05-01

Safe and efficient flight powered by an aircraft turbojet engine relies on the performance of the engine controller preventing compressor surge with robustness from noises or disturbances. This paper proposes the effective nonlinear controller associated with the nonlinear filter for the real turbojet engine with highly nonlinear dynamics. For the feasible controller study the nonlinearity of the engine dynamics was investigated by comparing the step responses from the linearized model with the original nonlinear dynamics. The fuzzy-based PID control logic is introduced to control the engine efficiently and FAUKF is applied for robustness from noises. The simulation results prove the effectiveness of FAUKF applied to the proposed controller such that the control performances are superior over the conventional controller and the filer performance using FAUKF indicates the satisfactory results such as clearing the defects by reducing the distortions without compressor surge, whereas the conventional UKF is not fully effective as occurring some distortions with compressor surge due to a process noise.

Anti-windup adaptive PID control design for a class of uncertain chaotic systems with input saturation.

PubMed

Tahoun, A H

2017-01-01

In this paper, the stabilization problem of actuators saturation in uncertain chaotic systems is investigated via an adaptive PID control method. The PID control parameters are auto-tuned adaptively via adaptive control laws. A multi-level augmented error is designed to account for the extra terms appearing due to the use of PID and saturation. The proposed control technique uses both the state-feedback and the output-feedback methodologies. Based on Lyapunov׳s stability theory, new anti-windup adaptive controllers are proposed. Demonstrative examples with MATLAB simulations are studied. The simulation results show the efficiency of the proposed adaptive PID controllers. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A robust hybrid fuzzy-simulated annealing-intelligent water drops approach for tuning a distribution static compensator nonlinear controller in a distribution system

NASA Astrophysics Data System (ADS)

Bagheri Tolabi, Hajar; Hosseini, Rahil; Shakarami, Mahmoud Reza

2016-06-01

This article presents a novel hybrid optimization approach for a nonlinear controller of a distribution static compensator (DSTATCOM). The DSTATCOM is connected to a distribution system with the distributed generation units. The nonlinear control is based on partial feedback linearization. Two proportional-integral-derivative (PID) controllers regulate the voltage and track the output in this control system. In the conventional scheme, the trial-and-error method is used to determine the PID controller coefficients. This article uses a combination of a fuzzy system, simulated annealing (SA) and intelligent water drops (IWD) algorithms to optimize the parameters of the controllers. The obtained results reveal that the response of the optimized controlled system is effectively improved by finding a high-quality solution. The results confirm that using the tuning method based on the fuzzy-SA-IWD can significantly decrease the settling and rising times, the maximum overshoot and the steady-state error of the voltage step response of the DSTATCOM. The proposed hybrid tuning method for the partial feedback linearizing (PFL) controller achieved better regulation of the direct current voltage for the capacitor within the DSTATCOM. Furthermore, in the event of a fault the proposed controller tuned by the fuzzy-SA-IWD method showed better performance than the conventional controller or the PFL controller without optimization by the fuzzy-SA-IWD method with regard to both fault duration and clearing times.

A hybrid intelligent controller for a twin rotor MIMO system and its hardware implementation.

PubMed

Juang, Jih-Gau; Liu, Wen-Kai; Lin, Ren-Wei

2011-10-01

This paper presents a fuzzy PID control scheme with a real-valued genetic algorithm (RGA) to a setpoint control problem. The objective of this paper is to control a twin rotor MIMO system (TRMS) to move quickly and accurately to the desired attitudes, both the pitch angle and the azimuth angle in a cross-coupled condition. A fuzzy compensator is applied to the PID controller. The proposed control structure includes four PID controllers with independent inputs in 2-DOF. In order to reduce total error and control energy, all parameters of the controller are obtained by a RGA with the system performance index as a fitness function. The system performance index utilized the integral of time multiplied by the square error criterion (ITSE) to build a suitable fitness function in the RGA. A new method for RGA to solve more than 10 parameters in the control scheme is investigated. For real-time control, Xilinx Spartan II SP200 FPGA (Field Programmable Gate Array) is employed to construct a hardware-in-the-loop system through writing VHDL on this FPGA. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Greenhouse irrigation control system design based on ZigBee and fuzzy PID technology

NASA Astrophysics Data System (ADS)

Zhou, Bing; Yang, Qiliang; Liu, Kenan; Li, Peiqing; Zhang, Jing; Wang, Qijian

In order to achieve the water demand information accurately detect of the greenhouse crop and its precision irrigation automatic control, this article has designed a set of the irrigated control system based on ZigBee and fuzzy PID technology, which composed by the soil water potential sensor, CC2530F256 wireless microprocessor, IAR Embedded Workbench software development platform. And the time of Irrigation as the output .while the amount of soil water potential and crop growth cycle as the input. The article depended on Greenhouse-grown Jatropha to verify the object, the results show that the system can irrigate timely and appropriately according to the soil water potential and water demend of the different stages of Jatropha growth , which basically meet the design requirements. Therefore, the system has broad application prospects in the amount of greenhouse crop of fine control irrigation.

Modelling of the automatic stabilization system of the aircraft course by a fuzzy logic method

NASA Astrophysics Data System (ADS)

Mamonova, T.; Syryamkin, V.; Vasilyeva, T.

2016-04-01

The problem of the present paper concerns the development of a fuzzy model of the system of an aircraft course stabilization. In this work modelling of the aircraft course stabilization system with the application of fuzzy logic is specified. Thus the authors have used the data taken for an ordinary passenger plane. As a result of the study the stabilization system models were realised in the environment of Matlab package Simulink on the basis of the PID-regulator and fuzzy logic. The authors of the paper have shown that the use of the method of artificial intelligence allows reducing the time of regulation to 1, which is 50 times faster than the time when standard receptions of the management theory are used. This fact demonstrates a positive influence of the use of fuzzy regulation.

The Temperature Fuzzy Control System of Barleythe Malt Drying Based on Microcontroller

NASA Astrophysics Data System (ADS)

Gao, Xiaoyang; Bi, Yang; Zhang, Lili; Chen, Jingjing; Yun, Jianmin

The control strategy of temperature and humidity in the beer barley malt drying chamber based on fuzzy logic control was implemented.Expounded in this paper was the selection of parameters for the structure of the regulatory device, as well as the essential design from control rules based on the existing experience. A temperature fuzzy controller was thus constructed using relevantfuzzy logic, and humidity control was achieved by relay, ensured the situation of the humidity to control the temperature. The temperature's fuzzy control and the humidity real-time control were all processed by single chip microcomputer with assembly program. The experimental results showed that the temperature control performance of this fuzzy regulatory system,especially in the ways of working stability and responding speed and so on,was better than normal used PID control. The cost of real-time system was inquite competitive position. It was demonstrated that the system have a promising prospect of extensive application.

Research on precise control of 3D print nozzle temperature in PEEK material

NASA Astrophysics Data System (ADS)

Liu, Zhichao; Wang, Gong; Huo, Yu; Zhao, Wei

2017-10-01

3D printing technology has shown more and more applicability in medication, designing and other fields for its low cost and high timeliness. PEEK (poly-ether-ether-ketone), as a typical high-performance special engineering plastic, become one of the most excellent materials to be used in 3D printing technology because of its excellent mechanical property, good lubricity, chemical resistance, and other properties. But the nozzle of 3D printer for PEEK has also a series of very high requirements. In this paper, we mainly use the nozzle temperature control as the research object, combining with the advantages and disadvantages of PID control and fuzzy control. Finally realize a kind of fuzzy PID controller to solve the problem of the inertia of the temperature system and the seriousness of the temperature control hysteresis in the temperature control of the nozzle, and to meet the requirements of the accuracy of the nozzle temperature control and rapid reaction.

Modeling and Simulation of Control Actuation System with Fuzzy-PID Logic Controlled Brushless Motor Drives for Missiles Glider Applications

PubMed Central

Muniraj, Murali; Arulmozhiyal, Ramaswamy

2015-01-01

A control actuation system has been used extensively in automotive, aerospace, and defense applications. The major challenges in modeling control actuation system are rise time, maximum peak to peak overshoot, and response to nonlinear system with percentage error. This paper addresses the challenges in modeling and real time implementation of control actuation system for missiles glider applications. As an alternative fuzzy-PID controller is proposed in BLDC motor drive followed by linkage mechanism to actuate fins in missiles and gliders. The proposed system will realize better rise time and less overshoot while operating in extreme nonlinear dynamic system conditions. A mathematical model of BLDC motor is derived in state space form. The complete control actuation system is modeled in MATLAB/Simulink environment and verified by performing simulation studies. A real time prototype of the control actuation is developed with dSPACE-1104 hardware controller and a detailed analysis is carried out to confirm the viability of the proposed system. PMID:26613102

Modeling and Simulation of Control Actuation System with Fuzzy-PID Logic Controlled Brushless Motor Drives for Missiles Glider Applications.

PubMed

Muniraj, Murali; Arulmozhiyal, Ramaswamy

2015-01-01

A control actuation system has been used extensively in automotive, aerospace, and defense applications. The major challenges in modeling control actuation system are rise time, maximum peak to peak overshoot, and response to nonlinear system with percentage error. This paper addresses the challenges in modeling and real time implementation of control actuation system for missiles glider applications. As an alternative fuzzy-PID controller is proposed in BLDC motor drive followed by linkage mechanism to actuate fins in missiles and gliders. The proposed system will realize better rise time and less overshoot while operating in extreme nonlinear dynamic system conditions. A mathematical model of BLDC motor is derived in state space form. The complete control actuation system is modeled in MATLAB/Simulink environment and verified by performing simulation studies. A real time prototype of the control actuation is developed with dSPACE-1104 hardware controller and a detailed analysis is carried out to confirm the viability of the proposed system.

Adaptive PID formation control of nonholonomic robots without leader's velocity information.

PubMed

Shen, Dongbin; Sun, Weijie; Sun, Zhendong

2014-03-01

This paper proposes an adaptive proportional integral derivative (PID) algorithm to solve a formation control problem in the leader-follower framework where the leader robot's velocities are unknown for the follower robots. The main idea is first to design some proper ideal control law for the formation system to obtain a required performance, and then to propose the adaptive PID methodology to approach the ideal controller. As a result, the formation is achieved with much more enhanced robust formation performance. The stability of the closed-loop system is theoretically proved by Lyapunov method. Both numerical simulations and physical vehicle experiments are presented to verify the effectiveness of the proposed adaptive PID algorithm. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

PIDs, Types and the Semantic Web

NASA Astrophysics Data System (ADS)

Schwardmann, Ulrich

2017-04-01

PID Information Types are becoming a crucial role in scientific data management because they can provide state (what) and binding (where) information about digital objects as attributes of the PID. This is a similar but much more flexible approach than the well known mime type characterization, because both of these types concepts allow to decide about preconditions for processes in advance and before touching the data. One aspect of this is the need for standards and correctness of the used types to ensure reliability for the processes operating on the digital objects. This requires registries and schemas for PID InfoTypes and suggests an automated schema generation process. Such a process in combination with data type registries will be described in more detail in the intended talk. Another aspect of PID InfoTypes is its intrinsic grammar as subject-predicate-object triple, with the PID as subject, the type as predicate and its value (often again a PID) as object in this relation. Given the registration of types and the proposed syntactical rigidness of the value, guaranteed by the schema, together with the use of PIDs in subject and predicate, the type concept has the ability to overcome the fuzziness and lack of reliability of semantic web categories with its URL references and possibly changing locations and content. The intended talk will also describe this approach in more detail, discusses the differences to linked data and describes some necessary technological developments for the type concept to keep up with the possibilities currently provided by the semantic web.

Type-2 fuzzy logic control of a 2-DOF helicopter (TRMS system)

NASA Astrophysics Data System (ADS)

Zeghlache, Samir; Kara, Kamel; Saigaa, Djamel

2014-09-01

The helicopter dynamic includes nonlinearities, parametric uncertainties and is subject to unknown external disturbances. Such complicated dynamics involve designing sophisticated control algorithms that can deal with these difficulties. In this paper, a type 2 fuzzy logic PID controller is proposed for TRMS (twin rotor mimo system) control problem. Using triangular membership functions and based on a human operator experience, two controllers are designed to control the position of the yaw and the pitch angles of the TRMS. Simulation results are given to illustrate the effectiveness of the proposed control scheme.

Fuzzy logic controllers for electrotechnical devices - On-site tuning approach

NASA Astrophysics Data System (ADS)

Hissel, D.; Maussion, P.; Faucher, J.

2001-12-01

Fuzzy logic offers nowadays an interesting alternative to the designers of non linear control laws for electrical or electromechanical systems. However, due to the huge number of tuning parameters, this kind of control is only used in a few industrial applications. This paper proposes a new, very simple, on-site tuning strategy for a PID-like fuzzy logic controller. Thanks to the experimental designs methodology, we will propose sets of optimized pre-established settings for this kind of fuzzy controllers. The proposed parameters are only depending on one on-site open-loop identification test. In this way, this on-site tuning methodology has to be compared to the Ziegler-Nichols one's for conventional controllers. Experimental results (on a permanent magnets synchronous motor and on a DC/DC converter) will underline all the efficiency of this tuning methodology. Finally, the field of validity of the proposed pre-established settings will be given.

Nonlinear Adaptive PID Control for Greenhouse Environment Based on RBF Network

PubMed Central

Zeng, Songwei; Hu, Haigen; Xu, Lihong; Li, Guanghui

2012-01-01

This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production. PMID:22778587

Introduction to Fuzzy Set Theory

NASA Technical Reports Server (NTRS)

Kosko, Bart

1990-01-01

An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

Intelligent neural network and fuzzy logic control of industrial and power systems

NASA Astrophysics Data System (ADS)

Kuljaca, Ognjen

The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of adaptive and neural network control systems, as well as for the analysis of the different algorithms such as elastic fuzzy systems.

An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload.

PubMed

Sharma, Richa; Kumar, Vikas; Gaur, Prerna; Mittal, A P

2016-05-01

Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive Fuzzy Control for Nonstrict Feedback Systems With Unmodeled Dynamics and Fuzzy Dead Zone via Output Feedback.

PubMed

Wang, Lijie; Li, Hongyi; Zhou, Qi; Lu, Renquan

2017-09-01

This paper investigates the problem of observer-based adaptive fuzzy control for a category of nonstrict feedback systems subject to both unmodeled dynamics and fuzzy dead zone. Through constructing a fuzzy state observer and introducing a center of gravity method, unmeasurable states are estimated and the fuzzy dead zone is defuzzified, respectively. By employing fuzzy logic systems to identify the unknown functions. And combining small-gain approach with adaptive backstepping control technique, a novel adaptive fuzzy output feedback control strategy is developed, which ensures that all signals involved are semi-globally uniformly bounded. Simulation results are given to demonstrate the effectiveness of the presented method.

Mutation particle swarm optimization of the BP-PID controller for piezoelectric ceramics

NASA Astrophysics Data System (ADS)

Zheng, Huaqing; Jiang, Minlan

2016-01-01

PID control is the most common used method in industrial control because its structure is simple and it is easy to implement. PID controller has good control effect, now it has been widely used. However, PID method has a few limitations. The overshoot of the PID controller is very big. The adjustment time is long. When the parameters of controlled plant are changing over time, the parameters of controller could hardly change automatically to adjust to changing environment. Thus, it can't meet the demand of control quality in the process of controlling piezoelectric ceramic. In order to effectively control the piezoelectric ceramic and improve the control accuracy, this paper replaced the learning algorithm of the BP with the mutation particle swarm optimization algorithm(MPSO) on the process of the parameters setting of BP-PID. That designed a better self-adaptive controller which is combing the BP neural network based on mutation particle swarm optimization with the conventional PID control theory. This combination is called the MPSO-BP-PID. In the mechanism of the MPSO, the mutation operation is carried out with the fitness variance and the global best fitness value as the standard. That can overcome the precocious of the PSO and strengthen its global search ability. As a result, the MPSO-BP-PID can complete controlling the controlled plant with higher speed and accuracy. Therefore, the MPSO-BP-PID is applied to the piezoelectric ceramic. It can effectively overcome the hysteresis, nonlinearity of the piezoelectric ceramic. In the experiment, compared with BP-PID and PSO-BP-PID, it proved that MPSO is effective and the MPSO-BP-PID has stronger adaptability and robustness.

Inverse optimal self-tuning PID control design for an autonomous underwater vehicle

NASA Astrophysics Data System (ADS)

Rout, Raja; Subudhi, Bidyadhar

2017-01-01

This paper presents a new approach to path following control design for an autonomous underwater vehicle (AUV). A NARMAX model of the AUV is derived first and then its parameters are adapted online using the recursive extended least square algorithm. An adaptive Propotional-Integral-Derivative (PID) controller is developed using the derived parameters to accomplish the path following task of an AUV. The gain parameters of the PID controller are tuned using an inverse optimal control technique, which alleviates the problem of solving Hamilton-Jacobian equation and also satisfies an error cost function. Simulation studies were pursued to verify the efficacy of the proposed control algorithm. From the obtained results, it is envisaged that the proposed NARMAX model-based self-tuning adaptive PID control provides good path following performance even in the presence of uncertainty arising due to ocean current or hydrodynamic parameter.

Method and apparatus for controlling LCL converters using asymmetric voltage cancellation techniques

DOEpatents

Wu, Hunter; Sealy, Kylee Devro; Sharp, Bryan Thomas; Gilchrist, Aaron

2016-01-26

A method and apparatus for LCL resonant converter control utilizing Asymmetric Voltage Cancellation is described. The methods to determine the optimal trajectory of the control variables are discussed. Practical implementations of sensing load parameters are included. Simple PI, PID and fuzzy logic controllers are included with AVC for achieving good transient response characteristics with output current regulation.

Real-time performance assessment and adaptive control for a water chiller unit in an HVAC system

NASA Astrophysics Data System (ADS)

Bai, Jianbo; Li, Yang; Chen, Jianhao

2018-02-01

The paper proposes an adaptive control method for a water chiller unit in a HVAC system. Based on the minimum variance evaluation, the adaptive control method was used to realize better control of the water chiller unit. To verify the performance of the adaptive control method, the proposed method was compared with an a conventional PID controller, the simulation results showed that adaptive control method had superior control performance to that of the conventional PID controller.

Decomposed fuzzy systems and their application in direct adaptive fuzzy control.

PubMed

Hsueh, Yao-Chu; Su, Shun-Feng; Chen, Ming-Chang

2014-10-01

In this paper, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator for adaptive fuzzy control systems. The proposed structure is to decompose each fuzzy variable into layers of fuzzy systems, and each layer is to characterize one traditional fuzzy set. Similar to forming fuzzy rules in traditional fuzzy systems, layers from different variables form the so-called component fuzzy systems. DFS is proposed to provide more adjustable parameters to facilitate possible adaptation in fuzzy rules, but without introducing a learning burden. It is because those component fuzzy systems are independent so that it can facilitate minimum distribution learning effects among component fuzzy systems. It can be seen from our experiments that even when the rule number increases, the learning time in terms of cycles is still almost constant. It can also be found that the function approximation capability and learning efficiency of the DFS are much better than that of the traditional fuzzy systems when employed in adaptive fuzzy control systems. Besides, in order to further reduce the computational burden, a simplified DFS is proposed in this paper to satisfy possible real time constraints required in many applications. From our simulation results, it can be seen that the simplified DFS can perform fairly with a more concise decomposition structure.

A design of LED adaptive dimming lighting system based on incremental PID controller

NASA Astrophysics Data System (ADS)

He, Xiangyan; Xiao, Zexin; He, Shaojia

2010-11-01

As a new generation energy-saving lighting source, LED is applied widely in various technology and industry fields. The requirement of its adaptive lighting technology is more and more rigorous, especially in the automatic on-line detecting system. In this paper, a closed loop feedback LED adaptive dimming lighting system based on incremental PID controller is designed, which consists of MEGA16 chip as a Micro-controller Unit (MCU), the ambient light sensor BH1750 chip with Inter-Integrated Circuit (I2C), and constant-current driving circuit. A given value of light intensity required for the on-line detecting environment need to be saved to the register of MCU. The optical intensity, detected by BH1750 chip in real time, is converted to digital signal by AD converter of the BH1750 chip, and then transmitted to MEGA16 chip through I2C serial bus. Since the variation law of light intensity in the on-line detecting environment is usually not easy to be established, incremental Proportional-Integral-Differential (PID) algorithm is applied in this system. Control variable obtained by the incremental PID determines duty cycle of Pulse-Width Modulation (PWM). Consequently, LED's forward current is adjusted by PWM, and the luminous intensity of the detection environment is stabilized by self-adaptation. The coefficients of incremental PID are obtained respectively after experiments. Compared with the traditional LED dimming system, it has advantages of anti-interference, simple construction, fast response, and high stability by the use of incremental PID algorithm and BH1750 chip with I2C serial bus. Therefore, it is suitable for the adaptive on-line detecting applications.

The fuzzy algorithm in the die casting mould for the application of multi-channel temperature control

NASA Astrophysics Data System (ADS)

Sun, Jin-gen; Chen, Yi; Zhang, Jia-nan

2017-01-01

Mould manufacturing is one of the most basic elements in the production chain of China. The mould manufacturing technology has become an important symbol to measure the level of a country's manufacturing industry. The die-casting mould multichannel intelligent temperature control method is studied by cooling water circulation, which uses fuzzy control to realize, aiming at solving the shortcomings of slow speed and big energy consumption during the cooling process of current die-casting mould. At present, the traditional PID control method is used to control the temperature, but it is difficult to ensure the control precision. While , the fuzzy algorithm is used to realize precise control of mould temperature in cooling process. The design is simple, fast response, strong anti-interference ability and good robustness. Simulation results show that the control method is completely feasible, which has higher control precision.

Adaptive variable structure hierarchical fuzzy control for a class of high-order nonlinear dynamic systems.

PubMed

Mansouri, Mohammad; Teshnehlab, Mohammad; Aliyari Shoorehdeli, Mahdi

2015-05-01

In this paper, a novel adaptive hierarchical fuzzy control system based on the variable structure control is developed for a class of SISO canonical nonlinear systems in the presence of bounded disturbances. It is assumed that nonlinear functions of the systems be completely unknown. Switching surfaces are incorporated into the hierarchical fuzzy control scheme to ensure the system stability. A fuzzy soft switching system decides the operation area of the hierarchical fuzzy control and variable structure control systems. All the nonlinearly appeared parameters of conclusion parts of fuzzy blocks located in different layers of the hierarchical fuzzy control system are adjusted through adaptation laws deduced from the defined Lyapunov function. The proposed hierarchical fuzzy control system reduces the number of rules and consequently the number of tunable parameters with respect to the ordinary fuzzy control system. Global boundedness of the overall adaptive system and the desired precision are achieved using the proposed adaptive control system. In this study, an adaptive hierarchical fuzzy system is used for two objectives; it can be as a function approximator or a control system based on an intelligent-classic approach. Three theorems are proven to investigate the stability of the nonlinear dynamic systems. The important point about the proposed theorems is that they can be applied not only to hierarchical fuzzy controllers with different structures of hierarchical fuzzy controller, but also to ordinary fuzzy controllers. Therefore, the proposed algorithm is more general. To show the effectiveness of the proposed method four systems (two mechanical, one mathematical and one chaotic) are considered in simulations. Simulation results demonstrate the validity, efficiency and feasibility of the proposed approach to control of nonlinear dynamic systems. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

A new composite adaptive controller featuring the neural network and prescribed sliding surface with application to vibration control

NASA Astrophysics Data System (ADS)

Phu, Do Xuan; Huy, Ta Duc; Mien, Van; Choi, Seung-Bok

2018-07-01

This work proposes a novel composite adaptive controller based on the prescribed performance of the sliding surface and applies it to vibration control of a semi-active vehicle seat suspension system subjected to severe external disturbances. As a first step, the online fast interval type 2 fuzzy neural network system is adopted to establish a model and two sliding surfaces are used; conventional surface and prescribed surface. Then, an equivalent control is determined by assuming the derivative of the prescribed surface is zero, followed by the design of a controller which can guarantee both stability and robustness. Then, two controllers are combined and integrated with adaptation laws using the projection algorithm. The effectiveness of the proposed composite controller is validated through both simulation and experiment by undertaking vibration control of a semi-active seat suspension system equipped with a magneto-rheological (MR) damper. It is shown from both simulation and experimental realization that excellent vibration control performances are achieved with a small tracking error between the proposed and prescribed objectives. In addition, the control superiority of the proposed controller to conventional sliding mode controller featuring one sliding surface and proportional-integral-derivative (PID) controllers are demonstrated through a comparative work.

Station Keeping of Small Outboard-Powered Boats

NASA Technical Reports Server (NTRS)

Fisher, A. D.; VanZwieten, J. H., Jr.; VanZwieten, T. S.

2010-01-01

Three station keeping controllers have been developed which work to minimize displacement of a small outboard-powered vessel from a desired location. Each of these three controllers has a common initial layer that uses fixed-gain feedback control to calculate the desired heading of the vessel. A second control layer uses a common fixed-gain feedback controller to calculate the net forward thrust, one of two algorithms for controlling engine angle (Fixed-Gain Proportional-integral-derivative (PID) or PID with Adaptively Augmented Gains), and one of two algorithms for differential throttle control (Fixed-Gain PID and PID with Adaptive Differential Throttle gains), which work together to eliminate heading error. The three selected controllers are evaluated using a numerical simulation of a 33-foot center console vessel with twin outboards that is subject to wave, wind, and current disturbances. Each controller is tested for its ability to maintain position in the presence of three sets of environmental disturbances. These algorithms were tested with current velocity of 1.5 m/s, significant wave height of 0.5 m, and wind speeds of 2, 5, and 10 m/s. These values were chosen to model conditions a small vessel may experience in the Gulf Stream off of Fort Lauderdale. The Fixed-gain PID controller progressively got worse as wind speeds increased, while the controllers using adaptive methodologies showed consistent performance over all weather conditions and decreased heading error by as much as 20%. Thus, enhanced robustness to environmental changes has been gained by using an adaptive algorithm.

Design and Experimental Evaluation of a Robust Position Controller for an Electrohydrostatic Actuator Using Adaptive Antiwindup Sliding Mode Scheme

PubMed Central

Lee, Ji Min; Park, Sung Hwan; Kim, Jong Shik

2013-01-01

A robust control scheme is proposed for the position control of the electrohydrostatic actuator (EHA) when considering hardware saturation, load disturbance, and lumped system uncertainties and nonlinearities. To reduce overshoot due to a saturation of electric motor and to realize robustness against load disturbance and lumped system uncertainties such as varying parameters and modeling error, this paper proposes an adaptive antiwindup PID sliding mode scheme as a robust position controller for the EHA system. An optimal PID controller and an optimal anti-windup PID controller are also designed to compare control performance. An EHA prototype is developed, carrying out system modeling and parameter identification in designing the position controller. The simply identified linear model serves as the basis for the design of the position controllers, while the robustness of the control systems is compared by experiments. The adaptive anti-windup PID sliding mode controller has been found to have the desired performance and become robust against hardware saturation, load disturbance, and lumped system uncertainties and nonlinearities. PMID:23983640

Fuzzy control of magnetic bearings

NASA Technical Reports Server (NTRS)

Feeley, J. J.; Niederauer, G. M.; Ahlstrom, D. J.

1991-01-01

The use of an adaptive fuzzy control algorithm implemented on a VLSI chip for the control of a magnetic bearing was considered. The architecture of the adaptive fuzzy controller is similar to that of a neural network. The performance of the fuzzy controller is compared to that of a conventional controller by computer simulation.

HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.

PubMed

Kim, J; Kasabov, N

1999-11-01

This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.

Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks.

PubMed

Savran, Aydogan; Tasaltin, Ramazan; Becerikli, Yasar

2006-04-01

This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control system. Performance of the control system is successfully tested by performing several six-degrees-of-freedom nonlinear simulations.

Adaptive Filter Design Using Type-2 Fuzzy Cerebellar Model Articulation Controller.

PubMed

Lin, Chih-Min; Yang, Ming-Shu; Chao, Fei; Hu, Xiao-Min; Zhang, Jun

2016-10-01

This paper aims to propose an efficient network and applies it as an adaptive filter for the signal processing problems. An adaptive filter is proposed using a novel interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC). The T2FCMAC realizes an interval type-2 fuzzy logic system based on the structure of the CMAC. Due to the better ability of handling uncertainties, type-2 fuzzy sets can solve some complicated problems with outstanding effectiveness than type-1 fuzzy sets. In addition, the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so that the convergence of the filtering error can be guaranteed. In order to demonstrate the performance of the proposed adaptive T2FCMAC filter, it is tested in signal processing applications, including a nonlinear channel equalization system, a time-varying channel equalization system, and an adaptive noise cancellation system. The advantages of the proposed filter over the other adaptive filters are verified through simulations.

An Adaptive Fuzzy-Logic Traffic Control System in Conditions of Saturated Transport Stream

PubMed Central

Marakhimov, A. R.; Igamberdiev, H. Z.; Umarov, Sh. X.

2016-01-01

This paper considers the problem of building adaptive fuzzy-logic traffic control systems (AFLTCS) to deal with information fuzziness and uncertainty in case of heavy traffic streams. Methods of formal description of traffic control on the crossroads based on fuzzy sets and fuzzy logic are proposed. This paper also provides efficient algorithms for implementing AFLTCS and develops the appropriate simulation models to test the efficiency of suggested approach. PMID:27517081

Fuzzy logic based robotic controller

NASA Technical Reports Server (NTRS)

Attia, F.; Upadhyaya, M.

1994-01-01

Existing Proportional-Integral-Derivative (PID) robotic controllers rely on an inverse kinematic model to convert user-specified cartesian trajectory coordinates to joint variables. These joints experience friction, stiction, and gear backlash effects. Due to lack of proper linearization of these effects, modern control theory based on state space methods cannot provide adequate control for robotic systems. In the presence of loads, the dynamic behavior of robotic systems is complex and nonlinear, especially where mathematical modeling is evaluated for real-time operators. Fuzzy Logic Control is a fast emerging alternative to conventional control systems in situations where it may not be feasible to formulate an analytical model of the complex system. Fuzzy logic techniques track a user-defined trajectory without having the host computer to explicitly solve the nonlinear inverse kinematic equations. The goal is to provide a rule-based approach, which is closer to human reasoning. The approach used expresses end-point error, location of manipulator joints, and proximity to obstacles as fuzzy variables. The resulting decisions are based upon linguistic and non-numerical information. This paper presents a solution to the conventional robot controller which is independent of computationally intensive kinematic equations. Computer simulation results of this approach as obtained from software implementation are also discussed.

Switched Fuzzy-PD Control of Contact Forces in Robotic Microbiomanipulation.

PubMed

Zhang, Weize; Dong, Xianke; Liu, Xinyu

2017-05-01

Force sensing and control are of paramount importance in robotic micromanipulation. A contact force regulator capable of accurately applying mechanical stimuli to a live Drosophila larva could greatly facilitate mechanobiology research on Drosophila and may eventually lead to novel discoveries in mechanotransduction mechanisms of neuronal circuitries. In this paper, we present a novel contact force control scheme implemented in an automated Drosophila larvae micromanipulation system, featuring a switched fuzzy to proportional-differential (PD) controller and a noise-insensitive extended high gain observer (EHGO). The switched fuzzy-PD control law inherits the fast convergence of fuzzy control and overcomes its drawbacks such as large overshoot and steady-state oscillation. The noise-insensitive EHGO can reliably estimate system modeling errors and is robust to force measurement noises, which is advantageous over conventional high gain observers (sensitive to signal noises). Force control experiments show that, compared to a proportional-integral-differential (PID) controller, this new force control scheme significantly enhances the system dynamic performance in terms of rising time, overshoot, and oscillation. The developed robotic system and the force control scheme will be applied to mechanical stimulation and fluorescence imaging of Drosophila larvae for identifying new mechanotransduction mechanisms.

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation.

PubMed

Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

2016-12-16

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

Adaptive Fuzzy Output-Constrained Fault-Tolerant Control of Nonlinear Stochastic Large-Scale Systems With Actuator Faults.

PubMed

Li, Yongming; Ma, Zhiyao; Tong, Shaocheng

2017-09-01

The problem of adaptive fuzzy output-constrained tracking fault-tolerant control (FTC) is investigated for the large-scale stochastic nonlinear systems of pure-feedback form. The nonlinear systems considered in this paper possess the unstructured uncertainties, unknown interconnected terms and unknown nonaffine nonlinear faults. The fuzzy logic systems are employed to identify the unknown lumped nonlinear functions so that the problems of structured uncertainties can be solved. An adaptive fuzzy state observer is designed to solve the nonmeasurable state problem. By combining the barrier Lyapunov function theory, adaptive decentralized and stochastic control principles, a novel fuzzy adaptive output-constrained FTC approach is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.

Adaptive Fuzzy Control Design for Stochastic Nonlinear Switched Systems With Arbitrary Switchings and Unmodeled Dynamics.

PubMed

Li, Yongming; Sui, Shuai; Tong, Shaocheng

2017-02-01

This paper deals with the problem of adaptive fuzzy output feedback control for a class of stochastic nonlinear switched systems. The controlled system in this paper possesses unmeasured states, completely unknown nonlinear system functions, unmodeled dynamics, and arbitrary switchings. A state observer which does not depend on the switching signal is constructed to tackle the unmeasured states. Fuzzy logic systems are employed to identify the completely unknown nonlinear system functions. Based on the common Lyapunov stability theory and stochastic small-gain theorem, a new robust adaptive fuzzy backstepping stabilization control strategy is developed. The stability of the closed-loop system on input-state-practically stable in probability is proved. The simulation results are given to verify the efficiency of the proposed fuzzy adaptive control scheme.

Adaptive Control of Small Outboard-Powered Boats for Survey Applications

NASA Technical Reports Server (NTRS)

VanZwieten, T.S.; VanZwieten, J.H.; Fisher, A.D.

2009-01-01

Four autopilot controllers have been developed in this work that can both hold a desired heading and follow a straight line. These PID, adaptive PID, neuro-adaptive, and adaptive augmenting control algorithms have all been implemented into a numerical simulation of a 33-foot center console vessel with wind, waves, and current disturbances acting in the perpendicular (across-track) direction of the boat s desired trajectory. Each controller is tested for its ability to follow a desired heading in the presence of these disturbances and then to follow a straight line at two different throttle settings for the same disturbances. These controllers were tuned for an input thrust of 2000 N and all four controllers showed good performance with none of the controllers significantly outperforming the others when holding a constant heading and following a straight line at this engine thrust. Each controller was then tested for a reduced engine thrust of 1200 N per engine where each of the three adaptive controllers reduced heading error and across-track error by approximately 50% after a 300 second tuning period when compared to the fixed gain PID, showing that significant robustness to changes in throttle setting was gained by using an adaptive algorithm.

Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

PubMed

Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

2017-09-01

This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

Using a Fuzzy Light Sensor to Improve the Efficiency of Solar Panels

NASA Astrophysics Data System (ADS)

Suryono; Suseno, Jatmiko Endro; Sulistiati, Ainie Khuriati Riza; Prahara, Tahan

2018-02-01

Solar panel efficiency can be increased by improving the quality of photovoltaic material, the effectiveness of electronic circuit, and the light source tracking model. This research is aimed at improving the quality of solar panels by tracking light source using a fuzzy logic sensor. A fuzzy light sensor property is obtained from two LDR (light dependent resistor) light sensors installed in parallel to each other and is given a light separator in between them. Both sensors are mounted on a solar panel. Sensor output is acquired using a 12 bit ADC from an ATSAM3XE microcontroller and is then sent to a computer using WIFI radio. A PID (Proportional-Integral-Derivative) control algorithm is used to manage the position of the solar panel in line with the input given by the fuzzy light sensor. This control mechanism works based on the margin of fuzzy membership from both sensors that is used to move a motor DC that in turn moves the solar panel. Experimental results show a characteristically symmetrical fuzzy membership of both sensors with a reflected correlation of R=0.9981 after gains from both sensors are arranged with a program. Upon being tested in the field, this system was capable of improving the performance of solar panels in gaining power compared to their original fixed position. The discrepancy was evident when the angle of incoming sunlight approached both 0° and 180°. Further calculations of data acquired by the fuzzy light sensor show increased solar panel power efficiency by up to 5.6%.

Fuzzy Adaptive Decentralized Optimal Control for Strict Feedback Nonlinear Large-Scale Systems.

PubMed

Sun, Kangkang; Sui, Shuai; Tong, Shaocheng

2018-04-01

This paper considers the optimal decentralized fuzzy adaptive control design problem for a class of interconnected large-scale nonlinear systems in strict feedback form and with unknown nonlinear functions. The fuzzy logic systems are introduced to learn the unknown dynamics and cost functions, respectively, and a state estimator is developed. By applying the state estimator and the backstepping recursive design algorithm, a decentralized feedforward controller is established. By using the backstepping decentralized feedforward control scheme, the considered interconnected large-scale nonlinear system in strict feedback form is changed into an equivalent affine large-scale nonlinear system. Subsequently, an optimal decentralized fuzzy adaptive control scheme is constructed. The whole optimal decentralized fuzzy adaptive controller is composed of a decentralized feedforward control and an optimal decentralized control. It is proved that the developed optimal decentralized controller can ensure that all the variables of the control system are uniformly ultimately bounded, and the cost functions are the smallest. Two simulation examples are provided to illustrate the validity of the developed optimal decentralized fuzzy adaptive control scheme.

A dynamic fuzzy genetic algorithm for natural image segmentation using adaptive mean shift

NASA Astrophysics Data System (ADS)

Arfan Jaffar, M.

2017-01-01

In this paper, a colour image segmentation approach based on hybridisation of adaptive mean shift (AMS), fuzzy c-mean and genetic algorithms (GAs) is presented. Image segmentation is the perceptual faction of pixels based on some likeness measure. GA with fuzzy behaviour is adapted to maximise the fuzzy separation and minimise the global compactness among the clusters or segments in spatial fuzzy c-mean (sFCM). It adds diversity to the search process to find the global optima. A simple fusion method has been used to combine the clusters to overcome the problem of over segmentation. The results show that our technique outperforms state-of-the-art methods.

Adaptive Fuzzy Bounded Control for Consensus of Multiple Strict-Feedback Nonlinear Systems.

PubMed

Wang, Wei; Tong, Shaocheng

2018-02-01

This paper studies the adaptive fuzzy bounded control problem for leader-follower multiagent systems, where each follower is modeled by the uncertain nonlinear strict-feedback system. Combining the fuzzy approximation with the dynamic surface control, an adaptive fuzzy control scheme is developed to guarantee the output consensus of all agents under directed communication topologies. Different from the existing results, the bounds of the control inputs are known as a priori, and they can be determined by the feedback control gains. To realize smooth and fast learning, a predictor is introduced to estimate each error surface, and the corresponding predictor error is employed to learn the optimal fuzzy parameter vector. It is proved that the developed adaptive fuzzy control scheme guarantees the uniformly ultimate boundedness of the closed-loop systems, and the tracking error converges to a small neighborhood of the origin. The simulation results and comparisons are provided to show the validity of the control strategy presented in this paper.

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

PubMed Central

Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

2016-01-01

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency. PMID:27999261

Adaptive fuzzy system for 3-D vision

NASA Technical Reports Server (NTRS)

Mitra, Sunanda

1993-01-01

An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.

Identification of two-phase flow regime based on electrical capacitance tomography and soft-sensing technique

NASA Astrophysics Data System (ADS)

Zhao, Ming-fu; Hu, Xin-Yu; Shao, Yun; Luo, Bin-bin; Wang, Xin

2008-10-01

This article analyses nowadays in common use of football robots in China, intended to improve the football robots' hardware platform system's capability, and designed a football robot which based on DSP core controller, and combined Fuzzy-PID control algorithm. The experiment showed, because of the advantages of DSP, such as quickly operation, various of interfaces, low power dissipation etc. It has great improvement on the football robot's performance of movement, controlling precision, real-time performance.

Coordinating IMC-PID and adaptive SMC controllers for a PEMFC.

PubMed

Wang, Guo-Liang; Wang, Yong; Shi, Jun-Hai; Shao, Hui-He

2010-01-01

For a Proton Exchange Membrane Fuel Cell (PEMFC) power plant with a methanol reformer, the process parameters and power output are considered simultaneously to avoid violation of the constraints and to keep the fuel cell power plant safe and effective. In this paper, a novel coordinating scheme is proposed by combining an Internal Model Control (IMC) based PID Control and adaptive Sliding Mode Control (SMC). The IMC-PID controller is designed for the reformer of the fuel flow rate according to the expected first-order dynamic properties. The adaptive SMC controller of the fuel cell current has been designed using the constant plus proportional rate reaching law. The parameters of the SMC controller are adaptively tuned according to the response of the fuel flow rate control system. When the power output controller feeds back the current references to these two controllers, the coordinating controllers system works in a system-wide way. The simulation results of the PEMFC power plant demonstrate the effectiveness of the proposed method. 2009 ISA. Published by Elsevier Ltd. All rights reserved.

A neural fuzzy controller learning by fuzzy error propagation

NASA Technical Reports Server (NTRS)

Nauck, Detlef; Kruse, Rudolf

1992-01-01

In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

Observed-Based Adaptive Fuzzy Tracking Control for Switched Nonlinear Systems With Dead-Zone.

PubMed

Tong, Shaocheng; Sui, Shuai; Li, Yongming

2015-12-01

In this paper, the problem of adaptive fuzzy output-feedback control is investigated for a class of uncertain switched nonlinear systems in strict-feedback form. The considered switched systems contain unknown nonlinearities, dead-zone, and immeasurable states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, a switched fuzzy state observer is designed and thus the immeasurable states are obtained by it. By applying the adaptive backstepping design principle and the average dwell time method, an adaptive fuzzy output-feedback tracking control approach is developed. It is proved that the proposed control approach can guarantee that all the variables in the closed-loop system are bounded under a class of switching signals with average dwell time, and also that the system output can track a given reference signal as closely as possible. The simulation results are given to check the effectiveness of the proposed approach.

Model Free iPID Control for Glycemia Regulation of Type-1 Diabetes.

PubMed

MohammadRidha, Taghreed; Ait-Ahmed, Mourad; Chaillous, Lucy; Krempf, Michel; Guilhem, Isabelle; Poirier, Jean-Yves; Moog, Claude H

2018-01-01

The objective is to design a fully automated glycemia controller of Type-1 Diabetes (T1D) in both fasting and postprandial phases on a large number of virtual patients. A model-free intelligent proportional-integral-derivative (iPID) is used to infuse insulin. The feasibility of iPID is tested in silico on two simulators with and without measurement noise. The first simulator is derived from a long-term linear time-invariant model. The controller is also validated on the UVa/Padova metabolic simulator on 10 adults under 25 runs/subject for noise robustness test. It was shown that without measurement noise, iPID mimicked the normal pancreatic secretion with a relatively fast reaction to meals as compared to a standard PID. With the UVa/Padova simulator, the robustness against CGM noise was tested. A higher percentage of time in target was obtained with iPID as compared to standard PID with reduced time spent in hyperglycemia. Two different T1D simulators tests showed that iPID detects meals and reacts faster to meal perturbations as compared to a classic PID. The intelligent part turns the controller to be more aggressive immediately after meals without neglecting safety. Further research is suggested to improve the computation of the intelligent part of iPID for such systems under actuator constraints. Any improvement can impact the overall performance of the model-free controller. The simple structure iPID is a step for PID-like controllers since it combines the classic PID nice properties with new adaptive features.

Slip control design of electric vehicle using indirect Dahlin Adaptive Pid

NASA Astrophysics Data System (ADS)

Fauzi, I. R.; Koko, F.; Kirom, M. R.

2016-11-01

In this paper the problem to be solved is to build a slip control on a wheel that may occur in an electric car wheel. Slip is the difference in vehicle velocity and wheel tangential velocity and to be enlarged when the torque given growing. Slip can be reduced by controlling the torque of the wheel so that the wheel tangential speed does not exceed the vehicle speed. The experiment in this paper is a simulation using MATLAB Simulink and using Adaptive control. The response adaptive PID control more quickly 1.5 s than PID control and can controlled wheel tangential speed close to the vehicle velocity on a dry asphalt, wet asphalt, snow and ice surface sequent at time 2s, 4s, 10s, and 50s. The maximum acceleration of the vehicle (V) on the surface of the dry asphalt, wet asphalt, snow, and ice surface sequent at 8.9 m/s2, 6.2 m/s2, 2.75 m/s2, and 0.34 m/s2.

Robust adaptive fuzzy tracking control for pure-feedback stochastic nonlinear systems with input constraints.

PubMed

Wang, Huanqing; Chen, Bing; Liu, Xiaoping; Liu, Kefu; Lin, Chong

2013-12-01

This paper is concerned with the problem of adaptive fuzzy tracking control for a class of pure-feedback stochastic nonlinear systems with input saturation. To overcome the design difficulty from nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is first introduced to approximate the saturation function; then, an adaptive fuzzy tracking controller based on the mean-value theorem is constructed by using backstepping technique. The proposed adaptive fuzzy controller guarantees that all signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighborhood of the desired reference signal in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the proposed control scheme.

Adaptive fuzzy sliding control of single-phase PV grid-connected inverter.

PubMed

Fei, Juntao; Zhu, Yunkai

2017-01-01

In this paper, an adaptive fuzzy sliding mode controller is proposed to control a two-stage single-phase photovoltaic (PV) grid-connected inverter. Two key technologies are discussed in the presented PV system. An incremental conductance method with adaptive step is adopted to track the maximum power point (MPP) by controlling the duty cycle of the controllable power switch of the boost DC-DC converter. An adaptive fuzzy sliding mode controller with an integral sliding surface is developed for the grid-connected inverter where a fuzzy system is used to approach the upper bound of the system nonlinearities. The proposed strategy has strong robustness for the sliding mode control can be designed independently and disturbances can be adaptively compensated. Simulation results of a PV grid-connected system verify the effectiveness of the proposed method, demonstrating the satisfactory robustness and performance.

Fault tolerant control of multivariable processes using auto-tuning PID controller.

PubMed

Yu, Ding-Li; Chang, T K; Yu, Ding-Wen

2005-02-01

Fault tolerant control of dynamic processes is investigated in this paper using an auto-tuning PID controller. A fault tolerant control scheme is proposed composing an auto-tuning PID controller based on an adaptive neural network model. The model is trained online using the extended Kalman filter (EKF) algorithm to learn system post-fault dynamics. Based on this model, the PID controller adjusts its parameters to compensate the effects of the faults, so that the control performance is recovered from degradation. The auto-tuning algorithm for the PID controller is derived with the Lyapunov method and therefore, the model predicted tracking error is guaranteed to converge asymptotically. The method is applied to a simulated two-input two-output continuous stirred tank reactor (CSTR) with various faults, which demonstrate the applicability of the developed scheme to industrial processes.

Elucidating PID Degradation Mechanisms and In Situ Dark I–V Monitoring for Modeling Degradation Rate in CdTe Thin-Film Modules

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hacke, Peter; Spataru, Sergiu; Johnston, Steve

A progression of potential-induced degradation (PID) mechanisms are observed in CdTe modules, including shunting/junction degradation and two different manifestations of series resistance depending on the stress level and water ingress. The dark I-V method for in-situ characterization of Pmax based on superposition was adapted for the thin-film modules undergoing PID in view of the degradation mechanisms observed. An exponential model based on module temperature and relative humidity was fit to the PID rate for multiple stress levels in chamber tests and validated by predicting the observed degradation of the module type in the field.

Abrasive slurry jet cutting model based on fuzzy relations

NASA Astrophysics Data System (ADS)

Qiang, C. H.; Guo, C. W.

2017-12-01

The cutting process of pre-mixed abrasive slurry or suspension jet (ASJ) is a complex process affected by many factors, and there is a highly nonlinear relationship between the cutting parameters and cutting quality. In this paper, guided by fuzzy theory, the fuzzy cutting model of ASJ was developed. In the modeling of surface roughness, the upper surface roughness prediction model and the lower surface roughness prediction model were established respectively. The adaptive fuzzy inference system combines the learning mechanism of neural networks and the linguistic reasoning ability of the fuzzy system, membership functions, and fuzzy rules are obtained by adaptive adjustment. Therefore, the modeling process is fast and effective. In this paper, the ANFIS module of MATLAB fuzzy logic toolbox was used to establish the fuzzy cutting model of ASJ, which is found to be quite instrumental to ASJ cutting applications.

Usefulness of Neuro-Fuzzy Models' Application for Tobacco Control

NASA Astrophysics Data System (ADS)

Petrovic-Lazarevic, Sonja; Zhang, Jian Ying

2007-12-01

The paper presents neuro-fuzzy models' application appropriate for tobacco control: the fuzzy control model, Adaptive Network Based Fuzzy Inference System, Evolving Fuzzy Neural Network models, and EVOlving POLicies. We propose further the use of Fuzzy Casual Networks to help tobacco control decision makers develop policies and measure their impact on social regulation.

The DSM-5 Trait Measure in a Psychiatric Sample of Late Adolescents and Emerging Adults: Structure, Reliability, and Validity.

PubMed

De Caluwé, Elien; Verbeke, Lize; van Aken, Marcel; van der Heijden, Paul T; De Clercq, Barbara

2018-02-22

The inclusion of a dimensional trait model of personality pathology in DSM-5 creates new opportunities for research on developmental antecedents of personality pathology. The traits of this model can be measured with the Personality Inventory for DSM-5 (PID-5), initially developed for adults, but also demonstrating validity in adolescents. The present study adds to the growing body of literature on the psychometrics of the PID-5, by examining its structure, validity, and reliability in 187 psychiatric-referred late adolescents and emerging adults. PID-5, Big Five Inventory, and Kidscreen self-reports were provided, and 88 non-clinical matched controls completed the PID-5. Results confirm the PID-5's five-factor structure, indicate adequate psychometric properties, and underscore the construct and criterion validity, showing meaningful associations with adaptive traits and quality of life. Results are discussed in terms of the PID-5's applicability in vulnerable populations who are going through important developmental transition phases, such as the step towards early adulthood.

Desired Accuracy Estimation of Noise Function from ECG Signal by Fuzzy Approach

PubMed Central

Vahabi, Zahra; Kermani, Saeed

2012-01-01

Unknown noise and artifacts present in medical signals with non-linear fuzzy filter will be estimated and then removed. An adaptive neuro-fuzzy interference system which has a non-linear structure presented for the noise function prediction by before Samples. This paper is about a neuro-fuzzy method to estimate unknown noise of Electrocardiogram signal. Adaptive neural combined with Fuzzy System to construct a fuzzy Predictor. For this system setting parameters such as the number of Membership Functions for each input and output, training epochs, type of MFs for each input and output, learning algorithm and etc. is determined by learning data. At the end simulated experimental results are presented for proper validation. PMID:23717810

Comment on “Based on interval type-2 adaptive fuzzy H∞ tracking controller for SISO time-delay nonlinear systems”

NASA Astrophysics Data System (ADS)

Pan, Yongping; Huang, Daoping

2011-03-01

In this comment, we point out the inappropriateness of Theorem 1 in the article [Tsung-Chih Lin, Mehdi Roopaei. Based on interval type-2 adaptive fuzzy H∞ tracking controller for SISO time-delay nonlinear systems. Commun Nonlinear Sci Numer Simulat 2010;15:4065-75]. For solving this problem, some formular mistakes are corrected and novel parameter adaptive laws of interval type-2 fuzzy neural network system are given.

Adaptive neuro-fuzzy and expert systems for power quality analysis and prediction of abnormal operation

NASA Astrophysics Data System (ADS)

Ibrahim, Wael Refaat Anis

The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an alarm is instigated predicting the advent of system abnormal operation. The incoming data could be compared to previous trends as well as matched to trends developed through computer simulations and stored using fuzzy learning.

Multistage Fuzzy Decision Making in Bilateral Negotiation with Finite Termination Times

NASA Astrophysics Data System (ADS)

Richter, Jan; Kowalczyk, Ryszard; Klusch, Matthias

In this paper we model the negotiation process as a multistage fuzzy decision problem where the agents preferences are represented by a fuzzy goal and fuzzy constraints. The opponent is represented by a fuzzy Markov decision process in the form of offer-response patterns which enables utilization of limited and uncertain information, e.g. the characteristics of the concession behaviour. We show that we can obtain adaptive negotiation strategies by only using the negotiation threads of two past cases to create and update the fuzzy transition matrix. The experimental evaluation demonstrates that our approach is adaptive towards different negotiation behaviours and that the fuzzy representation of the preferences and the transition matrix allows for application in many scenarios where the available information, preferences and constraints are soft or imprecise.

Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot

PubMed Central

Amador-Angulo, Leticia; Mendoza, Olivia; Castro, Juan R.; Rodríguez-Díaz, Antonio; Melin, Patricia; Castillo, Oscar

2016-01-01

A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot. We add two types of perturbations in the model for the Generalized Type-2 Fuzzy Logic System to better analyze its behavior under uncertainty and this shows better results when compared to the original BCO. We implemented various performance indices; ITAE, IAE, ISE, ITSE, RMSE and MSE to measure the performance of the controller. The experimental results show better performances using GT2FLS then by IT2FLS and T1FLS in the dynamic adaptation the parameters for the BCO algorithm. PMID:27618062

Adaptive fuzzy sliding control of single-phase PV grid-connected inverter

PubMed Central

Zhu, Yunkai

2017-01-01

In this paper, an adaptive fuzzy sliding mode controller is proposed to control a two-stage single-phase photovoltaic (PV) grid-connected inverter. Two key technologies are discussed in the presented PV system. An incremental conductance method with adaptive step is adopted to track the maximum power point (MPP) by controlling the duty cycle of the controllable power switch of the boost DC-DC converter. An adaptive fuzzy sliding mode controller with an integral sliding surface is developed for the grid-connected inverter where a fuzzy system is used to approach the upper bound of the system nonlinearities. The proposed strategy has strong robustness for the sliding mode control can be designed independently and disturbances can be adaptively compensated. Simulation results of a PV grid-connected system verify the effectiveness of the proposed method, demonstrating the satisfactory robustness and performance. PMID:28797060

ASICs Approach for the Implementation of a Symmetric Triangular Fuzzy Coprocessor and Its Application to Adaptive Filtering

NASA Technical Reports Server (NTRS)

Starks, Scott; Abdel-Hafeez, Saleh; Usevitch, Bryan

1997-01-01

This paper discusses the implementation of a fuzzy logic system using an ASICs design approach. The approach is based upon combining the inherent advantages of symmetric triangular membership functions and fuzzy singleton sets to obtain a novel structure for fuzzy logic system application development. The resulting structure utilizes a fuzzy static RAM to store the rule-base and the end-points of the triangular membership functions. This provides advantages over other approaches in which all sampled values of membership functions for all universes must be stored. The fuzzy coprocessor structure implements the fuzzification and defuzzification processes through a two-stage parallel pipeline architecture which is capable of executing complex fuzzy computations in less than 0.55us with an accuracy of more than 95%, thus making it suitable for a wide range of applications. Using the approach presented in this paper, a fuzzy logic rule-base can be directly downloaded via a host processor to an onchip rule-base memory with a size of 64 words. The fuzzy coprocessor's design supports up to 49 rules for seven fuzzy membership functions associated with each of the chip's two input variables. This feature allows designers to create fuzzy logic systems without the need for additional on-board memory. Finally, the paper reports on simulation studies that were conducted for several adaptive filter applications using the least mean squared adaptive algorithm for adjusting the knowledge rule-base.

Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV.

PubMed

Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad

2016-05-09

In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.

Development of Anti-lock Braking System (ABS) for Vehicles Braking

NASA Astrophysics Data System (ADS)

Minh, Vu Trieu; Oamen, Godwin; Vassiljeva, Kristina; Teder, Leo

2016-11-01

This paper develops a real laboratory of anti-lock braking system (ABS) for vehicle and conducts real experiments to verify the ability of this ABS to prevent the vehicle wheel from being locked while braking. Two controllers of PID and fuzzy logic are tested for analysis and comparison. This ABS laboratory is designed for bachelor and master students to simulate and analyze performances of ABS with different control techniques on various roads and load conditions. This paper provides educational theories and practices on the design of control for system dynamics.

A novel auto-tuning PID control mechanism for nonlinear systems.

PubMed

Cetin, Meric; Iplikci, Serdar

2015-09-01

In this paper, a novel Runge-Kutta (RK) discretization-based model-predictive auto-tuning proportional-integral-derivative controller (RK-PID) is introduced for the control of continuous-time nonlinear systems. The parameters of the PID controller are tuned using RK model of the system through prediction error-square minimization where the predicted information of tracking error provides an enhanced tuning of the parameters. Based on the model-predictive control (MPC) approach, the proposed mechanism provides necessary PID parameter adaptations while generating additive correction terms to assist the initially inadequate PID controller. Efficiency of the proposed mechanism has been tested on two experimental real-time systems: an unstable single-input single-output (SISO) nonlinear magnetic-levitation system and a nonlinear multi-input multi-output (MIMO) liquid-level system. RK-PID has been compared to standard PID, standard nonlinear MPC (NMPC), RK-MPC and conventional sliding-mode control (SMC) methods in terms of control performance, robustness, computational complexity and design issue. The proposed mechanism exhibits acceptable tuning and control performance with very small steady-state tracking errors, and provides very short settling time for parameter convergence. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Study on application of adaptive fuzzy control and neural network in the automatic leveling system

NASA Astrophysics Data System (ADS)

Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

2015-04-01

This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System

PubMed Central

Hosseini, Monireh Sheikh; Zekri, Maryam

2012-01-01

Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054

Adding dynamic rules to self-organizing fuzzy systems

NASA Technical Reports Server (NTRS)

Buhusi, Catalin V.

1992-01-01

This paper develops a Dynamic Self-Organizing Fuzzy System (DSOFS) capable of adding, removing, and/or adapting the fuzzy rules and the fuzzy reference sets. The DSOFS background consists of a self-organizing neural structure with neuron relocation features which will develop a map of the input-output behavior. The relocation algorithm extends the topological ordering concept. Fuzzy rules (neurons) are dynamically added or released while the neural structure learns the pattern. The DSOFS advantages are the automatic synthesis and the possibility of parallel implementation. A high adaptation speed and a reduced number of neurons is needed in order to keep errors under some limits. The computer simulation results are presented in a nonlinear systems modelling application.

Flatness-based embedded adaptive fuzzy control of turbocharged diesel engines

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos; Siano, Pierluigi; Arsie, Ivan

2014-10-01

In this paper nonlinear embedded control for turbocharged Diesel engines is developed with the use of Differential flatness theory and adaptive fuzzy control. It is shown that the dynamic model of the turbocharged Diesel engine is differentially flat and admits dynamic feedback linearization. It is also shown that the dynamic model can be written in the linear Brunovsky canonical form for which a state feedback controller can be easily designed. To compensate for modeling errors and external disturbances an adaptive fuzzy control scheme is implemanted making use of the transformed dynamical system of the diesel engine that is obtained through the application of differential flatness theory. Since only the system's output is measurable the complete state vector has to be reconstructed with the use of a state observer. It is shown that a suitable learning law can be defined for neuro-fuzzy approximators, which are part of the controller, so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed observer-based adaptive fuzzy control scheme results in H∞ tracking performance.

Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

PubMed Central

Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

2015-01-01

In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896

Adaptive Fuzzy Tracking Control for a Class of MIMO Nonlinear Systems in Nonstrict-Feedback Form.

PubMed

Chen, Bing; Lin, Chong; Liu, Xiaoping; Liu, Kefu

2015-12-01

This paper focuses on the problem of fuzzy adaptive control for a class of multiinput and multioutput (MIMO) nonlinear systems in nonstrict-feedback form, which contains the strict-feedback form as a special case. By the condition of variable partition, a new fuzzy adaptive backstepping is proposed for such a class of nonlinear MIMO systems. The suggested fuzzy adaptive controller guarantees that the proposed control scheme can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking errors eventually converge to a small neighborhood around the origin. The main advantage of this paper is that a control approach is systematically derived for nonlinear systems with strong interconnected terms which are the functions of all states of the whole system. Simulation results further illustrate the effectiveness of the suggested approach.

Adaptive fuzzy predictive sliding control of uncertain nonlinear systems with bound-known input delay.

PubMed

Khazaee, Mostafa; Markazi, Amir H D; Omidi, Ehsan

2015-11-01

In this paper, a new Adaptive Fuzzy Predictive Sliding Mode Control (AFP-SMC) is presented for nonlinear systems with uncertain dynamics and unknown input delay. The control unit consists of a fuzzy inference system to approximate the ideal linearization control, together with a switching strategy to compensate for the estimation errors. Also, an adaptive fuzzy predictor is used to estimate the future values of the system states to compensate for the time delay. The adaptation laws are used to tune the controller and predictor parameters, which guarantee the stability based on a Lyapunov-Krasovskii functional. To evaluate the method effectiveness, the simulation and experiment on an overhead crane system are presented. According to the obtained results, AFP-SMC can effectively control the uncertain nonlinear systems, subject to input delays of known bound. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive Fuzzy Output Feedback Control for Switched Nonlinear Systems With Unmodeled Dynamics.

PubMed

Tong, Shaocheng; Li, Yongming

2017-02-01

This paper investigates a robust adaptive fuzzy control stabilization problem for a class of uncertain nonlinear systems with arbitrary switching signals that use an observer-based output feedback scheme. The considered switched nonlinear systems possess the unstructured uncertainties, unmodeled dynamics, and without requiring the states being available for measurement. A state observer which is independent of switching signals is designed to solve the problem of unmeasured states. Fuzzy logic systems are used to identify unknown lumped nonlinear functions so that the problem of unstructured uncertainties can be solved. By combining adaptive backstepping design principle and small-gain approach, a novel robust adaptive fuzzy output feedback stabilization control approach is developed. The stability of the closed-loop system is proved via the common Lyapunov function theory and small-gain theorem. Finally, the simulation results are given to demonstrate the validity and performance of the proposed control strategy.

A Hybrid Neuro-Fuzzy Model For Integrating Large Earth-Science Datasets

NASA Astrophysics Data System (ADS)

Porwal, A.; Carranza, J.; Hale, M.

2004-12-01

A GIS-based hybrid neuro-fuzzy approach to integration of large earth-science datasets for mineral prospectivity mapping is described. It implements a Takagi-Sugeno type fuzzy inference system in the framework of a four-layered feed-forward adaptive neural network. Each unique combination of the datasets is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent datasets. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location) is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a prospectivity map. The procedure is demonstrated by an application to regional-scale base metal prospectivity mapping in a study area located in the Aravalli metallogenic province (western India). A comparison of the hybrid neuro-fuzzy approach with pure knowledge-driven fuzzy and pure data-driven neural network approaches indicates that the former offers a superior method for integrating large earth-science datasets for predictive spatial mathematical modelling.

Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV

PubMed Central

Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad

2016-01-01

In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability. PMID:27171084

Design of fuzzy system by NNs and realization of adaptability

NASA Technical Reports Server (NTRS)

Takagi, Hideyuki

1993-01-01

The issue of designing and tuning fuzzy membership functions by neural networks (NN's) was started by NN-driven Fuzzy Reasoning in 1988. NN-driven fuzzy reasoning involves a NN embedded in the fuzzy system which generates membership values. In conventional fuzzy system design, the membership functions are hand-crafted by trial and error for each input variable. In contrast, NN-driven fuzzy reasoning considers several variables simultaneously and can design a multidimensional, nonlinear membership function for the entire subspace.

Learning Rate Updating Methods Applied to Adaptive Fuzzy Equalizers for Broadband Power Line Communications

NASA Astrophysics Data System (ADS)

Ribeiro, Moisés V.

2004-12-01

This paper introduces adaptive fuzzy equalizers with variable step size for broadband power line (PL) communications. Based on delta-bar-delta and local Lipschitz estimation updating rules, feedforward, and decision feedback approaches, we propose singleton and nonsingleton fuzzy equalizers with variable step size to cope with the intersymbol interference (ISI) effects of PL channels and the hardness of the impulse noises generated by appliances and nonlinear loads connected to low-voltage power grids. The computed results show that the convergence rates of the proposed equalizers are higher than the ones attained by the traditional adaptive fuzzy equalizers introduced by J. M. Mendel and his students. Additionally, some interesting BER curves reveal that the proposed techniques are efficient for mitigating the above-mentioned impairments.

Adaptive Fuzzy Output Constrained Control Design for Multi-Input Multioutput Stochastic Nonstrict-Feedback Nonlinear Systems.

PubMed

Li, Yongming; Tong, Shaocheng

2017-12-01

In this paper, an adaptive fuzzy output constrained control design approach is addressed for multi-input multioutput uncertain stochastic nonlinear systems in nonstrict-feedback form. The nonlinear systems addressed in this paper possess unstructured uncertainties, unknown gain functions and unknown stochastic disturbances. Fuzzy logic systems are utilized to tackle the problem of unknown nonlinear uncertainties. The barrier Lyapunov function technique is employed to solve the output constrained problem. In the framework of backstepping design, an adaptive fuzzy control design scheme is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.

Nonlinear adaptive control based on fuzzy sliding mode technique and fuzzy-based compensator.

PubMed

Nguyen, Sy Dzung; Vo, Hoang Duy; Seo, Tae-Il

2017-09-01

It is difficult to efficiently control nonlinear systems in the presence of uncertainty and disturbance (UAD). One of the main reasons derives from the negative impact of the unknown features of UAD as well as the response delay of the control system on the accuracy rate in the real time of the control signal. In order to deal with this, we propose a new controller named CO-FSMC for a class of nonlinear control systems subjected to UAD, which is constituted of a fuzzy sliding mode controller (FSMC) and a fuzzy-based compensator (CO). Firstly, the FSMC and CO are designed independently, and then an adaptive fuzzy structure is discovered to combine them. Solutions for avoiding the singular cases of the fuzzy-based function approximation and reducing the calculating cost are proposed. Based on the solutions, fuzzy sliding mode technique, lumped disturbance observer and Lyapunov stability analysis, a closed-loop adaptive control law is formulated. Simulations along with a real application based on a semi-active train-car suspension are performed to fully evaluate the method. The obtained results reflected that vibration of the chassis mass is insensitive to UAD. Compared with the other fuzzy sliding mode control strategies, the CO-FSMC can provide the best control ability to reduce unwanted vibrations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Fuzzy Behavior-Based Navigation for Planetary

NASA Technical Reports Server (NTRS)

Tunstel, Edward; Danny, Harrison; Lippincott, Tanya; Jamshidi, Mo

1997-01-01

Adaptive behavioral capabilities are necessary for robust rover navigation in unstructured and partially-mapped environments. A control approach is described which exploits the approximate reasoning capability of fuzzy logic to produce adaptive motion behavior. In particular, a behavior-based architecture for hierarchical fuzzy control of microrovers is presented. Its structure is described, as well as mechanisms of control decision-making which give rise to adaptive behavior. Control decisions for local navigation result from a consensus of recommendations offered only by behaviors that are applicable to current situations. Simulation predicts the navigation performance on a microrover in simplified Mars-analog terrain.

Robust control for a biaxial servo with time delay system based on adaptive tuning technique.

PubMed

Chen, Tien-Chi; Yu, Chih-Hsien

2009-07-01

A robust control method for synchronizing a biaxial servo system motion is proposed in this paper. A new network based cross-coupled control and adaptive tuning techniques are used together to cancel out the skew error. The conventional fixed gain PID cross-coupled controller (CCC) is replaced with the adaptive cross-coupled controller (ACCC) in the proposed control scheme to maintain biaxial servo system synchronization motion. Adaptive-tuning PID (APID) position and velocity controllers provide the necessary control actions to maintain synchronization while following a variable command trajectory. A delay-time compensator (DTC) with an adaptive controller was augmented to set the time delay element, effectively moving it outside the closed loop, enhancing the stability of the robust controlled system. This scheme provides strong robustness with respect to uncertain dynamics and disturbances. The simulation and experimental results reveal that the proposed control structure adapts to a wide range of operating conditions and provides promising results under parameter variations and load changes.

Fuzzy Adaptive Control for Intelligent Autonomous Space Exploration Problems

NASA Technical Reports Server (NTRS)

Esogbue, Augustine O.

1998-01-01

The principal objective of the research reported here is the re-design, analysis and optimization of our newly developed neural network fuzzy adaptive controller model for complex processes capable of learning fuzzy control rules using process data and improving its control through on-line adaption. The learned improvement is according to a performance objective function that provides evaluative feedback; this performance objective is broadly defined to meet long-range goals over time. Although fuzzy control had proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are either inefficient, impractical or cannot be derived, the state of the art prior to our work showed that procedures for deriving fuzzy control, however, were mostly ad hoc heuristics. The learning ability of neural networks was exploited to systematically derive fuzzy control and permit on-line adaption and in the process optimize control. The operation of neural networks integrates very naturally with fuzzy logic. The neural networks which were designed and tested using simulation software and simulated data, followed by realistic industrial data were reconfigured for application on several platforms as well as for the employment of improved algorithms. The statistical procedures of the learning process were investigated and evaluated with standard statistical procedures (such as ANOVA, graphical analysis of residuals, etc.). The computational advantage of dynamic programming-like methods of optimal control was used to permit on-line fuzzy adaptive control. Tests for the consistency, completeness and interaction of the control rules were applied. Comparisons to other methods and controllers were made so as to identify the major advantages of the resulting controller model. Several specific modifications and extensions were made to the original controller. Additional modifications and explorations have been proposed for further study. Some of these are in progress in our laboratory while others await additional support. All of these enhancements will improve the attractiveness of the controller as an effective tool for the on line control of an array of complex process environments.

Adaptive time-variant models for fuzzy-time-series forecasting.

PubMed

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

The Design of the Automatic Control System of the Gripping-Belt Speed in Long-Rootstalk Traditional Chinese Herbal Harvester

NASA Astrophysics Data System (ADS)

Huang, Jinxia; Wang, Junfa; Yu, Yonghong

This article aims to design a kind of gripping-belt speed automatic tracking system of traditional Chinese herbal harvester by AT89C52 single-chip micro computer as a core combined with fuzzy PID control algorithm. The system can adjust the gripping-belt speed in accordance with the variation of the machine's operation, so there is a perfect matching between the machine operation speed and the gripping-belt speed. The harvesting performance of the machine can be improved greatly. System design includes hardware and software.

Real-Time Stability and Control Derivative Extraction From F-15 Flight Data

NASA Technical Reports Server (NTRS)

Smith, Mark S.; Moes, Timothy R.; Morelli, Eugene A.

2003-01-01

A real-time, frequency-domain, equation-error parameter identification (PID) technique was used to estimate stability and control derivatives from flight data. This technique is being studied to support adaptive control system concepts currently being developed by NASA (National Aeronautics and Space Administration), academia, and industry. This report describes the basic real-time algorithm used for this study and implementation issues for onboard usage as part of an indirect-adaptive control system. A confidence measures system for automated evaluation of PID results is discussed. Results calculated using flight data from a modified F-15 aircraft are presented. Test maneuvers included pilot input doublets and automated inputs at several flight conditions. Estimated derivatives are compared to aerodynamic model predictions. Data indicate that the real-time PID used for this study performs well enough to be used for onboard parameter estimation. For suitable test inputs, the parameter estimates converged rapidly to sufficient levels of accuracy. The devised confidence measures used were moderately successful.

Robust decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale MIMO nonlinear systems and its application to AHS.

PubMed

Huang, Yi-Shao; Liu, Wel-Ping; Wu, Min; Wang, Zheng-Wu

2014-09-01

This paper presents a novel observer-based decentralized hybrid adaptive fuzzy control scheme for a class of large-scale continuous-time multiple-input multiple-output (MIMO) uncertain nonlinear systems whose state variables are unmeasurable. The scheme integrates fuzzy logic systems, state observers, and strictly positive real conditions to deal with three issues in the control of a large-scale MIMO uncertain nonlinear system: algorithm design, controller singularity, and transient response. Then, the design of the hybrid adaptive fuzzy controller is extended to address a general large-scale uncertain nonlinear system. It is shown that the resultant closed-loop large-scale system keeps asymptotically stable and the tracking error converges to zero. The better characteristics of our scheme are demonstrated by simulations. Copyright © 2014. Published by Elsevier Ltd.

Fuzzy adaptive interacting multiple model nonlinear filter for integrated navigation sensor fusion.

PubMed

Tseng, Chien-Hao; Chang, Chih-Wen; Jwo, Dah-Jing

2011-01-01

In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF.

Adaptive fuzzy wavelet network control of second order multi-agent systems with unknown nonlinear dynamics.

PubMed

Taheri, Mehdi; Sheikholeslam, Farid; Najafi, Majddedin; Zekri, Maryam

2017-07-01

In this paper, consensus problem is considered for second order multi-agent systems with unknown nonlinear dynamics under undirected graphs. A novel distributed control strategy is suggested for leaderless systems based on adaptive fuzzy wavelet networks. Adaptive fuzzy wavelet networks are employed to compensate for the effect of unknown nonlinear dynamics. Moreover, the proposed method is developed for leader following systems and leader following systems with state time delays. Lyapunov functions are applied to prove uniformly ultimately bounded stability of closed loop systems and to obtain adaptive laws. Three simulation examples are presented to illustrate the effectiveness of the proposed control algorithms. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Design of a laser scanner for a digital mammography system.

PubMed

Rowlands, J A; Taylor, J E

1996-05-01

We have developed a digital readout system for radiographic images using a scanning laser beam. In this system, electrostatic charge images on amorphous selenium (alpha-Se) plates are read out using photo-induced discharge (PID). We discuss the design requirements of a laser scanner for the PID system and describe its construction from commercially available components. The principles demonstrated can be adapted to a variety of digital imaging systems.

Development of a constant surface pressure penetration langmuir balance based on axisymmetric drop shape analysis.

PubMed

Wege, H A; Holgado-Terriza, J A; Cabrerizo-Vílchez, M A

2002-05-15

A new constant pressure pendant-drop penetration surface balance has been developed combining a pendant-drop surface balance, a rapid-subphase-exchange technique, and a fuzzy logic control algorithm. Beside the determination of insoluble monolayer compression-expansion isotherms, it allows performance of noninvasive kinetic studies of the adsorption of surfactants added to the new subphase onto the free surface and of the adsorption/penetration/reaction of the former onto/into/with surface layers, respectively. The interfacial pressure pi is a fundamental parameter in these studies: by working at constant pi one controls the height of the energy barrier to adsorption/penetration and can select different regimes and steps of the adsorption/penetration process. In our device a solution drop is formed at the tip of a coaxial double capillary, connected to a double microinjector. Drop profiles are extracted from digital drop micrographs and fitted to the equation of capillarity, yielding pi, the drop volume V, and the interfacial area A. pi is varied changing V (and hence A) with the microinjector. Control is based on a case-adaptable modulated fuzzy-logic PID algorithm able to maintain constant pi (or A) under a wide range of experimental conditions. The drop subphase liquid can be exchanged quantitatively by the coaxial capillaries. The adsorption/penetration/reaction kinetics at constant pi are then studied monitoring A(t), i.e., determining the relative area change necessary at each instant to compensate the pressure variation due to the interaction of the surfactant in the subsurface with the surface layer. A fully Windows-integrated program manages the whole setup. Examples of experimental protein adsorption and monolayer penetration kinetics are presented.

Improvement of the Performance of an Electrocoagulation Process System Using Fuzzy Control of pH.

PubMed

Demirci, Yavuz; Pekel, Lutfiye Canan; Altinten, Ayla; Alpbaz, Mustafa

2015-12-01

The removal efficiencies of electrocoagulation (EC) systems are highly dependent on the initial value of pH. If an EC system has an acidic influent, the pH of the effluent increases during the treatment process; conversely, if such a system has an alkaline influent, the pH of the effluent decreases during the treatment process. Thus, changes in the pH of the wastewater affect the efficiency of the EC process. In this study, we investigated the dynamic effects of pH. To evaluate approaches for preventing increases in the pH of the system, the MATLAB/Simulink program was used to develop and evaluate an on-line computer-based system for pH control. The aim of this work was to study Proportional-Integral-Derivative (PID) control and fuzzy control of the pH of a real textile wastewater purification process using EC. The performances and dynamic behaviors of these two control systems were evaluated based on determinations of COD, colour, and turbidity removal efficiencies.

Modeling and simulation of permanent magnet synchronous motor based on neural network control strategy

NASA Astrophysics Data System (ADS)

Luo, Bingyang; Chi, Shangjie; Fang, Man; Li, Mengchao

2017-03-01

Permanent magnet synchronous motor is used widely in industry, the performance requirements wouldn't be met by adopting traditional PID control in some of the occasions with high requirements. In this paper, a hybrid control strategy - nonlinear neural network PID and traditional PID parallel control are adopted. The high stability and reliability of traditional PID was combined with the strong adaptive ability and robustness of neural network. The permanent magnet synchronous motor will get better control performance when switch different working modes according to different controlled object conditions. As the results showed, the speed response adopting the composite control strategy in this paper was faster than the single control strategy. And in the case of sudden disturbance, the recovery time adopting the composite control strategy designed in this paper was shorter, the recovery ability and the robustness were stronger.

An Efficient Fuzzy Controller Design for Parallel Connected Induction Motor Drives

NASA Astrophysics Data System (ADS)

Usha, S.; Subramani, C.

2018-04-01

Generally, an induction motors are highly non-linear and has a complex time varying dynamics. This makes the speed control of an induction motor a challenging issue in the industries. But, due to the recent trends in the power electronic devices and intelligent controllers, the speed control of the induction motor is achieved by including non-linear characteristics also. Conventionally a single inverter is used to run one induction motor in industries. In the traction applications, two or more inductions motors are operated in parallel to reduce the size and cost of induction motors. In this application, the parallel connected induction motors can be driven by a single inverter unit. The stability problems may introduce in the parallel operation under low speed operating conditions. Hence, the speed deviations should be reduce with help of suitable controllers. The speed control of the parallel connected system is performed by PID controller and fuzzy logic controller. In this paper the speed response of the induction motor for the rating of IHP, 1440 rpm, and 50Hz with these controller are compared in time domain specifications. The stability analysis of the system also performed under low speed using matlab platform. The hardware model is developed for speed control using fuzzy logic controller which exhibited superior performances over the other controller.

Adaptive fuzzy leader clustering of complex data sets in pattern recognition

NASA Technical Reports Server (NTRS)

Newton, Scott C.; Pemmaraju, Surya; Mitra, Sunanda

1992-01-01

A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.

Adaptive backstepping sliding mode control with fuzzy monitoring strategy for a kind of mechanical system.

PubMed

Song, Zhankui; Sun, Kaibiao

2014-01-01

A novel adaptive backstepping sliding mode control (ABSMC) law with fuzzy monitoring strategy is proposed for the tracking-control of a kind of nonlinear mechanical system. The proposed ABSMC scheme combining the sliding mode control and backstepping technique ensure that the occurrence of the sliding motion in finite-time and the trajectory of tracking-error converge to equilibrium point. To obtain a better perturbation rejection property, an adaptive control law is employed to compensate the lumped perturbation. Furthermore, we introduce fuzzy monitoring strategy to improve adaptive capacity and soften the control signal. The convergence and stability of the proposed control scheme are proved by using Lyaponov's method. Finally, numerical simulations demonstrate the effectiveness of the proposed control scheme. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Fuzzy Logic as a Tool for Assessing Students' Knowledge and Skills

ERIC Educational Resources Information Center

Voskoglou, Michael Gr.

2013-01-01

Fuzzy logic, which is based on fuzzy sets theory introduced by Zadeh in 1965, provides a rich and meaningful addition to standard logic. The applications which may be generated from or adapted to fuzzy logic are wide-ranging and provide the opportunity for modeling under conditions which are imprecisely defined. In this article we develop a fuzzy…

Based on interval type-2 fuzzy-neural network direct adaptive sliding mode control for SISO nonlinear systems

NASA Astrophysics Data System (ADS)

Lin, Tsung-Chih

2010-12-01

In this paper, a novel direct adaptive interval type-2 fuzzy-neural tracking control equipped with sliding mode and Lyapunov synthesis approach is proposed to handle the training data corrupted by noise or rule uncertainties for nonlinear SISO nonlinear systems involving external disturbances. By employing adaptive fuzzy-neural control theory, the update laws will be derived for approximating the uncertain nonlinear dynamical system. In the meantime, the sliding mode control method and the Lyapunov stability criterion are incorporated into the adaptive fuzzy-neural control scheme such that the derived controller is robust with respect to unmodeled dynamics, external disturbance and approximation errors. In comparison with conventional methods, the advocated approach not only guarantees closed-loop stability but also the output tracking error of the overall system will converge to zero asymptotically without prior knowledge on the upper bound of the lumped uncertainty. Furthermore, chattering effect of the control input will be substantially reduced by the proposed technique. To illustrate the performance of the proposed method, finally simulation example will be given.

Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks.

PubMed

Lin, Chuan-Kai

2005-04-01

A new adaptive critic autopilot design for bank-to-turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by online tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot.

[Researches in immunological responses after burn injury in China].

PubMed

Peng, Dai-zhi

2008-10-01

For five decades it has been recognized that severe burn injury may precipitate in marked alterations in immune function, resulting in life-threatening systemic infections, sepsis, multiple organ failure, and even death. Extensive and deep burns exert widespread and profound impacts on various cells and molecules of the immune system. The general characteristics of abnormal immune responses following major burns are hyperinflammatory response and hypoimmune response of innate and adaptive immunity. These are recognized as postburn immune dysfunction (PID). The stress reaction, massive necrotic tissue, shock, infection, malnutrition and various therapeutic procedures after burns alter the microenvironment of the immune cells and molecules in which they reside, and consequently result in the changes in immune cells and their secretions in quantity and/or activity, and also aberrant signal transduction in different immune cells. These events constitute the cellular and molecular bases in the pathogenesis of PID. The main clinical consequences of PID include tissue damages and increased susceptibility to opportunistic pathogens caused by refractory inflammation and suppressed adaptive immunity. In order to decrease the morbidity of these lethal complications, efforts to improve the immune dysfunction after burn injury have been made not only at the integral level of etiological factors, but also at the cellular and molecular levels of its mechanisms. In this review, all these above-mentioned aspects of PID are comprehensively discussed.

Evolving fuzzy rules in a learning classifier system

NASA Technical Reports Server (NTRS)

Valenzuela-Rendon, Manuel

1993-01-01

The fuzzy classifier system (FCS) combines the ideas of fuzzy logic controllers (FLC's) and learning classifier systems (LCS's). It brings together the expressive powers of fuzzy logic as it has been applied in fuzzy controllers to express relations between continuous variables, and the ability of LCS's to evolve co-adapted sets of rules. The goal of the FCS is to develop a rule-based system capable of learning in a reinforcement regime, and that can potentially be used for process control.

Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2

NASA Technical Reports Server (NTRS)

Lea, Robert N. (Editor); Villarreal, James A. (Editor)

1991-01-01

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making.

Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

NASA Astrophysics Data System (ADS)

Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

2012-06-01

SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

Implementation of Adaptive Digital Controllers on Programmable Logic Devices

NASA Technical Reports Server (NTRS)

Gwaltney, David A.; King, Kenneth D.; Smith, Keary J.; Monenegro, Justino (Technical Monitor)

2002-01-01

Much has been made of the capabilities of FPGA's (Field Programmable Gate Arrays) in the hardware implementation of fast digital signal processing. Such capability also makes an FPGA a suitable platform for the digital implementation of closed loop controllers. Other researchers have implemented a variety of closed-loop digital controllers on FPGA's. Some of these controllers include the widely used proportional-integral-derivative (PID) controller, state space controllers, neural network and fuzzy logic based controllers. There are myriad advantages to utilizing an FPGA for discrete-time control functions which include the capability for reconfiguration when SRAM-based FPGA's are employed, fast parallel implementation of multiple control loops and implementations that can meet space level radiation tolerance requirements in a compact form-factor. Generally, a software implementation on a DSP (Digital Signal Processor) or microcontroller is used to implement digital controllers. At Marshall Space Flight Center, the Control Electronics Group has been studying adaptive discrete-time control of motor driven actuator systems using digital signal processor (DSP) devices. While small form factor, commercial DSP devices are now available with event capture, data conversion, pulse width modulated (PWM) outputs and communication peripherals, these devices are not currently available in designs and packages which meet space level radiation requirements. In general, very few DSP devices are produced that are designed to meet any level of radiation tolerance or hardness. The goal of this effort is to create a fully digital, flight ready controller design that utilizes an FPGA for implementation of signal conditioning for control feedback signals, generation of commands to the controlled system, and hardware insertion of adaptive control algorithm approaches. An alternative is required for compact implementation of such functionality to withstand the harsh environment encountered on spacecraft. Radiation tolerant FPGA's are a feasible option for reaching this goal.

Implementation of Adaptive Digital Controllers on Programmable Logic Devices

NASA Technical Reports Server (NTRS)

Gwaltney, David A.; King, Kenneth D.; Smith, Keary J.; Montenegro, Justino (Technical Monitor)

2002-01-01

Much has been made of the capabilities of Field Programmable Gate Arrays (FPGA's) in the hardware implementation of fast digital signal processing functions. Such capability also makes an FPGA a suitable platform for the digital implementation of closed loop controllers. Other researchers have implemented a variety of closed-loop digital controllers on FPGA's. Some of these controllers include the widely used Proportional-Integral-Derivative (PID) controller, state space controllers, neural network and fuzzy logic based controllers. There are myriad advantages to utilizing an FPGA for discrete-time control functions which include the capability for reconfiguration when SRAM- based FPGA's are employed, fast parallel implementation of multiple control loops and implementations that can meet space level radiation tolerance requirements in a compact form-factor. Generally, a software implementation on a Digital Signal Processor (DSP) device or microcontroller is used to implement digital controllers. At Marshall Space Flight Center, the Control Electronics Group has been studying adaptive discrete-time control of motor driven actuator systems using DSP devices. While small form factor, commercial DSP devices are now available with event capture, data conversion, Pulse Width Modulated (PWM) outputs and communication peripherals, these devices are not currently available in designs and packages which meet space level radiation requirements. In general, very few DSP devices are produced that are designed to meet any level of radiation tolerance or hardness. An alternative is required for compact implementation of such functionality to withstand the harsh environment encountered on spacemap. The goal of this effort is to create a fully digital, flight ready controller design that utilizes an FPGA for implementation of signal conditioning for control feedback signals, generation of commands to the controlled system, and hardware insertion of adaptive-control algorithm approaches. Radiation tolerant FPGA's are a feasible option for reaching this goal.

Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

NASA Technical Reports Server (NTRS)

Mitra, Sunanda; Pemmaraju, Surya

1992-01-01

Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

PI and fuzzy logic controllers for shunt Active Power Filter--a report.

PubMed

P, Karuppanan; Mahapatra, Kamala Kanta

2012-01-01

This paper presents a shunt Active Power Filter (APF) for power quality improvements in terms of harmonics and reactive power compensation in the distribution network. The compensation process is based only on source current extraction that reduces the number of sensors as well as its complexity. A Proportional Integral (PI) or Fuzzy Logic Controller (FLC) is used to extract the required reference current from the distorted line-current, and this controls the DC-side capacitor voltage of the inverter. The shunt APF is implemented with PWM-current controlled Voltage Source Inverter (VSI) and the switching patterns are generated through a novel Adaptive-Fuzzy Hysteresis Current Controller (A-F-HCC). The proposed adaptive-fuzzy-HCC is compared with fixed-HCC and adaptive-HCC techniques and the superior features of this novel approach are established. The FLC based shunt APF system is validated through extensive simulation for diode-rectifier/R-L loads. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Fuzzy physical programming for Space Manoeuvre Vehicles trajectory optimization based on hp-adaptive pseudospectral method

NASA Astrophysics Data System (ADS)

Chai, Runqi; Savvaris, Al; Tsourdos, Antonios

2016-06-01

In this paper, a fuzzy physical programming (FPP) method has been introduced for solving multi-objective Space Manoeuvre Vehicles (SMV) skip trajectory optimization problem based on hp-adaptive pseudospectral methods. The dynamic model of SMV is elaborated and then, by employing hp-adaptive pseudospectral methods, the problem has been transformed to nonlinear programming (NLP) problem. According to the mission requirements, the solutions were calculated for each single-objective scenario. To get a compromised solution for each target, the fuzzy physical programming (FPP) model is proposed. The preference function is established with considering the fuzzy factor of the system such that a proper compromised trajectory can be acquired. In addition, the NSGA-II is tested to obtain the Pareto-optimal solution set and verify the Pareto optimality of the FPP solution. Simulation results indicate that the proposed method is effective and feasible in terms of dealing with the multi-objective skip trajectory optimization for the SMV.

Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology

NASA Astrophysics Data System (ADS)

Petković, Dalibor; Shamshirband, Shahaboddin; Pavlović, Nenad T.; Anuar, Nor Badrul; Kiah, Miss Laiha Mat

2014-07-01

The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

Fuzzy logic of Aristotelian forms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perlovsky, L.I.

1996-12-31

Model-based approaches to pattern recognition and machine vision have been proposed to overcome the exorbitant training requirements of earlier computational paradigms. However, uncertainties in data were found to lead to a combinatorial explosion of the computational complexity. This issue is related here to the roles of a priori knowledge vs. adaptive learning. What is the a-priori knowledge representation that supports learning? I introduce Modeling Field Theory (MFT), a model-based neural network whose adaptive learning is based on a priori models. These models combine deterministic, fuzzy, and statistical aspects to account for a priori knowledge, its fuzzy nature, and data uncertainties.more » In the process of learning, a priori fuzzy concepts converge to crisp or probabilistic concepts. The MFT is a convergent dynamical system of only linear computational complexity. Fuzzy logic turns out to be essential for reducing the combinatorial complexity to linear one. I will discuss the relationship of the new computational paradigm to two theories due to Aristotle: theory of Forms and logic. While theory of Forms argued that the mind cannot be based on ready-made a priori concepts, Aristotelian logic operated with just such concepts. I discuss an interpretation of MFT suggesting that its fuzzy logic, combining a-priority and adaptivity, implements Aristotelian theory of Forms (theory of mind). Thus, 2300 years after Aristotle, a logic is developed suitable for his theory of mind.« less

An Adaptive Handover Prediction Scheme for Seamless Mobility Based Wireless Networks

PubMed Central

Safa Sadiq, Ali; Fisal, Norsheila Binti; Ghafoor, Kayhan Zrar; Lloret, Jaime

2014-01-01

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches. PMID:25574490

An adaptive handover prediction scheme for seamless mobility based wireless networks.

PubMed

Sadiq, Ali Safa; Fisal, Norsheila Binti; Ghafoor, Kayhan Zrar; Lloret, Jaime

2014-01-01

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.

Fuzzy-neural control of an aircraft tracking camera platform

NASA Technical Reports Server (NTRS)

Mcgrath, Dennis

1994-01-01

A fuzzy-neural control system simulation was developed for the control of a camera platform used to observe aircraft on final approach to an aircraft carrier. The fuzzy-neural approach to control combines the structure of a fuzzy knowledge base with a supervised neural network's ability to adapt and improve. The performance characteristics of this hybrid system were compared to those of a fuzzy system and a neural network system developed independently to determine if the fusion of these two technologies offers any advantage over the use of one or the other. The results of this study indicate that the fuzzy-neural approach to control offers some advantages over either fuzzy or neural control alone.

Fuzzy support vector machines for adaptive Morse code recognition.

PubMed

Yang, Cheng-Hong; Jin, Li-Cheng; Chuang, Li-Yeh

2006-11-01

Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code to be effective as a communication tool. Therefore, an adaptive automatic recognition method with a high recognition rate is needed. The proposed system uses both fuzzy support vector machines and the variable-degree variable-step-size least-mean-square algorithm to achieve these objectives. We apply fuzzy memberships to each point, and provide different contributions to the decision learning function for support vector machines. Statistical analyses demonstrated that the proposed method elicited a higher recognition rate than other algorithms in the literature.

Genetic algorithms in adaptive fuzzy control

NASA Technical Reports Server (NTRS)

Karr, C. Lucas; Harper, Tony R.

1992-01-01

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.

Application of Fuzzy Reasoning for Filtering and Enhancement of Ultrasonic Images

NASA Technical Reports Server (NTRS)

Sacha, J. P.; Cios, K. J.; Roth, D. J.; Berke, L.; Vary, A.

1994-01-01

This paper presents a new type of an adaptive fuzzy operator for detection of isolated abnormalities, and enhancement of raw ultrasonic images. Fuzzy sets used in decision rules are defined for each image based on empirical statistics of the color intensities. Examples of the method are also presented in the paper.

Towards autonomous fuzzy control

NASA Technical Reports Server (NTRS)

Shenoi, Sujeet; Ramer, Arthur

1993-01-01

The efficient implementation of on-line adaptation in real time is an important research problem in fuzzy control. The goal is to develop autonomous self-organizing controllers employing system-independent control meta-knowledge which enables them to adjust their control policies depending on the systems they control and the environments in which they operate. An autonomous fuzzy controller would continuously observe system behavior while implementing its control actions and would use the outcomes of these actions to refine its control policy. It could be designed to lie dormant when its control actions give rise to adequate performance characteristics but could rapidly and autonomously initiate real-time adaptation whenever its performance degrades. Such an autonomous fuzzy controller would have immense practical value. It could accommodate individual variations in system characteristics and also compensate for degradations in system characteristics caused by wear and tear. It could also potentially deal with black-box systems and control scenarios. On-going research in autonomous fuzzy control is reported. The ultimate research objective is to develop robust and relatively inexpensive autonomous fuzzy control hardware suitable for use in real time environments.

Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System.

PubMed

Maharlou, Hamidreza; Niakan Kalhori, Sharareh R; Shahbazi, Shahrbanoo; Ravangard, Ramin

2018-04-01

Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery. A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated. The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60). The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.

Adaptive Fuzzy Hysteresis Band Current Controller for Four-Wire Shunt Active Filter

NASA Astrophysics Data System (ADS)

Hamoudi, F.; Chaghi, A.; Amimeur, H.; Merabet, E.

2008-06-01

This paper presents an adaptive fuzzy hysteresis band current controller for four-wire shunt active power filters to eliminate harmonics and to compensate reactive power in distribution systems in order to keep currents at the point of common coupling sinusoidal and in phase with the corresponding voltage and the cancel neutral current. The conventional hysteresis band known for its robustness and its advantage in current controlled applications is adapted with a fuzzy logic controller to change the bandwidth according to the operating point in order to keep the frequency modulation at tolerable limits. The algorithm used to identify the reference currents is based on the synchronous reference frame theory (dqγ). Finally, simulation results using Matlab/Simulink are given to validate the proposed control.

Adaptive Scaling of Cluster Boundaries for Large-Scale Social Media Data Clustering.

PubMed

Meng, Lei; Tan, Ah-Hwee; Wunsch, Donald C

2016-12-01

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the clustering mechanism of Fuzzy ART, and discover the vigilance region (VR) that essentially determines how a cluster in the Fuzzy ART system recognizes similar patterns in the feature space. The VR gives an intrinsic interpretation of the clustering mechanism and limitations of Fuzzy ART. Second, we introduce the idea of allowing different clusters in the Fuzzy ART system to have different vigilance levels in order to meet the diverse nature of the pattern distribution of social media data. To this end, we propose three vigilance adaptation methods, namely, the activation maximization (AM) rule, the confliction minimization (CM) rule, and the hybrid integration (HI) rule. With an initial vigilance value, the resulting clustering algorithms, namely, the AM-ART, CM-ART, and HI-ART, can automatically adapt the vigilance values of all clusters during the learning epochs in order to produce better cluster boundaries. Experiments on four social media data sets show that AM-ART, CM-ART, and HI-ART are more robust than Fuzzy ART to the initial vigilance value, and they usually achieve better or comparable performance and much faster speed than the state-of-the-art clustering algorithms that also do not require a predefined number of clusters.

Fuzzy inference game approach to uncertainty in business decisions and market competitions.

PubMed

Oderanti, Festus Oluseyi

2013-01-01

The increasing challenges and complexity of business environments are making business decisions and operations more difficult for entrepreneurs to predict the outcomes of these processes. Therefore, we developed a decision support scheme that could be used and adapted to various business decision processes. These involve decisions that are made under uncertain situations such as business competition in the market or wage negotiation within a firm. The scheme uses game strategies and fuzzy inference concepts to effectively grasp the variables in these uncertain situations. The games are played between human and fuzzy players. The accuracy of the fuzzy rule base and the game strategies help to mitigate the adverse effects that a business may suffer from these uncertain factors. We also introduced learning which enables the fuzzy player to adapt over time. We tested this scheme in different scenarios and discover that it could be an invaluable tool in the hand of entrepreneurs that are operating under uncertain and competitive business environments.

Pressure intelligent control strategy of Waste heat recovery system of converter vapors

NASA Astrophysics Data System (ADS)

Feng, Xugang; Wu, Zhiwei; Zhang, Jiayan; Qian, Hong

2013-01-01

The converter gas evaporative cooling system is mainly used for absorbing heat in the high temperature exhaust gas which produced by the oxygen blowing reaction. Vaporization cooling steam pressure control system of converter is a nonlinear, time-varying, lagging behind, close coupling of multivariable control object. This article based on the analysis of converter operation characteristics of evaporation cooling system, of vaporization in a production run of pipe pressure variation and disturbance factors.For the dynamic characteristics of the controlled objects,we have improved the conventional PID control scheme.In Oxygen blowing process, we make intelligent control by using fuzzy-PID cascade control method and adjusting the Lance,that it can realize the optimization of the boiler steam pressure control.By design simulation, results show that the design has a good control not only ensures drum steam pressure in the context of security, enabling efficient conversion of waste heat.And the converter of 1800 flue gas through pipes and cool and dust removal also can be cooled to about 800. Therefore the converter haze evaporative cooling system has achieved to the converter haze temperature decrease effect and enhanced to the coal gas returns-ratio.

Robust adaptive controller design for a class of uncertain nonlinear systems using online T-S fuzzy-neural modeling approach.

PubMed

Chien, Yi-Hsing; Wang, Wei-Yen; Leu, Yih-Guang; Lee, Tsu-Tian

2011-04-01

This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design

PubMed Central

Kwong, C. K.; Fung, K. Y.; Jiang, Huimin; Chan, K. Y.

2013-01-01

Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort. PMID:24385884

A modified dynamic evolving neural-fuzzy approach to modeling customer satisfaction for affective design.

PubMed

Kwong, C K; Fung, K Y; Jiang, Huimin; Chan, K Y; Siu, Kin Wai Michael

2013-01-01

Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.

Approximation-Based Discrete-Time Adaptive Position Tracking Control for Interior Permanent Magnet Synchronous Motors.

PubMed

Yu, Jinpeng; Shi, Peng; Yu, Haisheng; Chen, Bing; Lin, Chong

2015-07-01

This paper considers the problem of discrete-time adaptive position tracking control for a interior permanent magnet synchronous motor (IPMSM) based on fuzzy-approximation. Fuzzy logic systems are used to approximate the nonlinearities of the discrete-time IPMSM drive system which is derived by direct discretization using Euler method, and a discrete-time fuzzy position tracking controller is designed via backstepping approach. In contrast to existing results, the advantage of the scheme is that the number of the adjustable parameters is reduced to two only and the problem of coupling nonlinearity can be overcome. It is shown that the proposed discrete-time fuzzy controller can guarantee the tracking error converges to a small neighborhood of the origin and all the signals are bounded. Simulation results illustrate the effectiveness and the potentials of the theoretic results obtained.

Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration

NASA Technical Reports Server (NTRS)

Kottapalli, Sesi; Malki, Heidar A.; Langari, Reza

2003-01-01

Adaptive neuro-fuzzy relationships have been developed to model the UH-60A Black Hawk pilot floor vertical vibration. A 200 point database that approximates the entire UH-60A helicopter flight envelope is used for training and testing purposes. The NASA/Army Airloads Program flight test database was the source of the 200 point database. The present study is conducted in two parts. The first part involves level flight conditions and the second part involves the entire (200 point) database including maneuver conditions. The results show that a neuro-fuzzy model can successfully predict the pilot vibration. Also, it is found that the training phase of this neuro-fuzzy model takes only two or three iterations to converge for most cases. Thus, the proposed approach produces a potentially viable model for real-time implementation.

Control Design and Performance Analysis for Autonomous Formation Flight Experimentss

NASA Astrophysics Data System (ADS)

Rice, Caleb Michael

Autonomous Formation Flight is a key approach for reducing greenhouse gas emissions and managing traffic in future high density airspace. Unmanned Aerial Vehicles (UAV's) have made it possible for the physical demonstration and validation of autonomous formation flight concepts inexpensively and eliminates the flight risk to human pilots. This thesis discusses the design, implementation, and flight testing of three different formation flight control methods, Proportional Integral and Derivative (PID); Fuzzy Logic (FL); and NonLinear Dynamic Inversion (NLDI), and their respective performance behavior. Experimental results show achievable autonomous formation flight and performance quality with a pair of low-cost unmanned research fixed wing aircraft and also with a solo vertical takeoff and landing (VTOL) quadrotor.

Fuzzy adaptive integration scheme for low-cost SINS/GPS navigation system

NASA Astrophysics Data System (ADS)

Nourmohammadi, Hossein; Keighobadi, Jafar

2018-01-01

Due to weak stand-alone accuracy as well as poor run-to-run stability of micro-electro mechanical system (MEMS)-based inertial sensors, special approaches are required to integrate low-cost strap-down inertial navigation system (SINS) with global positioning system (GPS), particularly in long-term applications. This paper aims to enhance long-term performance of conventional SINS/GPS navigation systems using a fuzzy adaptive integration scheme. The main concept behind the proposed adaptive integration is the good performance of attitude-heading reference system (AHRS) in low-accelerated motions and its degradation in maneuvered or accelerated motions. Depending on vehicle maneuvers, gravity-based attitude angles can be intelligently utilized to improve orientation estimation in the SINS. Knowledge-based fuzzy inference system is developed for decision-making between the AHRS and the SINS according to vehicle maneuvering conditions. Inertial measurements are the main input data of the fuzzy system to determine the maneuvering level during the vehicle motions. Accordingly, appropriate weighting coefficients are produced to combine the SINS/GPS and the AHRS, efficiently. The assessment of the proposed integrated navigation system is conducted via real data in airborne tests.

Vehicle tracking using fuzzy-based vehicle detection window with adaptive parameters

NASA Astrophysics Data System (ADS)

Chitsobhuk, Orachat; Kasemsiri, Watjanapong; Glomglome, Sorayut; Lapamonpinyo, Pipatphon

2018-04-01

In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold Estimation (GATE) algorithm is adopted to provide the suitable threshold value for the sobel edge detection. The estimated threshold can be adapted to the changes of diverse illumination conditions throughout the day. This leads to greater vehicle detection performance compared to a fixed user's defined threshold. In the second process, this paper proposes the novel vehicle tracking algorithms namely Fuzzy-based Vehicle Analysis (FBA) in order to reduce the false estimation of the vehicle tracking caused by uneven edges of the large vehicles and vehicle changing lanes. The proposed FBA algorithm employs the average edge density and the Horizontal Moving Edge Detection (HMED) algorithm to alleviate those problems by adopting fuzzy rule-based algorithms to rectify the vehicle tracking. The experimental results demonstrate that the proposed system provides the high accuracy of vehicle detection about 98.22%. In addition, it also offers the low false detection rates about 3.92%.

A fuzzy call admission control scheme in wireless networks

NASA Astrophysics Data System (ADS)

Ma, Yufeng; Gong, Shenguang; Hu, Xiulin; Zhang, Yunyu

2007-11-01

Scarcity of the spectrum resource and mobility of users make quality of service (QoS) provision a critical issue in wireless networks. This paper presents a fuzzy call admission control scheme to meet the requirement of the QoS. A performance measure is formed as a weighted linear function of new call and handoff call blocking probabilities. Simulation compares the proposed fuzzy scheme with an adaptive channel reservation scheme. Simulation results show that fuzzy scheme has a better robust performance in terms of average blocking criterion.

Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 1

NASA Technical Reports Server (NTRS)

Lea, Robert N. (Editor); Villarreal, James (Editor)

1991-01-01

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Houston, Clear Lake. The workshop was held April 11 to 13 at the Johnson Space Flight Center. Technical topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making.

Solid oxide fuel cell anode image segmentation based on a novel quantum-inspired fuzzy clustering

NASA Astrophysics Data System (ADS)

Fu, Xiaowei; Xiang, Yuhan; Chen, Li; Xu, Xin; Li, Xi

2015-12-01

High quality microstructure modeling can optimize the design of fuel cells. For three-phase accurate identification of Solid Oxide Fuel Cell (SOFC) microstructure, this paper proposes a novel image segmentation method on YSZ/Ni anode Optical Microscopic (OM) images. According to Quantum Signal Processing (QSP), the proposed approach exploits a quantum-inspired adaptive fuzziness factor to adaptively estimate the energy function in the fuzzy system based on Markov Random Filed (MRF). Before defuzzification, a quantum-inspired probability distribution based on distance and gray correction is proposed, which can adaptively adjust the inaccurate probability estimation of uncertain points caused by noises and edge points. In this study, the proposed method improves accuracy and effectiveness of three-phase identification on the micro-investigation. It provides firm foundation to investigate the microstructural evolution and its related properties.

New agrophysics divisions: application of ANFIS, fuzzy indicator modeling, physic-technical bases of plant breeding, and materials based on humic acids (review)

USDA-ARS?s Scientific Manuscript database

This work is devoted to review the new scientific divisions that emerged in agrophysics in the last 10-15 years. Among them are the following: 1) application of Adaptive Neuro-Fuzzy Inference System (ANFIS), 2) development and application of fuzzy indicator modeling, 3) agrophysical and physic-tech...

Hybrid adaptive ascent flight control for a flexible launch vehicle

NASA Astrophysics Data System (ADS)

Lefevre, Brian D.

For the purpose of maintaining dynamic stability and improving guidance command tracking performance under off-nominal flight conditions, a hybrid adaptive control scheme is selected and modified for use as a launch vehicle flight controller. This architecture merges a model reference adaptive approach, which utilizes both direct and indirect adaptive elements, with a classical dynamic inversion controller. This structure is chosen for a number of reasons: the properties of the reference model can be easily adjusted to tune the desired handling qualities of the spacecraft, the indirect adaptive element (which consists of an online parameter identification algorithm) continually refines the estimates of the evolving characteristic parameters utilized in the dynamic inversion, and the direct adaptive element (which consists of a neural network) augments the linear feedback signal to compensate for any nonlinearities in the vehicle dynamics. The combination of these elements enables the control system to retain the nonlinear capabilities of an adaptive network while relying heavily on the linear portion of the feedback signal to dictate the dynamic response under most operating conditions. To begin the analysis, the ascent dynamics of a launch vehicle with a single 1st stage rocket motor (typical of the Ares 1 spacecraft) are characterized. The dynamics are then linearized with assumptions that are appropriate for a launch vehicle, so that the resulting equations may be inverted by the flight controller in order to compute the control signals necessary to generate the desired response from the vehicle. Next, the development of the hybrid adaptive launch vehicle ascent flight control architecture is discussed in detail. Alterations of the generic hybrid adaptive control architecture include the incorporation of a command conversion operation which transforms guidance input from quaternion form (as provided by NASA) to the body-fixed angular rate commands needed by the hybrid adaptive flight controller, development of a Newton's method based online parameter update that is modified to include a step size which regulates the rate of change in the parameter estimates, comparison of the modified Newton's method and recursive least squares online parameter update algorithms, modification of the neural network's input structure to accommodate for the nature of the nonlinearities present in a launch vehicle's ascent flight, examination of both tracking error based and modeling error based neural network weight update laws, and integration of feedback filters for the purpose of preventing harmful interaction between the flight control system and flexible structural modes. To validate the hybrid adaptive controller, a high-fidelity Ares I ascent flight simulator and a classical gain-scheduled proportional-integral-derivative (PID) ascent flight controller were obtained from the NASA Marshall Space Flight Center. The classical PID flight controller is used as a benchmark when analyzing the performance of the hybrid adaptive flight controller. Simulations are conducted which model both nominal and off-nominal flight conditions with structural flexibility of the vehicle either enabled or disabled. First, rigid body ascent simulations are performed with the hybrid adaptive controller under nominal flight conditions for the purpose of selecting the update laws which drive the indirect and direct adaptive components. With the neural network disabled, the results revealed that the recursive least squares online parameter update caused high frequency oscillations to appear in the engine gimbal commands. This is highly undesirable for long and slender launch vehicles, such as the Ares I, because such oscillation of the rocket nozzle could excite unstable structural flex modes. In contrast, the modified Newton's method online parameter update produced smooth control signals and was thus selected for use in the hybrid adaptive launch vehicle flight controller. In the simulations where the online parameter identification algorithm was disabled, the tracking error based neural network weight update law forced the network's output to diverge despite repeated reductions of the adaptive learning rate. As a result, the modeling error based neural network weight update law (which generated bounded signals) is utilized by the hybrid adaptive controller in all subsequent simulations. Comparing the PID and hybrid adaptive flight controllers under nominal flight conditions in rigid body ascent simulations showed that their tracking error magnitudes are similar for a period of time during the middle of the ascent phase. Though the PID controller performs better for a short interval around the 20 second mark, the hybrid adaptive controller performs far better from roughly 70 to 120 seconds. Elevating the aerodynamic loads by increasing the force and moment coefficients produced results very similar to the nominal case. However, applying a 5% or 10% thrust reduction to the first stage rocket motor causes the tracking error magnitude observed by the PID controller to be significantly elevated and diverge rapidly as the simulation concludes. In contrast, the hybrid adaptive controller steadily maintains smaller errors (often less than 50% of the corresponding PID value). Under the same sets of flight conditions with flexibility enabled, the results exhibit similar trends with the hybrid adaptive controller performing even better in each case. Again, the reduction of the first stage rocket motor's thrust clearly illustrated the superior robustness of the hybrid adaptive flight controller.

North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 1

NASA Technical Reports Server (NTRS)

Villarreal, James A. (Compiler)

1992-01-01

This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such as neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies.

North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2

NASA Technical Reports Server (NTRS)

Villarreal, James A. (Compiler)

1992-01-01

This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies.

PID Tuning Using Extremum Seeking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Killingsworth, N; Krstic, M

2005-11-15

Although proportional-integral-derivative (PID) controllers are widely used in the process industry, their effectiveness is often limited due to poor tuning. Manual tuning of PID controllers, which requires optimization of three parameters, is a time-consuming task. To remedy this difficulty, much effort has been invested in developing systematic tuning methods. Many of these methods rely on knowledge of the plant model or require special experiments to identify a suitable plant model. Reviews of these methods are given in [1] and the survey paper [2]. However, in many situations a plant model is not known, and it is not desirable to openmore » the process loop for system identification. Thus a method for tuning PID parameters within a closed-loop setting is advantageous. In relay feedback tuning [3]-[5], the feedback controller is temporarily replaced by a relay. Relay feedback causes most systems to oscillate, thus determining one point on the Nyquist diagram. Based on the location of this point, PID parameters can be chosen to give the closed-loop system a desired phase and gain margin. An alternative tuning method, which does not require either a modification of the system or a system model, is unfalsified control [6], [7]. This method uses input-output data to determine whether a set of PID parameters meets performance specifications. An adaptive algorithm is used to update the PID controller based on whether or not the controller falsifies a given criterion. The method requires a finite set of candidate PID controllers that must be initially specified [6]. Unfalsified control for an infinite set of PID controllers has been developed in [7]; this approach requires a carefully chosen input signal [8]. Yet another model-free PID tuning method that does not require opening of the loop is iterative feedback tuning (IFT). IFT iteratively optimizes the controller parameters with respect to a cost function derived from the output signal of the closed-loop system, see [9]. This method is based on the performance of the closed-loop system during a step response experiment [10], [11]. In this article we present a method for optimizing the step response of a closed-loop system consisting of a PID controller and an unknown plant with a discrete version of extremum seeking (ES). Specifically, ES is used to minimize a cost function similar to that used in [10], [11], which quantifies the performance of the PID controller. ES, a non-model-based method, iteratively modifies the arguments (in this application the PID parameters) of a cost function so that the output of the cost function reaches a local minimum or local maximum. In the next section we apply ES to PID controller tuning. We illustrate this technique through simulations comparing the effectiveness of ES to other PID tuning methods. Next, we address the importance of the choice of cost function and consider the effect of controller saturation. Furthermore, we discuss the choice of ES tuning parameters. Finally, we offer some conclusions.« less

Fuzzy adaptive strong tracking scaled unscented Kalman filter for initial alignment of large misalignment angles

NASA Astrophysics Data System (ADS)

Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui

2016-07-01

In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF.

A comparative study of artificial intelligent-based maximum power point tracking for photovoltaic systems

NASA Astrophysics Data System (ADS)

Hussain Mutlag, Ammar; Mohamed, Azah; Shareef, Hussain

2016-03-01

Maximum power point tracking (MPPT) is normally required to improve the performance of photovoltaic (PV) systems. This paper presents artificial intelligent-based maximum power point tracking (AI-MPPT) by considering three artificial intelligent techniques, namely, artificial neural network (ANN), adaptive neuro fuzzy inference system with seven triangular fuzzy sets (7-tri), and adaptive neuro fuzzy inference system with seven gbell fuzzy sets. The AI-MPPT is designed for the 25 SolarTIFSTF-120P6 PV panels, with the capacity of 3 kW peak. A complete PV system is modelled using 300,000 data samples and simulated in the MATLAB/SIMULINK. The AI-MPPT has been tested under real environmental conditions for two days from 8 am to 18 pm. The results showed that the ANN based MPPT gives the most accurate performance and then followed by the 7-tri-based MPPT.

Fuzzy Edge Connectivity of Graphical Fuzzy State Space Model in Multi-connected System

NASA Astrophysics Data System (ADS)

Harish, Noor Ainy; Ismail, Razidah; Ahmad, Tahir

2010-11-01

Structured networks of interacting components illustrate complex structure in a direct or intuitive way. Graph theory provides a mathematical modeling for studying interconnection among elements in natural and man-made systems. On the other hand, directed graph is useful to define and interpret the interconnection structure underlying the dynamics of the interacting subsystem. Fuzzy theory provides important tools in dealing various aspects of complexity, imprecision and fuzziness of the network structure of a multi-connected system. Initial development for systems of Fuzzy State Space Model (FSSM) and a fuzzy algorithm approach were introduced with the purpose of solving the inverse problems in multivariable system. In this paper, fuzzy algorithm is adapted in order to determine the fuzzy edge connectivity between subsystems, in particular interconnected system of Graphical Representation of FSSM. This new approach will simplify the schematic diagram of interconnection of subsystems in a multi-connected system.

Fuzzy logic applications to control engineering

NASA Astrophysics Data System (ADS)

Langari, Reza

1993-12-01

This paper presents the results of a project presently under way at Texas A&M which focuses on the use of fuzzy logic in integrated control of manufacturing systems. The specific problems investigated here include diagnosis of critical tool wear in machining of metals via a neuro-fuzzy algorithm, as well as compensation of friction in mechanical positioning systems via an adaptive fuzzy logic algorithm. The results indicate that fuzzy logic in conjunction with conventional algorithmic based approaches or neural nets can prove useful in dealing with the intricacies of control/monitoring of manufacturing systems and can potentially play an active role in multi-modal integrated control systems of the future.

Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

USGS Publications Warehouse

Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

2000-01-01

Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

Discrimination of Human Forearm Motions on the Basis of Myoelectric Signals by Using Adaptive Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Kiso, Atsushi; Seki, Hirokazu

This paper describes a method for discriminating of the human forearm motions based on the myoelectric signals using an adaptive fuzzy inference system. In conventional studies, the neural network is often used to estimate motion intention by the myoelectric signals and realizes the high discrimination precision. On the other hand, this study uses the fuzzy inference for a human forearm motion discrimination based on the myoelectric signals. This study designs the membership function and the fuzzy rules using the average value and the standard deviation of the root mean square of the myoelectric potential for every channel of each motion. In addition, the characteristics of the myoelectric potential gradually change as a result of the muscle fatigue. Therefore, the motion discrimination should be performed by taking muscle fatigue into consideration. This study proposes a method to redesign the fuzzy inference system such that dynamic change of the myoelectric potential because of the muscle fatigue will be taken into account. Some experiments carried out using a myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.

WNN 92; Proceedings of the 3rd Workshop on Neural Networks: Academic/Industrial/NASA/Defense, Auburn Univ., AL, Feb. 10-12, 1992 and South Shore Harbour, TX, Nov. 4-6, 1992

NASA Technical Reports Server (NTRS)

Padgett, Mary L. (Editor)

1993-01-01

The present conference discusses such neural networks (NN) related topics as their current development status, NN architectures, NN learning rules, NN optimization methods, NN temporal models, NN control methods, NN pattern recognition systems and applications, biological and biomedical applications of NNs, VLSI design techniques for NNs, NN systems simulation, fuzzy logic, and genetic algorithms. Attention is given to missileborne integrated NNs, adaptive-mixture NNs, implementable learning rules, an NN simulator for travelling salesman problem solutions, similarity-based forecasting, NN control of hypersonic aircraft takeoff, NN control of the Space Shuttle Arm, an adaptive NN robot manipulator controller, a synthetic approach to digital filtering, NNs for speech analysis, adaptive spline networks, an anticipatory fuzzy logic controller, and encoding operations for fuzzy associative memories.

Distributed Adaptive Fuzzy Control for Nonlinear Multiagent Systems Via Sliding Mode Observers.

PubMed

Shen, Qikun; Shi, Peng; Shi, Yan

2016-12-01

In this paper, the problem of distributed adaptive fuzzy control is investigated for high-order uncertain nonlinear multiagent systems on directed graph with a fixed topology. It is assumed that only the outputs of each follower and its neighbors are available in the design of its distributed controllers. Equivalent output injection sliding mode observers are proposed for each follower to estimate the states of itself and its neighbors, and an observer-based distributed adaptive controller is designed for each follower to guarantee that it asymptotically synchronizes to a leader with tracking errors being semi-globally uniform ultimate bounded, in which fuzzy logic systems are utilized to approximate unknown functions. Based on algebraic graph theory and Lyapunov function approach, using Filippov-framework, the closed-loop system stability analysis is conducted. Finally, numerical simulations are provided to illustrate the effectiveness and potential of the developed design techniques.

Fuzzy-rule-based Adaptive Resource Control for Information Sharing in P2P Networks

NASA Astrophysics Data System (ADS)

Wu, Zhengping; Wu, Hao

With more and more peer-to-peer (P2P) technologies available for online collaboration and information sharing, people can launch more and more collaborative work in online social networks with friends, colleagues, and even strangers. Without face-to-face interactions, the question of who can be trusted and then share information with becomes a big concern of a user in these online social networks. This paper introduces an adaptive control service using fuzzy logic in preference definition for P2P information sharing control, and designs a novel decision-making mechanism using formal fuzzy rules and reasoning mechanisms adjusting P2P information sharing status following individual users' preferences. Applications of this adaptive control service into different information sharing environments show that this service can provide a convenient and accurate P2P information sharing control for individual users in P2P networks.

Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs.

PubMed

Shi, Wuxi; Luo, Rui; Li, Baoquan

2017-01-01

In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Fuzzy Adaptive Output Feedback Control of Uncertain Nonlinear Systems With Prescribed Performance.

PubMed

Zhang, Jin-Xi; Yang, Guang-Hong

2018-05-01

This paper investigates the tracking control problem for a family of strict-feedback systems in the presence of unknown nonlinearities and immeasurable system states. A low-complexity adaptive fuzzy output feedback control scheme is proposed, based on a backstepping method. In the control design, a fuzzy adaptive state observer is first employed to estimate the unmeasured states. Then, a novel error transformation approach together with a new modification mechanism is introduced to guarantee the finite-time convergence of the output error to a predefined region and ensure the closed-loop stability. Compared with the existing methods, the main advantages of our approach are that: 1) without using extra command filters or auxiliary dynamic surface control techniques, the problem of explosion of complexity can still be addressed and 2) the design procedures are independent of the initial conditions. Finally, two practical examples are performed to further illustrate the above theoretic findings.

Type-2 fuzzy logic control based MRAS speed estimator for speed sensorless direct torque and flux control of an induction motor drive.

PubMed

Ramesh, Tejavathu; Kumar Panda, Anup; Shiva Kumar, S

2015-07-01

In this research study, a model reference adaptive system (MRAS) speed estimator for speed sensorless direct torque and flux control (DTFC) of an induction motor drive (IMD) using two adaptation mechanism schemes are proposed to replace the conventional proportional integral controller (PIC). The first adaptation mechanism scheme is based on Type-1 fuzzy logic controller (T1FLC), which is used to achieve high performance sensorless drive in both transient as well as steady state conditions. However, the Type-1 fuzzy sets are certain and unable to work effectively when higher degree of uncertainties presents in the system which can be caused by sudden change in speed or different load disturbances, process noise etc. Therefore, a new Type-2 fuzzy logic controller (T2FLC) based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties and improves the performance and also robust to various load torque and sudden change in speed conditions, respectively. The detailed performances of various adaptation mechanism schemes are carried out in a MATLAB/Simulink environment with a speed sensor and speed sensorless modes of operation when an IMD is operating under different operating conditions, such as, no-load, load and sudden change in speed, respectively. To validate the different control approaches, the system also implemented on real-time system and adequate results are reported for its validation. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

The Study and Design of Adaptive Learning System Based on Fuzzy Set Theory

NASA Astrophysics Data System (ADS)

Jia, Bing; Zhong, Shaochun; Zheng, Tianyang; Liu, Zhiyong

Adaptive learning is an effective way to improve the learning outcomes, that is, the selection of learning content and presentation should be adapted to each learner's learning context, learning levels and learning ability. Adaptive Learning System (ALS) can provide effective support for adaptive learning. This paper proposes a new ALS based on fuzzy set theory. It can effectively estimate the learner's knowledge level by test according to learner's target. Then take the factors of learner's cognitive ability and preference into consideration to achieve self-organization and push plan of knowledge. This paper focuses on the design and implementation of domain model and user model in ALS. Experiments confirmed that the system providing adaptive content can effectively help learners to memory the content and improve their comprehension.

SU-F-J-10: Sliding Mode Control of a SMA Actuated Active Flexible Needle for Medical Procedures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Podder, T

Purpose: In medical interventional procedures such as brachytherapy, ablative therapies and biopsy precise steering and accurate placement of needles are very important for anatomical obstacle avoidance and accurate targeting. This study presents the efficacy of a sliding mode controller for Shape Memory Alloy (SMA) actuated flexible needle for medical procedures. Methods: Second order system dynamics of the SMA actuated active flexible needle was used for deriving the sliding mode control equations. Both proportional-integral-derivative (PID) and adaptive PID sliding mode control (APIDSMC) algorithms were developed and implemented. The flexible needle was attached at the end of a 6 DOF robotic system.more » Through LabView programming environment, the control commands were generated using the PID and APIDSMC algorithms. Experiments with artificial tissue mimicking phantom were performed to evaluate the performance of the controller. The actual needle tip position was obtained using an electromagnetic (EM) tracking sensor (Aurora, NDI, waterloo, Canada) at a sampling period of 1ms. During experiment, external disturbances were created applying force and thermal shock to investigate the robustness of the controllers. Results: The root mean square error (RMSE) values for APIDSMC and PID controllers were 0.75 mm and 0.92 mm, respectively, for sinusoidal reference input. In the presence of external disturbances, the APIDSMC controller showed much smoother and less overshooting response compared to that of the PID controller. Conclusion: Performance of the APIDSMC was superior to the PID controller. The APIDSMC was proved to be more effective controller in compensating the SMA uncertainties and external disturbances with clinically acceptable thresholds.« less

Anticipatory Neurofuzzy Control

NASA Technical Reports Server (NTRS)

Mccullough, Claire L.

1994-01-01

Technique of feedback control, called "anticipatory neurofuzzy control," developed for use in controlling flexible structures and other dynamic systems for which mathematical models of dynamics poorly known or unknown. Superior ability to act during operation to compensate for, and adapt to, errors in mathematical model of dynamics, changes in dynamics, and noise. Also offers advantage of reduced computing time. Hybrid of two older fuzzy-logic control techniques: standard fuzzy control and predictive fuzzy control.

High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets

NASA Astrophysics Data System (ADS)

Chen, Tai-Liang; Cheng, Ching-Hsue; Teoh, Hia-Jong

2008-02-01

Stock investors usually make their short-term investment decisions according to recent stock information such as the late market news, technical analysis reports, and price fluctuations. To reflect these short-term factors which impact stock price, this paper proposes a comprehensive fuzzy time-series, which factors linear relationships between recent periods of stock prices and fuzzy logical relationships (nonlinear relationships) mined from time-series into forecasting processes. In empirical analysis, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Heng Seng Index) are employed as experimental datasets, and four recent fuzzy time-series models, Chen’s (1996), Yu’s (2005), Cheng’s (2006) and Chen’s (2007), are used as comparison models. Besides, to compare with conventional statistic method, the method of least squares is utilized to estimate the auto-regressive models of the testing periods within the databases. From analysis results, the performance comparisons indicate that the multi-period adaptation model, proposed in this paper, can effectively improve the forecasting performance of conventional fuzzy time-series models which only factor fuzzy logical relationships in forecasting processes. From the empirical study, the traditional statistic method and the proposed model both reveal that stock price patterns in the Taiwan stock and Hong Kong stock markets are short-term.

L∞-gain adaptive fuzzy fault accommodation control design for nonlinear time-delay systems.

PubMed

Wu, Huai-Ning; Qiang, Xiao-Hong; Guo, Lei

2011-06-01

In this paper, an adaptive fuzzy fault accommodation (FA) control design with a guaranteed L(∞)-gain performance is developed for a class of nonlinear time-delay systems with persistent bounded disturbances. Using the Lyapunov technique and the Razumikhin-type lemma, the existence condition of the L(∞) -gain adaptive fuzzy FA controllers is provided in terms of linear matrix inequalities (LMIs). In the proposed FA scheme, a fuzzy logic system is employed to approximate the unknown term in the derivative of the Lyapunov function due to the unknown fault function; a continuous-state feedback control strategy is adopted for the control design to avoid the undesirable chattering phenomenon. The resulting FA controllers can ensure that every response of the closed-loop system is uniformly ultimately bounded with a guaranteed L(∞)-gain performance in the presence of a fault. Moreover, by the existing LMI optimization technique, a suboptimal controller is obtained in the sense of minimizing an upper bound of the L(∞)-gain. Finally, the achieved simulation results on the FA control of a continuous stirred tank reactor (CSTR) show the effectiveness of the proposed design procedure.

Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses.

PubMed

Mathur, Neha; Glesk, Ivan; Buis, Arjan

2016-10-01

Monitoring of the interface temperature at skin level in lower-limb prosthesis is notoriously complicated. This is due to the flexible nature of the interface liners used impeding the required consistent positioning of the temperature sensors during donning and doffing. Predicting the in-socket residual limb temperature by monitoring the temperature between socket and liner rather than skin and liner could be an important step in alleviating complaints on increased temperature and perspiration in prosthetic sockets. In this work, we propose to implement an adaptive neuro fuzzy inference strategy (ANFIS) to predict the in-socket residual limb temperature. ANFIS belongs to the family of fused neuro fuzzy system in which the fuzzy system is incorporated in a framework which is adaptive in nature. The proposed method is compared to our earlier work using Gaussian processes for machine learning. By comparing the predicted and actual data, results indicate that both the modeling techniques have comparable performance metrics and can be efficiently used for non-invasive temperature monitoring. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Information analysis of immune and endocrine organs. Morphological changes in the course of infection.

PubMed

Avtandilov, G G; Barsukov, V S

1992-11-01

Morphological and morphometric studies were conducted into lymphoid and endocrine organs of 259 human adults and infants with pyoinflammatory diseases (PID) and of 300 experimental mice. Informative and correlation analyses of the data thus recorded provided evidence to the effect that in the course of an infection process adaptation and compensation responses were characterized by intensified exchange of information within the immune-endocrine system (IES). Septic courses of PID were found to be accompanied by impairment of inter-organ correlations, increase in information entropy and progressive structural disorganization of the IES.

Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Shaohua

This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaosmore » of PMSM and show the effectiveness and robustness of the proposed method.« less

Designing a Successful Bidding Strategy Using Fuzzy Sets and Agent Attitudes

NASA Astrophysics Data System (ADS)

Ma, Jun; Goyal, Madhu Lata

To be successful in a multi-attribute auction, agents must be capable of adapting to continuously changing bidding price. This chapter presents a novel fuzzy attitude-based bidding strategy (FA-Bid), which employs dual assessment technique, i.e., assessment of multiple attributes of the goods as well as assessment of agents' attitude (eagerness) to procure an item in automated auction. The assessment of attributes adapts the fuzzy sets technique to handle uncertainty of the bidding process as well use heuristic rules to determine the attitude of bidding agents in simulated auctions to procure goods. The overall assessment is used to determine a price range based on current bid, which finally selects the best one as the new bid.

Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain.

PubMed

Luo, Shaohua

2014-09-01

This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.

Modal-space reference-model-tracking fuzzy control of earthquake excited structures

NASA Astrophysics Data System (ADS)

Park, Kwan-Soon; Ok, Seung-Yong

2015-01-01

This paper describes an adaptive modal-space reference-model-tracking fuzzy control technique for the vibration control of earthquake-excited structures. In the proposed approach, the fuzzy logic is introduced to update optimal control force so that the controlled structural response can track the desired response of a reference model. For easy and practical implementation, the reference model is constructed by assigning the target damping ratios to the first few dominant modes in modal space. The numerical simulation results demonstrate that the proposed approach successfully achieves not only the adaptive fault-tolerant control system against partial actuator failures but also the robust performance against the variations of the uncertain system properties by redistributing the feedback control forces to the available actuators.

Anfis Approach for Sssc Controller Design for the Improvement of Transient Stability Performance

NASA Astrophysics Data System (ADS)

Khuntia, Swasti R.; Panda, Sidhartha

2011-06-01

In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) method based on the Artificial Neural Network (ANN) is applied to design a Static Synchronous Series Compensator (SSSC)-based controller for improvement of transient stability. The proposed ANFIS controller combines the advantages of fuzzy controller and quick response and adaptability nature of ANN. The ANFIS structures were trained using the generated database by fuzzy controller of SSSC. It is observed that the proposed SSSC controller improves greatly the voltage profile of the system under severe disturbances. The results prove that the proposed SSSC-based ANFIS controller is found to be robust to fault location and change in operating conditions. Further, the results obtained are compared with the conventional lead-lag controllers for SSSC.

Aerodynamic load control strategy of wind turbine in microgrid

NASA Astrophysics Data System (ADS)

Wang, Xiangming; Liu, Heshun; Chen, Yanfei

2017-12-01

A control strategy is proposed in the paper to optimize the aerodynamic load of the wind turbine in micro-grid. In grid-connection mode, the wind turbine adopts a new individual variable pitch control strategy. The pitch angle of the blade is rapidly given by the controller, and the pitch angle of each blade is fine tuned by the weight coefficient distributor. In islanding mode, according to the requirements of energy storage system, a given power tracking control method based on fuzzy PID control is proposed. Simulation result shows that this control strategy can effectively improve the axial aerodynamic load of the blade under rated wind speed in grid-connection mode, and ensure the smooth operation of the micro-grid in islanding mode.

Position control of an electro-pneumatic system based on PWM technique and FLC.

PubMed

Najjari, Behrouz; Barakati, S Masoud; Mohammadi, Ali; Futohi, Muhammad J; Bostanian, Muhammad

2014-03-01

In this paper, modeling and PWM based control of an electro-pneumatic system, including the four 2-2 valves and a double acting cylinder are studied. Dynamic nonlinear behavior of the system, containing fast switching solenoid valves and a pneumatic cylinder, as well as electrical, magnetic, mechanical, and fluid subsystems are modeled. A DC-DC power converter is employed to improve solenoid valve performance and suppress system delay. Among different position control methods, a proportional integrator derivative (PID) controller and fuzzy logic controller (FLC) are evaluated. An experimental setup, using an AVR microcontroller is implemented. Simulation and experimental results verify the effectiveness of the proposed control strategies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

A fuzzy adaptive network approach to parameter estimation in cases where independent variables come from an exponential distribution

NASA Astrophysics Data System (ADS)

Dalkilic, Turkan Erbay; Apaydin, Aysen

2009-11-01

In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y=f(X)+[epsilon]. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the [`]switching regression model' and it is expressed with . Here li indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665-685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363-399; E.Q. Richard, A new approach to estimating switching regressions, Journal of the American Statistical Association 67 (338) (1972) 306-310]. In this study, adaptive networks have been used to construct a model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, in defining the optimal class number of independent variables, the use of suggested validity criterion for fuzzy clustering has been aimed. In the case that independent variables have an exponential distribution, an algorithm has been suggested for defining the unknown parameter of the switching regression model and for obtaining the estimated values after obtaining an optimal membership function, which is suitable for exponential distribution.

A neuro-fuzzy architecture for real-time applications

NASA Technical Reports Server (NTRS)

Ramamoorthy, P. A.; Huang, Song

1992-01-01

Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach.

Genetic Adaptive Control for PZT Actuators

NASA Technical Reports Server (NTRS)

Kim, Jeongwook; Stover, Shelley K.; Madisetti, Vijay K.

1995-01-01

A piezoelectric transducer (PZT) is capable of providing linear motion if controlled correctly and could provide a replacement for traditional heavy and large servo systems using motors. This paper focuses on a genetic model reference adaptive control technique (GMRAC) for a PZT which is moving a mirror where the goal is to keep the mirror velocity constant. Genetic Algorithms (GAs) are an integral part of the GMRAC technique acting as the search engine for an optimal PID controller. Two methods are suggested to control the actuator in this research. The first one is to change the PID parameters and the other is to add an additional reference input in the system. The simulation results of these two methods are compared. Simulated Annealing (SA) is also used to solve the problem. Simulation results of GAs and SA are compared after simulation. GAs show the best result according to the simulation results. The entire model is designed using the Mathworks' Simulink tool.

Enhancing the stabilization of aircraft pitch motion control via intelligent and classical method

NASA Astrophysics Data System (ADS)

Lukman, H.; Munawwarah, S.; Azizan, A.; Yakub, F.; Zaki, S. A.; Rasid, Z. A.

2017-12-01

The pitching movement of an aircraft is very important to ensure passengers are intrinsically safe and the aircraft achieve its maximum stability. The equations governing the motion of an aircraft are a complex set of six nonlinear coupled differential equations. Under certain assumptions, it can be decoupled and linearized into longitudinal and lateral equations. Pitch control is a longitudinal problem and thus, only the longitudinal dynamics equations are involved in this system. It is a third order nonlinear system, which is linearized about the operating point. The system is also inherently unstable due to the presence of a free integrator. Because of this, a feedback controller is added in order to solve this problem and enhance the system performance. This study uses two approaches in designing controller: a conventional controller and an intelligent controller. The pitch control scheme consists of proportional, integral and derivatives (PID) for conventional controller and fuzzy logic control (FLC) for intelligent controller. Throughout the paper, the performance of the presented controllers are investigated and compared based on the common criteria of step response. Simulation results have been obtained and analysed by using Matlab and Simulink software. The study shows that FLC controller has higher ability to control and stabilize the aircraft's pitch angle as compared to PID controller.

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

NASA Technical Reports Server (NTRS)

Culbert, Christopher J. (Editor)

1993-01-01

Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making.

eFSM--a novel online neural-fuzzy semantic memory model.

PubMed

Tung, Whye Loon; Quek, Chai

2010-01-01

Fuzzy rule-based systems (FRBSs) have been successfully applied to many areas. However, traditional fuzzy systems are often manually crafted, and their rule bases that represent the acquired knowledge are static and cannot be trained to improve the modeling performance. This subsequently leads to intensive research on the autonomous construction and tuning of a fuzzy system directly from the observed training data to address the knowledge acquisition bottleneck, resulting in well-established hybrids such as neural-fuzzy systems (NFSs) and genetic fuzzy systems (GFSs). However, the complex and dynamic nature of real-world problems demands that fuzzy rule-based systems and models be able to adapt their parameters and ultimately evolve their rule bases to address the nonstationary (time-varying) characteristics of their operating environments. Recently, considerable research efforts have been directed to the study of evolving Tagaki-Sugeno (T-S)-type NFSs based on the concept of incremental learning. In contrast, there are very few incremental learning Mamdani-type NFSs reported in the literature. Hence, this paper presents the evolving neural-fuzzy semantic memory (eFSM) model, a neural-fuzzy Mamdani architecture with a data-driven progressively adaptive structure (i.e., rule base) based on incremental learning. Issues related to the incremental learning of the eFSM rule base are carefully investigated, and a novel parameter learning approach is proposed for the tuning of the fuzzy set parameters in eFSM. The proposed eFSM model elicits highly interpretable semantic knowledge in the form of Mamdani-type if-then fuzzy rules from low-level numeric training data. These Mamdani fuzzy rules define the computing structure of eFSM and are incrementally learned with the arrival of each training data sample. New rules are constructed from the emergence of novel training data and obsolete fuzzy rules that no longer describe the recently observed data trends are pruned. This enables eFSM to maintain a current and compact set of Mamdani-type if-then fuzzy rules that collectively generalizes and describes the salient associative mappings between the inputs and outputs of the underlying process being modeled. The learning and modeling performances of the proposed eFSM are evaluated using several benchmark applications and the results are encouraging.

Novel Observer Scheme of Fuzzy-MRAS Sensorless Speed Control of Induction Motor Drive

NASA Astrophysics Data System (ADS)

Chekroun, S.; Zerikat, M.; Mechernene, A.; Benharir, N.

2017-01-01

This paper presents a novel approach Fuzzy-MRAS conception for robust accurate tracking of induction motor drive operating in a high-performance drives environment. Of the different methods for sensorless control of induction motor drive the model reference adaptive system (MRAS) finds lot of attention due to its good performance. The analysis of the sensorless vector control system using MRAS is presented and the resistance parameters variations and speed observer using new Fuzzy Self-Tuning adaptive IP Controller is proposed. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. The present approach helps to achieve a good dynamic response, disturbance rejection and low to plant parameter variations of the induction motor. In order to verify the performances of the proposed observer and control algorithms and to test behaviour of the controlled system, numerical simulation is achieved. Simulation results are presented and discussed to shown the validity and the performance of the proposed observer.

Active fault tolerant control based on interval type-2 fuzzy sliding mode controller and non linear adaptive observer for 3-DOF laboratory helicopter.

PubMed

Zeghlache, Samir; Benslimane, Tarak; Bouguerra, Abderrahmen

2017-11-01

In this paper, a robust controller for a three degree of freedom (3 DOF) helicopter control is proposed in presence of actuator and sensor faults. For this purpose, Interval type-2 fuzzy logic control approach (IT2FLC) and sliding mode control (SMC) technique are used to design a controller, named active fault tolerant interval type-2 Fuzzy Sliding mode controller (AFTIT2FSMC) based on non-linear adaptive observer to estimate and detect the system faults for each subsystem of the 3-DOF helicopter. The proposed control scheme allows avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the rules number of the fuzzy controller. Exponential stability of the closed loop is guaranteed by using the Lyapunov method. The simulation results show that the AFTIT2FSMC can greatly alleviate the chattering effect, providing good tracking performance, even in presence of actuator and sensor faults. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive Neural Networks for Automatic Negotiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sakas, D. P.; Vlachos, D. S.; Simos, T. E.

The use of fuzzy logic and fuzzy neural networks has been found effective for the modelling of the uncertain relations between the parameters of a negotiation procedure. The problem with these configurations is that they are static, that is, any new knowledge from theory or experiment lead to the construction of entirely new models. To overcome this difficulty, we apply in this work, an adaptive neural topology to model the negotiation process. Finally a simple simulation is carried in order to test the new method.

The architecture of adaptive neural network based on a fuzzy inference system for implementing intelligent control in photovoltaic systems

NASA Astrophysics Data System (ADS)

Gimazov, R.; Shidlovskiy, S.

2018-05-01

In this paper, we consider the architecture of the algorithm for extreme regulation in the photovoltaic system. An algorithm based on an adaptive neural network with fuzzy inference is proposed. The implementation of such an algorithm not only allows solving a number of problems in existing algorithms for extreme power regulation of photovoltaic systems, but also creates a reserve for the creation of a universal control system for a photovoltaic system.

A NOISE ADAPTIVE FUZZY EQUALIZATION METHOD FOR PROCESSING SOLAR EXTREME ULTRAVIOLET IMAGES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Druckmueller, M., E-mail: druckmuller@fme.vutbr.cz

A new image enhancement tool ideally suited for the visualization of fine structures in extreme ultraviolet images of the corona is presented in this paper. The Noise Adaptive Fuzzy Equalization method is particularly suited for the exceptionally high dynamic range images from the Atmospheric Imaging Assembly instrument on the Solar Dynamics Observatory. This method produces artifact-free images and gives significantly better results than methods based on convolution or Fourier transform which are often used for that purpose.

Fuzzy/Neural Software Estimates Costs of Rocket-Engine Tests

NASA Technical Reports Server (NTRS)

Douglas, Freddie; Bourgeois, Edit Kaminsky

2005-01-01

The Highly Accurate Cost Estimating Model (HACEM) is a software system for estimating the costs of testing rocket engines and components at Stennis Space Center. HACEM is built on a foundation of adaptive-network-based fuzzy inference systems (ANFIS) a hybrid software concept that combines the adaptive capabilities of neural networks with the ease of development and additional benefits of fuzzy-logic-based systems. In ANFIS, fuzzy inference systems are trained by use of neural networks. HACEM includes selectable subsystems that utilize various numbers and types of inputs, various numbers of fuzzy membership functions, and various input-preprocessing techniques. The inputs to HACEM are parameters of specific tests or series of tests. These parameters include test type (component or engine test), number and duration of tests, and thrust level(s) (in the case of engine tests). The ANFIS in HACEM are trained by use of sets of these parameters, along with costs of past tests. Thereafter, the user feeds HACEM a simple input text file that contains the parameters of a planned test or series of tests, the user selects the desired HACEM subsystem, and the subsystem processes the parameters into an estimate of cost(s).

Ozone levels in the Empty Quarter of Saudi Arabia--application of adaptive neuro-fuzzy model.

PubMed

Rahman, Syed Masiur; Khondaker, A N; Khan, Rouf Ahmad

2013-05-01

In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO₂ concentrations and their transformations, as inputs. The root mean square error and Willmott's index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model.

Space-Time Joint Interference Cancellation Using Fuzzy-Inference-Based Adaptive Filtering Techniques in Frequency-Selective Multipath Channels

NASA Astrophysics Data System (ADS)

Hu, Chia-Chang; Lin, Hsuan-Yu; Chen, Yu-Fan; Wen, Jyh-Horng

2006-12-01

An adaptive minimum mean-square error (MMSE) array receiver based on the fuzzy-logic recursive least-squares (RLS) algorithm is developed for asynchronous DS-CDMA interference suppression in the presence of frequency-selective multipath fading. This receiver employs a fuzzy-logic control mechanism to perform the nonlinear mapping of the squared error and squared error variation, denoted by ([InlineEquation not available: see fulltext.],[InlineEquation not available: see fulltext.]), into a forgetting factor[InlineEquation not available: see fulltext.]. For the real-time applicability, a computationally efficient version of the proposed receiver is derived based on the least-mean-square (LMS) algorithm using the fuzzy-inference-controlled step-size[InlineEquation not available: see fulltext.]. This receiver is capable of providing both fast convergence/tracking capability as well as small steady-state misadjustment as compared with conventional LMS- and RLS-based MMSE DS-CDMA receivers. Simulations show that the fuzzy-logic LMS and RLS algorithms outperform, respectively, other variable step-size LMS (VSS-LMS) and variable forgetting factor RLS (VFF-RLS) algorithms at least 3 dB and 1.5 dB in bit-error-rate (BER) for multipath fading channels.

Fuzzy Logic-Based Filter for Removing Additive and Impulsive Noise from Color Images

NASA Astrophysics Data System (ADS)

Zhu, Yuhong; Li, Hongyang; Jiang, Huageng

2017-12-01

This paper presents an efficient filter method based on fuzzy logics for adaptively removing additive and impulsive noise from color images. The proposed filter comprises two parts including noise detection and noise removal filtering. In the detection part, the fuzzy peer group concept is applied to determine what type of noise is added to each pixel of the corrupted image. In the filter part, the impulse noise is deducted by the vector median filter in the CIELAB color space and an optimal fuzzy filter is introduced to reduce the Gaussian noise, while they can work together to remove the mixed Gaussian-impulse noise from color images. Experimental results on several color images proves the efficacy of the proposed fuzzy filter.

Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network

NASA Astrophysics Data System (ADS)

Shao, Yuxiang; Chen, Qing; Wei, Zhenhua

Logistics distribution center location evaluation is a dynamic, fuzzy, open and complicated nonlinear system, which makes it difficult to evaluate the distribution center location by the traditional analysis method. The paper proposes a distribution center location evaluation system which uses the fuzzy neural network combined with the genetic algorithm. In this model, the neural network is adopted to construct the fuzzy system. By using the genetic algorithm, the parameters of the neural network are optimized and trained so as to improve the fuzzy system’s abilities of self-study and self-adaptation. At last, the sampled data are trained and tested by Matlab software. The simulation results indicate that the proposed identification model has very small errors.

Development of neural network techniques for finger-vein pattern classification

NASA Astrophysics Data System (ADS)

Wu, Jian-Da; Liu, Chiung-Tsiung; Tsai, Yi-Jang; Liu, Jun-Ching; Chang, Ya-Wen

2010-02-01

A personal identification system using finger-vein patterns and neural network techniques is proposed in the present study. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis and classification. The biometric system for verification consists of a combination of feature extraction using principal component analysis and pattern classification using both back-propagation network and adaptive neuro-fuzzy inference systems. Finger-vein features are first extracted by principal component analysis method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed adaptive neuro-fuzzy inference system in the pattern classification, the back-propagation network is compared with the proposed system. The experimental results indicated the proposed system using adaptive neuro-fuzzy inference system demonstrated a better performance than the back-propagation network for personal identification using the finger-vein patterns.

Application of Soft Computing in Coherent Communications Phase Synchronization

NASA Technical Reports Server (NTRS)

Drake, Jeffrey T.; Prasad, Nadipuram R.

2000-01-01

The use of soft computing techniques in coherent communications phase synchronization provides an alternative to analytical or hard computing methods. This paper discusses a novel use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for phase synchronization in coherent communications systems utilizing Multiple Phase Shift Keying (MPSK) modulation. A brief overview of the M-PSK digital communications bandpass modulation technique is presented and it's requisite need for phase synchronization is discussed. We briefly describe the hybrid platform developed by Jang that incorporates fuzzy/neural structures namely the, Adaptive Neuro-Fuzzy Interference Systems (ANFIS). We then discuss application of ANFIS to phase estimation for M-PSK. The modeling of both explicit, and implicit phase estimation schemes for M-PSK symbols with unknown structure are discussed. Performance results from simulation of the above scheme is presented.

Functional Based Adaptive and Fuzzy Sliding Controller for Non-Autonomous Active Suspension System

NASA Astrophysics Data System (ADS)

Huang, Shiuh-Jer; Chen, Hung-Yi

In this paper, an adaptive sliding controller is developed for controlling a vehicle active suspension system. The functional approximation technique is employed to substitute the unknown non-autonomous functions of the suspension system and release the model-based requirement of sliding mode control algorithm. In order to improve the control performance and reduce the implementation problem, a fuzzy strategy with online learning ability is added to compensate the functional approximation error. The update laws of the functional approximation coefficients and the fuzzy tuning parameters are derived from the Lyapunov theorem to guarantee the system stability. The proposed controller is implemented on a quarter-car hydraulic actuating active suspension system test-rig. The experimental results show that the proposed controller suppresses the oscillation amplitude of the suspension system effectively.

Soft computing methods for geoidal height transformation

NASA Astrophysics Data System (ADS)

Akyilmaz, O.; Özlüdemir, M. T.; Ayan, T.; Çelik, R. N.

2009-07-01

Soft computing techniques, such as fuzzy logic and artificial neural network (ANN) approaches, have enabled researchers to create precise models for use in many scientific and engineering applications. Applications that can be employed in geodetic studies include the estimation of earth rotation parameters and the determination of mean sea level changes. Another important field of geodesy in which these computing techniques can be applied is geoidal height transformation. We report here our use of a conventional polynomial model, the Adaptive Network-based Fuzzy (or in some publications, Adaptive Neuro-Fuzzy) Inference System (ANFIS), an ANN and a modified ANN approach to approximate geoid heights. These approximation models have been tested on a number of test points. The results obtained through the transformation processes from ellipsoidal heights into local levelling heights have also been compared.

On position/force tracking control problem of cooperative robot manipulators using adaptive fuzzy backstepping approach.

PubMed

Baigzadehnoe, Barmak; Rahmani, Zahra; Khosravi, Alireza; Rezaie, Behrooz

2017-09-01

In this paper, the position and force tracking control problem of cooperative robot manipulator system handling a common rigid object with unknown dynamical models and unknown external disturbances is investigated. The universal approximation properties of fuzzy logic systems are employed to estimate the unknown system dynamics. On the other hand, by defining new state variables based on the integral and differential of position and orientation errors of the grasped object, the error system of coordinated robot manipulators is constructed. Subsequently by defining the appropriate change of coordinates and using the backstepping design strategy, an adaptive fuzzy backstepping position tracking control scheme is proposed for multi-robot manipulator systems. By utilizing the properties of internal forces, extra terms are also added to the control signals to consider the force tracking problem. Moreover, it is shown that the proposed adaptive fuzzy backstepping position/force control approach ensures all the signals of the closed loop system uniformly ultimately bounded and tracking errors of both positions and forces can converge to small desired values by proper selection of the design parameters. Finally, the theoretic achievements are tested on the two three-link planar robot manipulators cooperatively handling a common object to illustrate the effectiveness of the proposed approach. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Estimating Reservoir Inflow Using RADAR Forecasted Precipitation and Adaptive Neuro Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Yi, J.; Choi, C.

2014-12-01

Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.

A Review and Reappraisal of Adaptive Human-Computer Interfaces in Complex Control Systems

DTIC Science & Technology

2006-08-01

maneuverability measures. The cost elements were expressed as fuzzy membership functions. Figure 9 shows the flowchart of the route planner. A fuzzy navigator...and updating of the user model, which contains information about three generic stereotypes ( beginner , intermediate and expert users) plus an

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 1

NASA Technical Reports Server (NTRS)

Culbert, Christopher J. (Editor)

1993-01-01

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake. The workshop was held June 1-3, 1992 at the Lyndon B. Johnson Space Center in Houston, Texas. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control, and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making.

Adaptive Fuzzy Systems in Computational Intelligence

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1996-01-01

In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.

Land cover classification of Landsat 8 satellite data based on Fuzzy Logic approach

NASA Astrophysics Data System (ADS)

Taufik, Afirah; Sakinah Syed Ahmad, Sharifah

2016-06-01

The aim of this paper is to propose a method to classify the land covers of a satellite image based on fuzzy rule-based system approach. The study uses bands in Landsat 8 and other indices, such as Normalized Difference Water Index (NDWI), Normalized difference built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) as input for the fuzzy inference system. The selected three indices represent our main three classes called water, built- up land, and vegetation. The combination of the original multispectral bands and selected indices provide more information about the image. The parameter selection of fuzzy membership is performed by using a supervised method known as ANFIS (Adaptive neuro fuzzy inference system) training. The fuzzy system is tested for the classification on the land cover image that covers Klang Valley area. The results showed that the fuzzy system approach is effective and can be explored and implemented for other areas of Landsat data.

Fuzzy control for closed-loop, patient-specific hypnosis in intraoperative patients: a simulation study.

PubMed

Moore, Brett L; Pyeatt, Larry D; Doufas, Anthony G

2009-01-01

Research has demonstrated the efficacy of closed-loop control of anesthesia using bispectral index (BIS) as the controlled variable, and the recent development of model-based, patient-adaptive systems has considerably improved anesthetic control. To further explore the use of model-based control in anesthesia, we investigated the application of fuzzy control in the delivery of patient-specific propofol-induced hypnosis. In simulated intraoperative patients, the fuzzy controller demonstrated clinically acceptable performance, suggesting that further study is warranted.

Target of physiological gait: Realization of speed adaptive control for a prosthetic knee during swing flexion.

PubMed

Cao, Wujing; Yu, Hongliu; Zhao, Weiliang; Li, Jin; Wei, Xiaodong

2018-01-01

Prosthetic knee is the most important component of lower limb prosthesis. Speed adaptive for prosthetic knee during swing flexion is the key method to realize physiological gait. This study aims to discuss the target of physiological gait, propose a speed adaptive control method during swing flexion and research the damping adjustment law of intelligent hydraulic prosthetic knee. According to the physiological gait trials of healthy people, the control target during swing flexion is defined. A new prosthetic knee with fuzzy logical control during swing flexion is designed to realize the damping adjustment automatically. The function simulation and evaluation system of intelligent knee prosthesis is provided. Speed adaptive control test of the intelligent prosthetic knee in different velocities are researched. The maximum swing flexion of the knee angle is set between sixty degree and seventy degree as the target of physiological gait. Preliminary experimental results demonstrate that the prosthetic knee with fuzzy logical control is able to realize physiological gait under different speeds. The faster the walking, the bigger the valve closure percentage of the hydraulic prosthetic knee. The proposed fuzzy logical control strategy and intelligent hydraulic prosthetic knee are effective for the amputee to achieve physiological gait.

Robust Fuzzy Controllers Using FPGAs

NASA Technical Reports Server (NTRS)

Monroe, Author Gene S., Jr.

2007-01-01

Electro-mechanical device controllers typically come in one of three forms, proportional (P), Proportional Derivative (PD), and Proportional Integral Derivative (PID). Two methods of control are discussed in this paper; they are (1) the classical technique that requires an in-depth mathematical use of poles and zeros, and (2) the fuzzy logic (FL) technique that is similar to the way humans think and make decisions. FL controllers are used in multiple industries; examples include control engineering, computer vision, pattern recognition, statistics, and data analysis. Presented is a study on the development of a PD motor controller written in very high speed hardware description language (VHDL), and implemented in FL. Four distinct abstractions compose the FL controller, they are the fuzzifier, the rule-base, the fuzzy inference system (FIS), and the defuzzifier. FL is similar to, but different from, Boolean logic; where the output value may be equal to 0 or 1, but it could also be equal to any decimal value between them. This controller is unique because of its VHDL implementation, which uses integer mathematics. To compensate for VHDL's inability to synthesis floating point numbers, a scale factor equal to 10(sup (N/4) is utilized; where N is equal to data word size. The scaling factor shifts the decimal digits to the left of the decimal point for increased precision. PD controllers are ideal for use with servo motors, where position control is effective. This paper discusses control methods for motion-base platforms where a constant velocity equivalent to a spectral resolution of 0.25 cm(exp -1) is required; however, the control capability of this controller extends to various other platforms.

Fuzzy logic and neural network technologies

NASA Technical Reports Server (NTRS)

Villarreal, James A.; Lea, Robert N.; Savely, Robert T.

1992-01-01

Applications of fuzzy logic technologies in NASA projects are reviewed to examine their advantages in the development of neural networks for aerospace and commercial expert systems and control. Examples of fuzzy-logic applications include a 6-DOF spacecraft controller, collision-avoidance systems, and reinforcement-learning techniques. The commercial applications examined include a fuzzy autofocusing system, an air conditioning system, and an automobile transmission application. The practical use of fuzzy logic is set in the theoretical context of artificial neural systems (ANSs) to give the background for an overview of ANS research programs at NASA. The research and application programs include the Network Execution and Training Simulator and faster training algorithms such as the Difference Optimized Training Scheme. The networks are well suited for pattern-recognition applications such as predicting sunspots, controlling posture maintenance, and conducting adaptive diagnoses.

Simulation analysis of adaptive cruise prediction control

NASA Astrophysics Data System (ADS)

Zhang, Li; Cui, Sheng Min

2017-09-01

Predictive control is suitable for multi-variable and multi-constraint system control.In order to discuss the effect of predictive control on the vehicle longitudinal motion, this paper establishes the expected spacing model by combining variable pitch spacing and the of safety distance strategy. The model predictive control theory and the optimization method based on secondary planning are designed to obtain and track the best expected acceleration trajectory quickly. Simulation models are established including predictive and adaptive fuzzy control. Simulation results show that predictive control can realize the basic function of the system while ensuring the safety. The application of predictive and fuzzy adaptive algorithm in cruise condition indicates that the predictive control effect is better.

Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints

NASA Astrophysics Data System (ADS)

Edalati, L.; Khaki Sedigh, A.; Aliyari Shooredeli, M.; Moarefianpour, A.

2018-02-01

This paper deals with the design of adaptive fuzzy dynamic surface control for uncertain strict-feedback nonlinear systems with asymmetric time-varying output constraints in the presence of input saturation. To approximate the unknown nonlinear functions and overcome the problem of explosion of complexity, a Fuzzy logic system is combined with the dynamic surface control in the backstepping design technique. To ensure the output constraints satisfaction, an asymmetric time-varying Barrier Lyapunov Function (BLF) is used. Moreover, by applying the minimal learning parameter technique, the number of the online parameters update for each subsystem is reduced to 2. Hence, the semi-globally uniformly ultimately boundedness (SGUUB) of all the closed-loop signals with appropriate tracking error convergence is guaranteed. The effectiveness of the proposed control is demonstrated by two simulation examples.

Fuzzy control of small servo motors

NASA Technical Reports Server (NTRS)

Maor, Ron; Jani, Yashvant

1993-01-01

To explore the benefits of fuzzy logic and understand the differences between the classical control methods and fuzzy control methods, the Togai InfraLogic applications engineering staff developed and implemented a motor control system for small servo motors. The motor assembly for testing the fuzzy and conventional controllers consist of servo motor RA13M and an encoder with a range of 4096 counts. An interface card was designed and fabricated to interface the motor assembly and encoder to an IBM PC. The fuzzy logic based motor controller was developed using the TILShell and Fuzzy C Development System on an IBM PC. A Proportional-Derivative (PD) type conventional controller was also developed and implemented in the IBM PC to compare the performance with the fuzzy controller. Test cases were defined to include step inputs of 90 and 180 degrees rotation, sine and square wave profiles in 5 to 20 hertz frequency range, as well as ramp inputs. In this paper we describe our approach to develop a fuzzy as well as PH controller, provide details of hardware set-up and test cases, and discuss the performance results. In comparison, the fuzzy logic based controller handles the non-linearities of the motor assembly very well and provides excellent control over a broad range of parameters. Fuzzy technology, as indicated by our results, possesses inherent adaptive features.

System identification of smart structures using a wavelet neuro-fuzzy model

NASA Astrophysics Data System (ADS)

Mitchell, Ryan; Kim, Yeesock; El-Korchi, Tahar

2012-11-01

This paper proposes a complex model of smart structures equipped with magnetorheological (MR) dampers. Nonlinear behavior of the structure-MR damper systems is represented by the use of a wavelet-based adaptive neuro-fuzzy inference system (WANFIS). The WANFIS is developed through the integration of wavelet transforms, artificial neural networks, and fuzzy logic theory. To evaluate the effectiveness of the WANFIS model, a three-story building employing an MR damper under a variety of natural hazards is investigated. An artificial earthquake is used for training the input-output mapping of the WANFIS model. The artificial earthquake is generated such that the characteristics of a variety of real recorded earthquakes are included. It is demonstrated that this new WANFIS approach is effective in modeling nonlinear behavior of the structure-MR damper system subjected to a variety of disturbances while resulting in shorter training times in comparison with an adaptive neuro-fuzzy inference system (ANFIS) model. Comparison with high fidelity data proves the viability of the proposed approach in a structural health monitoring setting, and it is validated using known earthquake signals such as El-Centro, Kobe, Northridge, and Hachinohe.

Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA

PubMed Central

Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari

2014-01-01

A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962

Adaptive Jacobian Fuzzy Attitude Control for Flexible Spacecraft Combined Attitude and Sun Tracking System

NASA Astrophysics Data System (ADS)

Chak, Yew-Chung; Varatharajoo, Renuganth

2016-07-01

Many spacecraft attitude control systems today use reaction wheels to deliver precise torques to achieve three-axis attitude stabilization. However, irrecoverable mechanical failure of reaction wheels could potentially lead to mission interruption or total loss. The electrically-powered Solar Array Drive Assemblies (SADA) are usually installed in the pitch axis which rotate the solar arrays to track the Sun, can produce torques to compensate for the pitch-axis wheel failure. In addition, the attitude control of a flexible spacecraft poses a difficult problem. These difficulties include the strong nonlinear coupled dynamics between the rigid hub and flexible solar arrays, and the imprecisely known system parameters, such as inertia matrix, damping ratios, and flexible mode frequencies. In order to overcome these drawbacks, the adaptive Jacobian tracking fuzzy control is proposed for the combined attitude and sun-tracking control problem of a flexible spacecraft during attitude maneuvers in this work. For the adaptation of kinematic and dynamic uncertainties, the proposed scheme uses an adaptive sliding vector based on estimated attitude velocity via approximate Jacobian matrix. The unknown nonlinearities are approximated by deriving the fuzzy models with a set of linguistic If-Then rules using the idea of sector nonlinearity and local approximation in fuzzy partition spaces. The uncertain parameters of the estimated nonlinearities and the Jacobian matrix are being adjusted online by an adaptive law to realize feedback control. The attitude of the spacecraft can be directly controlled with the Jacobian feedback control when the attitude pointing trajectory is designed with respect to the spacecraft coordinate frame itself. A significant feature of this work is that the proposed adaptive Jacobian tracking scheme will result in not only the convergence of angular position and angular velocity tracking errors, but also the convergence of estimated angular velocity to the actual angular velocity. Numerical results are presented to demonstrate the effectiveness of the proposed scheme in tracking the desired attitude, as well as suppressing the elastic deflection effects of solar arrays during maneuver.

An Adaptive Supervisory Sliding Fuzzy Cerebellar Model Articulation Controller for Sensorless Vector-Controlled Induction Motor Drive Systems

PubMed Central

Wang, Shun-Yuan; Tseng, Chwan-Lu; Lin, Shou-Chuang; Chiu, Chun-Jung; Chou, Jen-Hsiang

2015-01-01

This paper presents the implementation of an adaptive supervisory sliding fuzzy cerebellar model articulation controller (FCMAC) in the speed sensorless vector control of an induction motor (IM) drive system. The proposed adaptive supervisory sliding FCMAC comprised a supervisory controller, integral sliding surface, and an adaptive FCMAC. The integral sliding surface was employed to eliminate steady-state errors and enhance the responsiveness of the system. The adaptive FCMAC incorporated an FCMAC with a compensating controller to perform a desired control action. The proposed controller was derived using the Lyapunov approach, which guarantees learning-error convergence. The implementation of three intelligent control schemes—the adaptive supervisory sliding FCMAC, adaptive sliding FCMAC, and adaptive sliding CMAC—were experimentally investigated under various conditions in a realistic sensorless vector-controlled IM drive system. The root mean square error (RMSE) was used as a performance index to evaluate the experimental results of each control scheme. The analysis results indicated that the proposed adaptive supervisory sliding FCMAC substantially improved the system performance compared with the other control schemes. PMID:25815450

An adaptive supervisory sliding fuzzy cerebellar model articulation controller for sensorless vector-controlled induction motor drive systems.

PubMed

Wang, Shun-Yuan; Tseng, Chwan-Lu; Lin, Shou-Chuang; Chiu, Chun-Jung; Chou, Jen-Hsiang

2015-03-25

This paper presents the implementation of an adaptive supervisory sliding fuzzy cerebellar model articulation controller (FCMAC) in the speed sensorless vector control of an induction motor (IM) drive system. The proposed adaptive supervisory sliding FCMAC comprised a supervisory controller, integral sliding surface, and an adaptive FCMAC. The integral sliding surface was employed to eliminate steady-state errors and enhance the responsiveness of the system. The adaptive FCMAC incorporated an FCMAC with a compensating controller to perform a desired control action. The proposed controller was derived using the Lyapunov approach, which guarantees learning-error convergence. The implementation of three intelligent control schemes--the adaptive supervisory sliding FCMAC, adaptive sliding FCMAC, and adaptive sliding CMAC--were experimentally investigated under various conditions in a realistic sensorless vector-controlled IM drive system. The root mean square error (RMSE) was used as a performance index to evaluate the experimental results of each control scheme. The analysis results indicated that the proposed adaptive supervisory sliding FCMAC substantially improved the system performance compared with the other control schemes.

A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

PubMed Central

Wang, Zhihao; Yi, Jing

2016-01-01

For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291

Adaptive fuzzy controller for thermal comfort inside the air-conditioned automobile chamber

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tong, L.; Yu, B.; Chen, Z.

1999-07-01

In order to meet the passengers' demand for thermal comfort, the adaptive fuzzy logic control design methodology is applied for the automobile airconditioner system. In accordance with the theory of air flow and heat transfer, the air temperature field inside the airconditioned automobile chamber is simulated by a set of simplified half-empirical formula. Then, instead of PMV (Predicted Mean Vote) criterion, RIV (Real Individual Vote) criterion is adopted as the base of the control for passengers' thermal comfort. The proposed controller is applied to the air temperature regulation at the individual passenger position. The control procedure is based on partitioningmore » the state space of the system into cell-groups and fuzzily quantificating the state space into these cells. When the system model has some parameter perturbation, the controller can also adjust its control parameters to compensate for the perturbation and maintain the good performance. The learning procedure shows its ideal effect in both computer simulation and experiments. The final results demonstrate the ideal performance of this adaptive fuzzy controller.« less

Automatic detection of multiple UXO-like targets using magnetic anomaly inversion and self-adaptive fuzzy c-means clustering

NASA Astrophysics Data System (ADS)

Yin, Gang; Zhang, Yingtang; Fan, Hongbo; Ren, Guoquan; Li, Zhining

2017-12-01

We have developed a method for automatically detecting UXO-like targets based on magnetic anomaly inversion and self-adaptive fuzzy c-means clustering. Magnetic anomaly inversion methods are used to estimate the initial locations of multiple UXO-like sources. Although these initial locations have some errors with respect to the real positions, they form dense clouds around the actual positions of the magnetic sources. Then we use the self-adaptive fuzzy c-means clustering algorithm to cluster these initial locations. The estimated number of cluster centroids represents the number of targets and the cluster centroids are regarded as the locations of magnetic targets. Effectiveness of the method has been demonstrated using synthetic datasets. Computational results show that the proposed method can be applied to the case of several UXO-like targets that are randomly scattered within in a confined, shallow subsurface, volume. A field test was carried out to test the validity of the proposed method and the experimental results show that the prearranged magnets can be detected unambiguously and located precisely.

A reduced-order adaptive neuro-fuzzy inference system model as a software sensor for rapid estimation of five-day biochemical oxygen demand

NASA Astrophysics Data System (ADS)

Noori, Roohollah; Safavi, Salman; Nateghi Shahrokni, Seyyed Afshin

2013-07-01

The five-day biochemical oxygen demand (BOD5) is one of the key parameters in water quality management. In this study, a novel approach, i.e., reduced-order adaptive neuro-fuzzy inference system (ROANFIS) model was developed for rapid estimation of BOD5. In addition, an uncertainty analysis of adaptive neuro-fuzzy inference system (ANFIS) and ROANFIS models was carried out based on Monte-Carlo simulation. Accuracy analysis of ANFIS and ROANFIS models based on both developed discrepancy ratio and threshold statistics revealed that the selected ROANFIS model was superior. Pearson correlation coefficient (R) and root mean square error for the best fitted ROANFIS model were 0.96 and 7.12, respectively. Furthermore, uncertainty analysis of the developed models indicated that the selected ROANFIS had less uncertainty than the ANFIS model and accurately forecasted BOD5 in the Sefidrood River Basin. Besides, the uncertainty analysis also showed that bracketed predictions by 95% confidence bound and d-factor in the testing steps for the selected ROANFIS model were 94% and 0.83, respectively.

Method of Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement

PubMed Central

Yang, Jie; Kasabov, Nikola

2017-01-01

Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affects the clinical diagnoses. This paper proposes an algorithm for medical image enhancement based on the nonsubsampled contourlet transform (NSCT), which combines adaptive threshold and an improved fuzzy set. First, the original image is decomposed into the NSCT domain with a low-frequency subband and several high-frequency subbands. Then, a linear transformation is adopted for the coefficients of the low-frequency component. An adaptive threshold method is used for the removal of high-frequency image noise. Finally, the improved fuzzy set is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experiments and simulation results show that the proposed method is superior to existing methods of image noise removal, improves the contrast of the image significantly, and obtains a better visual effect. PMID:28744464

Using a Mixed Model to Explore Evaluation Criteria for Bank Supervision: A Banking Supervision Law Perspective

PubMed Central

Tsai, Sang-Bing; Chen, Kuan-Yu; Zhao, Hongrui; Wei, Yu-Min; Wang, Cheng-Kuang; Zheng, Yuxiang; Chang, Li-Chung; Wang, Jiangtao

2016-01-01

Financial supervision means that monetary authorities have the power to supervise and manage financial institutions according to laws. Monetary authorities have this power because of the requirements of improving financial services, protecting the rights of depositors, adapting to industrial development, ensuring financial fair trade, and maintaining stable financial order. To establish evaluation criteria for bank supervision in China, this study integrated fuzzy theory and the decision making trial and evaluation laboratory (DEMATEL) and proposes a fuzzy-DEMATEL model. First, fuzzy theory was applied to examine bank supervision criteria and analyze fuzzy semantics. Second, the fuzzy-DEMATEL model was used to calculate the degree to which financial supervision criteria mutually influenced one another and their causal relationship. Finally, an evaluation criteria model for evaluating bank and financial supervision was established. PMID:27992449

Using a Mixed Model to Explore Evaluation Criteria for Bank Supervision: A Banking Supervision Law Perspective.

PubMed

Tsai, Sang-Bing; Chen, Kuan-Yu; Zhao, Hongrui; Wei, Yu-Min; Wang, Cheng-Kuang; Zheng, Yuxiang; Chang, Li-Chung; Wang, Jiangtao

2016-01-01

Financial supervision means that monetary authorities have the power to supervise and manage financial institutions according to laws. Monetary authorities have this power because of the requirements of improving financial services, protecting the rights of depositors, adapting to industrial development, ensuring financial fair trade, and maintaining stable financial order. To establish evaluation criteria for bank supervision in China, this study integrated fuzzy theory and the decision making trial and evaluation laboratory (DEMATEL) and proposes a fuzzy-DEMATEL model. First, fuzzy theory was applied to examine bank supervision criteria and analyze fuzzy semantics. Second, the fuzzy-DEMATEL model was used to calculate the degree to which financial supervision criteria mutually influenced one another and their causal relationship. Finally, an evaluation criteria model for evaluating bank and financial supervision was established.

Fuzzy Multicriteria Decision Analysis for Adaptive Watershed Management

NASA Astrophysics Data System (ADS)

Chang, N.

2006-12-01

The dramatic changes of societal complexity due to intensive interactions among agricultural, industrial, and municipal sectors have resulted in acute issues of water resources redistribution and water quality management in many river basins. Given the fact that integrated watershed management is more a political and societal than a technical challenge, there is a need for developing a compelling method leading to justify a water-based land use program in some critical regions. Adaptive watershed management is viewed as an indispensable tool nowadays for providing step-wise constructive decision support that is concerned with all related aspects of the water consumption cycle and those facilities affecting water quality and quantity temporally and spatially. Yet the greatest challenge that decision makers face today is to consider how to leverage ambiguity, paradox, and uncertainty to their competitive advantage of management policy quantitatively. This paper explores a fuzzy multicriteria evaluation method for water resources redistribution and subsequent water quality management with respect to a multipurpose channel-reservoir system--the Tseng- Wen River Basin, South Taiwan. Four fuzzy operators tailored for this fuzzy multicriteria decision analysis depict greater flexibility in representing the complexity of various possible trade-offs among management alternatives constrained by physical, economic, and technical factors essential for adaptive watershed management. The management strategies derived may enable decision makers to integrate a vast number of internal weirs, water intakes, reservoirs, drainage ditches, transfer pipelines, and wastewater treatment facilities within the basin and bring up the permitting issue for transboundary diversion from a neighboring river basin. Experience gained indicates that the use of different types of fuzzy operators is highly instructive, which also provide unique guidance collectively for achieving the overarching goals of sustainable development on a regional scale.

Real-time fuzzy inference based robot path planning

NASA Technical Reports Server (NTRS)

Pacini, Peter J.; Teichrow, Jon S.

1990-01-01

This project addresses the problem of adaptive trajectory generation for a robot arm. Conventional trajectory generation involves computing a path in real time to minimize a performance measure such as expended energy. This method can be computationally intensive, and it may yield poor results if the trajectory is weakly constrained. Typically some implicit constraints are known, but cannot be encoded analytically. The alternative approach used here is to formulate domain-specific knowledge, including implicit and ill-defined constraints, in terms of fuzzy rules. These rules utilize linguistic terms to relate input variables to output variables. Since the fuzzy rulebase is determined off-line, only high-level, computationally light processing is required in real time. Potential applications for adaptive trajectory generation include missile guidance and various sophisticated robot control tasks, such as automotive assembly, high speed electrical parts insertion, stepper alignment, and motion control for high speed parcel transfer systems.

Indirect adaptive fuzzy fault-tolerant tracking control for MIMO nonlinear systems with actuator and sensor failures.

PubMed

Bounemeur, Abdelhamid; Chemachema, Mohamed; Essounbouli, Najib

2018-05-10

In this paper, an active fuzzy fault tolerant tracking control (AFFTTC) scheme is developed for a class of multi-input multi-output (MIMO) unknown nonlinear systems in the presence of unknown actuator faults, sensor failures and external disturbance. The developed control scheme deals with four kinds of faults for both sensors and actuators. The bias, drift, and loss of accuracy additive faults are considered along with the loss of effectiveness multiplicative fault. A fuzzy adaptive controller based on back-stepping design is developed to deal with actuator failures and unknown system dynamics. However, an additional robust control term is added to deal with sensor faults, approximation errors, and external disturbances. Lyapunov theory is used to prove the stability of the closed loop system. Numerical simulations on a quadrotor are presented to show the effectiveness of the proposed approach. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive Performance Seeking Control Using Fuzzy Model Reference Learning Control and Positive Gradient Control

NASA Technical Reports Server (NTRS)

Kopasakis, George

1997-01-01

Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.

Adaptive neuro fuzzy system for modelling and prediction of distance pantograph catenary in railway transportation

NASA Astrophysics Data System (ADS)

Panoiu, M.; Panoiu, C.; Lihaciu, I. L.

2018-01-01

This research presents an adaptive neuro-fuzzy system which is used in the prediction of the distance between the pantograph and contact line of the electrical locomotives used in railway transportation. In railway transportation any incident that occurs in the electrical system can have major negative effects: traffic interrupts, equipment destroying. Therefore, a prediction as good as possible of such situations is very useful. In the paper was analyzing the possibility of modeling and prediction the variation of the distance between the pantograph and the contact line using intelligent techniques

Approximation abilities of neuro-fuzzy networks

NASA Astrophysics Data System (ADS)

Mrówczyńska, Maria

2010-01-01

The paper presents the operation of two neuro-fuzzy systems of an adaptive type, intended for solving problems of the approximation of multi-variable functions in the domain of real numbers. Neuro-fuzzy systems being a combination of the methodology of artificial neural networks and fuzzy sets operate on the basis of a set of fuzzy rules "if-then", generated by means of the self-organization of data grouping and the estimation of relations between fuzzy experiment results. The article includes a description of neuro-fuzzy systems by Takaga-Sugeno-Kang (TSK) and Wang-Mendel (WM), and in order to complement the problem in question, a hierarchical structural self-organizing method of teaching a fuzzy network. A multi-layer structure of the systems is a structure analogous to the structure of "classic" neural networks. In its final part the article presents selected areas of application of neuro-fuzzy systems in the field of geodesy and surveying engineering. Numerical examples showing how the systems work concerned: the approximation of functions of several variables to be used as algorithms in the Geographic Information Systems (the approximation of a terrain model), the transformation of coordinates, and the prediction of a time series. The accuracy characteristics of the results obtained have been taken into consideration.

Fuzzy logic control of telerobot manipulators

NASA Technical Reports Server (NTRS)

Franke, Ernest A.; Nedungadi, Ashok

1992-01-01

Telerobot systems for advanced applications will require manipulators with redundant 'degrees of freedom' (DOF) that are capable of adapting manipulator configurations to avoid obstacles while achieving the user specified goal. Conventional methods for control of manipulators (based on solution of the inverse kinematics) cannot be easily extended to these situations. Fuzzy logic control offers a possible solution to these needs. A current research program at SRI developed a fuzzy logic controller for a redundant, 4 DOF, planar manipulator. The manipulator end point trajectory can be specified by either a computer program (robot mode) or by manual input (teleoperator). The approach used expresses end-point error and the location of manipulator joints as fuzzy variables. Joint motions are determined by a fuzzy rule set without requiring solution of the inverse kinematics. Additional rules for sensor data, obstacle avoidance and preferred manipulator configuration, e.g., 'righty' or 'lefty', are easily accommodated. The procedure used to generate the fuzzy rules can be extended to higher DOF systems.

Study of distributed learning as a solution to category proliferation in Fuzzy ARTMAP based neural systems.

PubMed

Parrado-Hernández, Emilio; Gómez-Sánchez, Eduardo; Dimitriadis, Yannis A

2003-09-01

An evaluation of distributed learning as a means to attenuate the category proliferation problem in Fuzzy ARTMAP based neural systems is carried out, from both qualitative and quantitative points of view. The study involves two original winner-take-all (WTA) architectures, Fuzzy ARTMAP and FasArt, and their distributed versions, dARTMAP and dFasArt. A qualitative analysis of the distributed learning properties of dARTMAP is made, focusing on the new elements introduced to endow Fuzzy ARTMAP with distributed learning. In addition, a quantitative study on a selected set of classification problems points out that problems have to present certain features in their output classes in order to noticeably reduce the number of recruited categories and achieve an acceptable classification accuracy. As part of this analysis, distributed learning was successfully adapted to a member of the Fuzzy ARTMAP family, FasArt, and similar procedures can be used to extend distributed learning capabilities to other Fuzzy ARTMAP based systems.

AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

PubMed Central

Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

2015-01-01

Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes’ status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors’ detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability. PMID:26193280

Identification and real-time position control of a servo-hydraulic rotary actuator by means of a neurobiologically motivated algorithm.

PubMed

Sadeghieh, Ali; Sazgar, Hadi; Goodarzi, Kamyar; Lucas, Caro

2012-01-01

This paper presents a new intelligent approach for adaptive control of a nonlinear dynamic system. A modified version of the brain emotional learning based intelligent controller (BELBIC), a bio-inspired algorithm based upon a computational model of emotional learning which occurs in the amygdala, is utilized for position controlling a real laboratorial rotary electro-hydraulic servo (EHS) system. EHS systems are known to be nonlinear and non-smooth due to many factors such as leakage, friction, hysteresis, null shift, saturation, dead zone, and especially fluid flow expression through the servo valve. The large value of these factors can easily influence the control performance in the presence of a poor design. In this paper, a mathematical model of the EHS system is derived, and then the parameters of the model are identified using the recursive least squares method. In the next step, a BELBIC is designed based on this dynamic model and utilized to control the real laboratorial EHS system. To prove the effectiveness of the modified BELBIC's online learning ability in reducing the overall tracking error, results have been compared to those obtained from an optimal PID controller, an auto-tuned fuzzy PI controller (ATFPIC), and a neural network predictive controller (NNPC) under similar circumstances. The results demonstrate not only excellent improvement in control action, but also less energy consumption. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Robust H(infinity) tracking control of boiler-turbine systems.

PubMed

Wu, J; Nguang, S K; Shen, J; Liu, G; Li, Y G

2010-07-01

In this paper, the problem of designing a fuzzy H(infinity) state feedback tracking control of a boiler-turbine is solved. First, the Takagi and Sugeno fuzzy model is used to model a boiler-turbine system. Next, based on the Takagi and Sugeno fuzzy model, sufficient conditions for the existence of a fuzzy H(infinity) nonlinear state feedback tracking control are derived in terms of linear matrix inequalities. The advantage of the proposed tracking control design is that it does not involve feedback linearization technique and complicated adaptive scheme. An industrial boiler-turbine system is used to illustrate the effectiveness of the proposed design as compared with a linearized approach. 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Learning and tuning fuzzy logic controllers through reinforcements.

PubMed

Berenji, H R; Khedkar, P

1992-01-01

A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

A New Type of Tea Baking Machine Based on Pro/E Design

NASA Astrophysics Data System (ADS)

Lin, Xin-Ying; Wang, Wei

2017-11-01

In this paper, the production process of wulong tea was discussed, mainly the effect of baking on the quality of tea. The suitable baking temperature of different tea was introduced. Based on Pro/E, a new type of baking machine suitable for wulong tea baking was designed. The working principle, mechanical structure and constant temperature timing intelligent control system of baking machine were expounded. Finally, the characteristics and innovation of new baking machine were discussed.The mechanical structure of this baking machine is more simple and reasonable, and can use the heat of the inlet and outlet, more energy saving and environmental protection. The temperature control part adopts fuzzy PID control, which can improve the accuracy and response speed of temperature control and reduce the dependence of baking operation on skilled experience.

Knowledge acquisition and representation using fuzzy evidential reasoning and dynamic adaptive fuzzy Petri nets.

PubMed

Liu, Hu-Chen; Liu, Long; Lin, Qing-Lian; Liu, Nan

2013-06-01

The two most important issues of expert systems are the acquisition of domain experts' professional knowledge and the representation and reasoning of the knowledge rules that have been identified. First, during expert knowledge acquisition processes, the domain expert panel often demonstrates different experience and knowledge from one another and produces different types of knowledge information such as complete and incomplete, precise and imprecise, and known and unknown because of its cross-functional and multidisciplinary nature. Second, as a promising tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) still suffer a couple of deficiencies. The parameters in current FPN models could not accurately represent the increasingly complex knowledge-based systems, and the rules in most existing knowledge inference frameworks could not be dynamically adjustable according to propositions' variation as human cognition and thinking. In this paper, we present a knowledge acquisition and representation approach using the fuzzy evidential reasoning approach and dynamic adaptive FPNs to solve the problems mentioned above. As is illustrated by the numerical example, the proposed approach can well capture experts' diversity experience, enhance the knowledge representation power, and reason the rule-based knowledge more intelligently.

A new fuzzy-disturbance observer-enhanced sliding controller for vibration control of a train-car suspension with magneto-rheological dampers

NASA Astrophysics Data System (ADS)

Nguyen, Sy Dzung; Choi, Seung-Bok; Nguyen, Quoc Hung

2018-05-01

Semi-active train-car suspensions are always impacted negatively by uncertainty and disturbance (UAD). In order to deal with this, we propose a novel optimal fuzzy disturbance observer-enhanced sliding mode controller (FDO-SMC) for magneto-rheological damper (MRD)-based semi-active train-car suspensions subjected to UAD whose variability rate may be high but bounded. The two main parts of the FDO-SMC are an adaptive sliding mode controller (ad-SMC) and an optimal fuzzy disturbance observer (op-FDO). As the first step, the initial structures of the sliding mode controller (SMC) and disturbance observer (DO) are built. Adaptive update laws for the SMC and DO are then set up synchronously via Lyapunov stability analysis. Subsequently, an optimal fuzzy system (op-FS) is designed to fully implement a parameter constraint mechanism so as to guarantee the system stability converging to the desired state even if the UAD variability rate increases in a given range. As a result, both the ad-SMC and op-FDO are formulated. It is shown from the comparative work with existing controllers that the proposed method provides the best vibration control capability with relatively low consumed power.

Enhancing dissolved oxygen control using an on-line hybrid fuzzy-neural soft-sensing model-based control system in an anaerobic/anoxic/oxic process.

PubMed

Huang, Mingzhi; Wan, Jinquan; Hu, Kang; Ma, Yongwen; Wang, Yan

2013-12-01

An on-line hybrid fuzzy-neural soft-sensing model-based control system was developed to optimize dissolved oxygen concentration in a bench-scale anaerobic/anoxic/oxic (A(2)/O) process. In order to improve the performance of the control system, a self-adapted fuzzy c-means clustering algorithm and adaptive network-based fuzzy inference system (ANFIS) models were employed. The proposed control system permits the on-line implementation of every operating strategy of the experimental system. A set of experiments involving variable hydraulic retention time (HRT), influent pH (pH), dissolved oxygen in the aerobic reactor (DO), and mixed-liquid return ratio (r) was carried out. Using the proposed system, the amount of COD in the effluent stabilized at the set-point and below. The improvement was achieved with optimum dissolved oxygen concentration because the performance of the treatment process was optimized using operating rules implemented in real time. The system allows various expert operational approaches to be deployed with the goal of minimizing organic substances in the outlet while using the minimum amount of energy.

An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions

NASA Astrophysics Data System (ADS)

Ajay Kumar, M.; Srikanth, N. V.

2014-03-01

In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.

FFMPD scales: Comparisons with the FFM, PID-5, and CAT-PD-SF.

PubMed

Crego, Cristina; Oltmanns, Joshua R; Widiger, Thomas A

2018-01-01

A series of 8 Five Factor Model Personality Disorder (FFMPD) scales have been developed to assess, from the perspective of the Five Factor Model (FFM), the maladaptive traits included within DSM-5 Section II personality disorders. An extensive body of FFMPD research has accumulated. However, for the most part, each study has been confined to the scales within 1 particular FFMPD Inventory. The current study considered 36 FFMPD scales, at least 1 from each of the 8 FFMPD inventories, including 8 scales considered to be from neuroticism, 8 from extraversion, 5 from openness, 8 from agreeableness, and 7 from conscientiousness. Their convergent, discriminant, and structural relationship with the FFM was considered, and compared with the structural relationship with the FFM obtained by the Personality Inventory for DSM-5 (PID-5) and the Computerized Adaptive Test-Personality Disorder-Static Form (CAT-PD-SF). Support for an FFM structure was obtained (albeit with agreeableness defining 1 factor and antagonism a separate factor). Similarities and differences across the FFMPD, PID-5, and CAT-PD-SF scales were highlighted. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Automation of a portable extracorporeal circulatory support system with adaptive fuzzy controllers.

PubMed

Mendoza García, A; Krane, M; Baumgartner, B; Sprunk, N; Schreiber, U; Eichhorn, S; Lange, R; Knoll, A

2014-08-01

The presented work relates to the procedure followed for the automation of a portable extracorporeal circulatory support system. Such a device may help increase the chances of survival after suffering from cardiogenic shock outside the hospital, additionally a controller can provide of optimal organ perfusion, while reducing the workload of the operator. Animal experiments were carried out for the acquisition of haemodynamic behaviour of the body under extracorporeal circulation. A mathematical model was constructed based on the experimental data, including a cardiovascular model, gas exchange and the administration of medication. As the base of the controller fuzzy logic was used allowing the easy integration of knowledge from trained perfusionists, an adaptive mechanism was included to adapt to the patient's individual response. Initial simulations show the effectiveness of the controller and the improvements of perfusion after adaptation. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

Adaptive process control using fuzzy logic and genetic algorithms

NASA Technical Reports Server (NTRS)

Karr, C. L.

1993-01-01

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

Adaptive Process Control with Fuzzy Logic and Genetic Algorithms

NASA Technical Reports Server (NTRS)

Karr, C. L.

1993-01-01

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

Adaptive AOA-aided TOA self-positioning for mobile wireless sensor networks.

PubMed

Wen, Chih-Yu; Chan, Fu-Kai

2010-01-01

Location-awareness is crucial and becoming increasingly important to many applications in wireless sensor networks. This paper presents a network-based positioning system and outlines recent work in which we have developed an efficient principled approach to localize a mobile sensor using time of arrival (TOA) and angle of arrival (AOA) information employing multiple seeds in the line-of-sight scenario. By receiving the periodic broadcasts from the seeds, the mobile target sensors can obtain adequate observations and localize themselves automatically. The proposed positioning scheme performs location estimation in three phases: (I) AOA-aided TOA measurement, (II) Geometrical positioning with particle filter, and (III) Adaptive fuzzy control. Based on the distance measurements and the initial position estimate, adaptive fuzzy control scheme is applied to solve the localization adjustment problem. The simulations show that the proposed approach provides adaptive flexibility and robust improvement in position estimation.

Fuzzy Adaptive Control Design and Discretization for a Class of Nonlinear Uncertain Systems.

PubMed

Zhao, Xudong; Shi, Peng; Zheng, Xiaolong

2016-06-01

In this paper, tracking control problems are investigated for a class of uncertain nonlinear systems in lower triangular form. First, a state-feedback controller is designed by using adaptive backstepping technique and the universal approximation ability of fuzzy logic systems. During the design procedure, a developed method with less computation is proposed by constructing one maximum adaptive parameter. Furthermore, adaptive controllers with nonsymmetric dead-zone are also designed for the systems. Then, a sampled-data control scheme is presented to discretize the obtained continuous-time controller by using the forward Euler method. It is shown that both proposed continuous and discrete controllers can ensure that the system output tracks the target signal with a small bounded error and the other closed-loop signals remain bounded. Two simulation examples are presented to verify the effectiveness and applicability of the proposed new design techniques.

Fuzzy regulator design for wind turbine yaw control.

PubMed

Theodoropoulos, Stefanos; Kandris, Dionisis; Samarakou, Maria; Koulouras, Grigorios

2014-01-01

This paper proposes the development of an advanced fuzzy logic controller which aims to perform intelligent automatic control of the yaw movement of wind turbines. The specific fuzzy controller takes into account both the wind velocity and the acceptable yaw error correlation in order to achieve maximum performance efficacy. In this way, the proposed yaw control system is remarkably adaptive to the existing conditions. In this way, the wind turbine is enabled to retain its power output close to its nominal value and at the same time preserve its yaw system from pointless movement. Thorough simulation tests evaluate the proposed system effectiveness.

An algorithm for solving an arbitrary triangular fully fuzzy Sylvester matrix equations

NASA Astrophysics Data System (ADS)

Daud, Wan Suhana Wan; Ahmad, Nazihah; Malkawi, Ghassan

2017-11-01

Sylvester matrix equations played a prominent role in various areas including control theory. Considering to any un-certainty problems that can be occurred at any time, the Sylvester matrix equation has to be adapted to the fuzzy environment. Therefore, in this study, an algorithm for solving an arbitrary triangular fully fuzzy Sylvester matrix equation is constructed. The construction of the algorithm is based on the max-min arithmetic multiplication operation. Besides that, an associated arbitrary matrix equation is modified in obtaining the final solution. Finally, some numerical examples are presented to illustrate the proposed algorithm.

Automated Interpretation of LIBS Spectra using a Fuzzy Logic Inference Engine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jeremy J. Hatch; Timothy R. McJunkin; Cynthia Hanson

2012-02-01

Automated interpretation of laser-induced breakdown spectroscopy (LIBS) data is necessary due to the plethora of spectra that can be acquired in a relatively short time. However, traditional chemometric and artificial neural network methods that have been employed are not always transparent to a skilled user. A fuzzy logic approach to data interpretation has now been adapted to LIBS spectral interpretation. A fuzzy logic inference engine (FLIE) was used to differentiate between various copper containing and stainless steel alloys as well as unknowns. Results using FLIE indicate a high degree of confidence in spectral assignment.

A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing.

PubMed

Ahmad, Muneer; Jung, Low Tan; Bhuiyan, Al-Amin

2017-10-01

Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals. This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise. Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms. This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary to fixed window length conventional filters. Copyright © 2017 Elsevier B.V. All rights reserved.

An experimental methodology for a fuzzy set preference model

NASA Technical Reports Server (NTRS)

Turksen, I. B.; Willson, Ian A.

1992-01-01

A flexible fuzzy set preference model first requires approximate methodologies for implementation. Fuzzy sets must be defined for each individual consumer using computer software, requiring a minimum of time and expertise on the part of the consumer. The amount of information needed in defining sets must also be established. The model itself must adapt fully to the subject's choice of attributes (vague or precise), attribute levels, and importance weights. The resulting individual-level model should be fully adapted to each consumer. The methodologies needed to develop this model will be equally useful in a new generation of intelligent systems which interact with ordinary consumers, controlling electronic devices through fuzzy expert systems or making recommendations based on a variety of inputs. The power of personal computers and their acceptance by consumers has yet to be fully utilized to create interactive knowledge systems that fully adapt their function to the user. Understanding individual consumer preferences is critical to the design of new products and the estimation of demand (market share) for existing products, which in turn is an input to management systems concerned with production and distribution. The question of what to make, for whom to make it and how much to make requires an understanding of the customer's preferences and the trade-offs that exist between alternatives. Conjoint analysis is a widely used methodology which de-composes an overall preference for an object into a combination of preferences for its constituent parts (attributes such as taste and price), which are combined using an appropriate combination function. Preferences are often expressed using linguistic terms which cannot be represented in conjoint models. Current models are also not implemented an individual level, making it difficult to reach meaningful conclusions about the cause of an individual's behavior from an aggregate model. The combination of complex aggregate models and vague linguistic preferences has greatly limited the usefulness and predictive validity of existing preference models. A fuzzy set preference model that uses linguistic variables and a fully interactive implementation should be able to simultaneously address these issues and substantially improve the accuracy of demand estimates. The parallel implementation of crisp and fuzzy conjoint models using identical data not only validates the fuzzy set model but also provides an opportunity to assess the impact of fuzzy set definitions and individual attribute choices implemented in the interactive methodology developed in this research. The generalized experimental tools needed for conjoint models can also be applied to many other types of intelligent systems.

Parameters-tuning of PID controller for automatic voltage regulators using the African buffalo optimization.

PubMed

Odili, Julius Beneoluchi; Mohmad Kahar, Mohd Nizam; Noraziah, A

2017-01-01

In this paper, an attempt is made to apply the African Buffalo Optimization (ABO) to tune the parameters of a PID controller for an effective Automatic Voltage Regulator (AVR). Existing metaheuristic tuning methods have been proven to be quite successful but there were observable areas that need improvements especially in terms of the system's gain overshoot and steady steady state errors. Using the ABO algorithm where each buffalo location in the herd is a candidate solution to the Proportional-Integral-Derivative parameters was very helpful in addressing these two areas of concern. The encouraging results obtained from the simulation of the PID Controller parameters-tuning using the ABO when compared with the performance of Genetic Algorithm PID (GA-PID), Particle-Swarm Optimization PID (PSO-PID), Ant Colony Optimization PID (ACO-PID), PID, Bacteria-Foraging Optimization PID (BFO-PID) etc makes ABO-PID a good addition to solving PID Controller tuning problems using metaheuristics.

Evolving RBF neural networks for adaptive soft-sensor design.

PubMed

Alexandridis, Alex

2013-12-01

This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.

PubMed

Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing

2016-07-26

This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.

Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems

PubMed Central

Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing

2016-01-01

This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches. PMID:27472336

Road sign recognition with fuzzy adaptive pre-processing models.

PubMed

Lin, Chien-Chuan; Wang, Ming-Shi

2012-01-01

A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle's speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance.

Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models

PubMed Central

Lin, Chien-Chuan; Wang, Ming-Shi

2012-01-01

A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle's speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance. PMID:22778650

An ANFIS-based on B2C electronic commerce transaction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, Juan, E-mail: linjuanliucaihong@qq.com; Liu, Chenlian, E-mail: chenglian.liu@gmail.com; Guo, Yongning, E-mail: guoyn@163.com

2014-10-06

The purpose of this study is to use an adaptive-network-based fuzzy inference system to model a fuzzy logic-based system (FIS) for supporting decision-making process in B2C electronic commerce transaction. Firstly we introduce FIS in B2C electronic commerce transaction and ANFIS. Then we use ANFIS to model FIS with different membership functions(MF). Lastly we give a conclusion.

An ANFIS-based on B2C electronic commerce transaction

NASA Astrophysics Data System (ADS)

Lin, Juan; Liu, Chenlian; Guo, Yongning

2014-10-01

The purpose of this study is to use an adaptive-network-based fuzzy inference system to model a fuzzy logic-based system (FIS) for supporting decision-making process in B2C electronic commerce transaction. Firstly we introduce FIS in B2C electronic commerce transaction and ANFIS. Then we use ANFIS to model FIS with different membership functions(MF). Lastly we give a conclusion.

Shear wave prediction using committee fuzzy model constrained by lithofacies, Zagros basin, SW Iran

NASA Astrophysics Data System (ADS)

Shiroodi, Sadjad Kazem; Ghafoori, Mohammad; Ansari, Hamid Reza; Lashkaripour, Golamreza; Ghanadian, Mostafa

2017-02-01

The main purpose of this study is to introduce the geological controlling factors in improving an intelligence-based model to estimate shear wave velocity from seismic attributes. The proposed method includes three main steps in the framework of geological events in a complex sedimentary succession located in the Persian Gulf. First, the best attributes were selected from extracted seismic data. Second, these attributes were transformed into shear wave velocity using fuzzy inference systems (FIS) such as Sugeno's fuzzy inference (SFIS), adaptive neuro-fuzzy inference (ANFIS) and optimized fuzzy inference (OFIS). Finally, a committee fuzzy machine (CFM) based on bat-inspired algorithm (BA) optimization was applied to combine previous predictions into an enhanced solution. In order to show the geological effect on improving the prediction, the main classes of predominate lithofacies in the reservoir of interest including shale, sand, and carbonate were selected and then the proposed algorithm was performed with and without lithofacies constraint. The results showed a good agreement between real and predicted shear wave velocity in the lithofacies-based model compared to the model without lithofacies especially in sand and carbonate.

Experimental Verification of Electric Drive Technologies Based on Artificial Intelligence Tools

NASA Technical Reports Server (NTRS)

Rubaai, Ahmed; Ricketts, Daniel; Kotaru, Raj; Thomas, Robert; Noga, Donald F. (Technical Monitor); Kankam, Mark D. (Technical Monitor)

2000-01-01

In this report, a fully integrated prototype of a flight servo control system is successfully developed and implemented using brushless dc motors. The control system is developed by the fuzzy logic theory, and implemented with a multilayer neural network. First, a neural network-based architecture is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. The network structure and the parameter learning are performed simultaneously and online in the fuzzy-neural network system. The structure learning is based on the partition of input space. The parameter learning is based on the supervised gradient decent method, using a delta adaptation law. Using experimental setup, the performance of the proposed control system is evaluated under various operating conditions. Test results are presented and discussed in the report. The proposed learning control system has several advantages, namely, simple structure and learning capability, robustness and high tracking performance and few nodes at hidden layers. In comparison with the PI controller, the proposed fuzzy-neural network system can yield a better dynamic performance with shorter settling time, and without overshoot. Experimental results have shown that the proposed control system is adaptive and robust in responding to a wide range of operating conditions. In summary, the goal of this study is to design and implement-advanced servosystems to actuate control surfaces for flight vehicles, namely, aircraft and helicopters, missiles and interceptors, and mini- and micro-air vehicles.

Adaptive Critic-based Neurofuzzy Controller for the Steam Generator Water Level

NASA Astrophysics Data System (ADS)

Fakhrazari, Amin; Boroushaki, Mehrdad

2008-06-01

In this paper, an adaptive critic-based neurofuzzy controller is presented for water level regulation of nuclear steam generators. The problem has been of great concern for many years as the steam generator is a highly nonlinear system showing inverse response dynamics especially at low operating power levels. Fuzzy critic-based learning is a reinforcement learning method based on dynamic programming. The only information available for the critic agent is the system feedback which is interpreted as the last action the controller has performed in the previous state. The signal produced by the critic agent is used alongside the backpropagation of error algorithm to tune online conclusion parts of the fuzzy inference rules. The critic agent here has a proportional-derivative structure and the fuzzy rule base has nine rules. The proposed controller shows satisfactory transient responses, disturbance rejection and robustness to model uncertainty. Its simple design procedure and structure, nominates it as one of the suitable controller designs for the steam generator water level control in nuclear power plant industry.

Processing of ICARTT Data Files Using Fuzzy Matching and Parser Combinators

NASA Technical Reports Server (NTRS)

Rutherford, Matthew T.; Typanski, Nathan D.; Wang, Dali; Chen, Gao

2014-01-01

In this paper, the task of parsing and matching inconsistent, poorly formed text data through the use of parser combinators and fuzzy matching is discussed. An object-oriented implementation of the parser combinator technique is used to allow for a relatively simple interface for adapting base parsers. For matching tasks, a fuzzy matching algorithm with Levenshtein distance calculations is implemented to match string pair, which are otherwise difficult to match due to the aforementioned irregularities and errors in one or both pair members. Used in concert, the two techniques allow parsing and matching operations to be performed which had previously only been done manually.

Computerized decision support system for mass identification in breast using digital mammogram: a study on GA-based neuro-fuzzy approaches.

PubMed

Das, Arpita; Bhattacharya, Mahua

2011-01-01

In the present work, authors have developed a treatment planning system implementing genetic based neuro-fuzzy approaches for accurate analysis of shape and margin of tumor masses appearing in breast using digital mammogram. It is obvious that a complicated structure invites the problem of over learning and misclassification. In proposed methodology, genetic algorithm (GA) has been used for searching of effective input feature vectors combined with adaptive neuro-fuzzy model for final classification of different boundaries of tumor masses. The study involves 200 digitized mammograms from MIAS and other databases and has shown 86% correct classification rate.

Fuzzy Regulator Design for Wind Turbine Yaw Control

PubMed Central

Koulouras, Grigorios

2014-01-01

This paper proposes the development of an advanced fuzzy logic controller which aims to perform intelligent automatic control of the yaw movement of wind turbines. The specific fuzzy controller takes into account both the wind velocity and the acceptable yaw error correlation in order to achieve maximum performance efficacy. In this way, the proposed yaw control system is remarkably adaptive to the existing conditions. In this way, the wind turbine is enabled to retain its power output close to its nominal value and at the same time preserve its yaw system from pointless movement. Thorough simulation tests evaluate the proposed system effectiveness. PMID:24693237

Application of Fuzzy Analytic Hierarchy Process to Building Research Teams

NASA Astrophysics Data System (ADS)

Dąbrowski, Karol; Skrzypek, Katarzyna

2016-03-01

Building teams has a fundamental impact for execution of research and development projects. The teams appointed for the needs of given projects are based on individuals from both inside and outside of the organization. Knowledge is not only a product available on the market but also an intangible resource affecting their internal and external processes. Thus it is vitally important for businesses and scientific research facilities to effectively manage knowledge within project teams. The article presents a proposal to use Fuzzy AHP (Analytic Hierarchy Process) and ANFIS (Adaptive Neuro Fuzzy Inference System) methods in working groups building for R&D projects on the basis of employees skills.

Improvement of the F-Perceptory Approach Through Management of Fuzzy Complex Geographic Objects

NASA Astrophysics Data System (ADS)

Khalfi, B.; de Runz, C.; Faiz, S.; Akdag, H.

2015-08-01

In the real world, data is imperfect and in various ways such as imprecision, vagueness, uncertainty, ambiguity and inconsistency. For geographic data, the fuzzy aspect is mainly manifested in time, space and the function of objects and is due to a lack of precision. Therefore, the researchers in the domain emphasize the importance of modeling data structures in GIS but also their lack of adaptation to fuzzy data. The F-Perceptory approachh manages the modeling of imperfect geographic information with UML. This management is essential to maintain faithfulness to reality and to better guide the user in his decision-making. However, this approach does not manage fuzzy complex geographic objects. The latter presents a multiple object with similar or different geographic shapes. So, in this paper, we propose to improve the F-Perceptory approach by proposing to handle fuzzy complex geographic objects modeling. In a second step, we propose its transformation to the UML modeling.

Deduction of reservoir operating rules for application in global hydrological models

NASA Astrophysics Data System (ADS)

Coerver, Hubertus M.; Rutten, Martine M.; van de Giesen, Nick C.

2018-01-01

A big challenge in constructing global hydrological models is the inclusion of anthropogenic impacts on the water cycle, such as caused by dams. Dam operators make decisions based on experience and often uncertain information. In this study information generally available to dam operators, like inflow into the reservoir and storage levels, was used to derive fuzzy rules describing the way a reservoir is operated. Using an artificial neural network capable of mimicking fuzzy logic, called the ANFIS adaptive-network-based fuzzy inference system, fuzzy rules linking inflow and storage with reservoir release were determined for 11 reservoirs in central Asia, the US and Vietnam. By varying the input variables of the neural network, different configurations of fuzzy rules were created and tested. It was found that the release from relatively large reservoirs was significantly dependent on information concerning recent storage levels, while release from smaller reservoirs was more dependent on reservoir inflows. Subsequently, the derived rules were used to simulate reservoir release with an average Nash-Sutcliffe coefficient of 0.81.

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Using fuzzy logic to integrate neural networks and knowledge-based systems

NASA Technical Reports Server (NTRS)

Yen, John

1991-01-01

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

Nonlinear dynamic analysis and robust controller design for Francis hydraulic turbine regulating system with a straight-tube surge tank

NASA Astrophysics Data System (ADS)

Liang, Ji; Yuan, Xiaohui; Yuan, Yanbin; Chen, Zhihuan; Li, Yuanzheng

2017-02-01

The safety and stability of hydraulic turbine regulating system (HTRS) in hydropower plants become increasingly important since the rapid development and the broad application of hydro energy technology. In this paper, a novel mathematical model of Francis hydraulic turbine regulating system with a straight-tube surge tank based on a few state-space equations is introduced to study the dynamic behaviors of the HTRS system, where the existence of possible unstable oscillations of this model is studied extensively and presented in the forms of the bifurcation diagram, time waveform plot, phase trajectories, and power spectrum. To eliminate these undesirable behaviors, a specified fuzzy sliding mode controller is designed. In this hybrid controller, the sliding mode control law makes full use of the proposed model to guarantee the robust control in the presence of system uncertainties, while the fuzzy system is applied to approximate the proper gains of the switching control in sliding mode technique to reduce the chattering effect, and particle swarm optimization is developed to search the optimal gains of the controller. Numerical simulations are presented to verify the effectiveness of the designed controller, and the results show that the performances of the nonlinear HTRS system assisted with the proposed controller is much better than that with the commonly used optimal PID controller.

Distribution of Intelligence in Airborne Air-Defense Mission Systems

DTIC Science & Technology

2001-03-01

their ,,creator" has given them a structure - not only a program - allowing them to organize themselves, to learn and to adapt themselves to changing...self- organization capability. They are modelled on the structures of the unconscious mind. "• By contrast, fuzzy logic/fuzzy control has developed an...of these techniques as indicated in Fig. 3 is of particular importance for achieving unprecedented levels of self- organization capability and

A Distributed Fuzzy Associative Classifier for Big Data.

PubMed

Segatori, Armando; Bechini, Alessio; Ducange, Pietro; Marcelloni, Francesco

2017-09-19

Fuzzy associative classification has not been widely analyzed in the literature, although associative classifiers (ACs) have proved to be very effective in different real domain applications. The main reason is that learning fuzzy ACs is a very heavy task, especially when dealing with large datasets. To overcome this drawback, in this paper, we propose an efficient distributed fuzzy associative classification approach based on the MapReduce paradigm. The approach exploits a novel distributed discretizer based on fuzzy entropy for efficiently generating fuzzy partitions of the attributes. Then, a set of candidate fuzzy association rules is generated by employing a distributed fuzzy extension of the well-known FP-Growth algorithm. Finally, this set is pruned by using three purposely adapted types of pruning. We implemented our approach on the popular Hadoop framework. Hadoop allows distributing storage and processing of very large data sets on computer clusters built from commodity hardware. We have performed an extensive experimentation and a detailed analysis of the results using six very large datasets with up to 11,000,000 instances. We have also experimented different types of reasoning methods. Focusing on accuracy, model complexity, computation time, and scalability, we compare the results achieved by our approach with those obtained by two distributed nonfuzzy ACs recently proposed in the literature. We highlight that, although the accuracies result to be comparable, the complexity, evaluated in terms of number of rules, of the classifiers generated by the fuzzy distributed approach is lower than the one of the nonfuzzy classifiers.

Design and Implementation of a Hypothermic Machine Perfusion Device for Clinical Preservation of Isolated Organs

PubMed Central

Shen, Fei; Yan, Ruqiang

2017-01-01

The imbalance between limited organ supply and huge potential need has hindered the development of organ-graft techniques. In this paper a low-cost hypothermic machine perfusion (HMP) device is designed and implemented to maintain suitable preservation surroundings and extend the survival life of isolated organs. Four necessary elements (the machine perfusion, the physiological parameter monitoring, the thermostatic control and the oxygenation apparatus) involved in this HMP device are introduced. Especially during the thermostatic control process, a modified Bayes estimation, which introduces the concept of improvement factor, is realized to recognize and reduce the possible measurement errors resulting from sensor faults and noise interference. Also, a fuzzy-PID controller contributes to improve the accuracy and reduces the computational load using the DSP. Our experiments indicate that the reliability of the instrument meets the design requirements, thus being appealing for potential clinical preservation applications. PMID:28587173

Neurocontrol and fuzzy logic: Connections and designs

NASA Technical Reports Server (NTRS)

Werbos, Paul J.

1991-01-01

Artificial neural networks (ANNs) and fuzzy logic are complementary technologies. ANNs extract information from systems to be learned or controlled, while fuzzy techniques mainly use verbal information from experts. Ideally, both sources of information should be combined. For example, one can learn rules in a hybrid fashion, and then calibrate them for better whole-system performance. ANNs offer universal approximation theorems, pedagogical advantages, very high-throughput hardware, and links to neurophysiology. Neurocontrol - the use of ANNs to directly control motors or actuators, etc. - uses five generalized designs, related to control theory, which can work on fuzzy logic systems as well as ANNs. These designs can copy what experts do instead of what they say, learn to track trajectories, generalize adaptive control, and maximize performance or minimize cost over time, even in noisy environments. Design tradeoffs and future directions are discussed throughout.

Fuzzy Logic Path Planning System for Collision Avoidance by an Autonomous Rover Vehicle

NASA Technical Reports Server (NTRS)

Murphy, Michael G.

1991-01-01

Systems already developed at JSC have shown the benefits of applying fuzzy logic control theory to space related operations. Four major issues are addressed that are associated with developing an autonomous collision avoidance subsystem within a path planning system designed for application in a remote, hostile environment that does not lend itself well to remote manipulation of the vehicle involved through Earth-based telecommunication. A good focus for this is unmanned exploration of the surface of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. The four major issues addressed are: (1) avoidance of a single fuzzy moving obstacle; (2) back off from a dead end in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system.

Exploiting expert systems in cardiology: a comparative study.

PubMed

Economou, George-Peter K; Sourla, Efrosini; Stamatopoulou, Konstantina-Maria; Syrimpeis, Vasileios; Sioutas, Spyros; Tsakalidis, Athanasios; Tzimas, Giannis

2015-01-01

An improved Adaptive Neuro-Fuzzy Inference System (ANFIS) in the field of critical cardiovascular diseases is presented. The system stems from an earlier application based only on a Sugeno-type Fuzzy Expert System (FES) with the addition of an Artificial Neural Network (ANN) computational structure. Thus, inherent characteristics of ANNs, along with the human-like knowledge representation of fuzzy systems are integrated. The ANFIS has been utilized into building five different sub-systems, distinctly covering Coronary Disease, Hypertension, Atrial Fibrillation, Heart Failure, and Diabetes, hence aiding doctors of medicine (MDs), guide trainees, and encourage medical experts in their diagnoses centering a wide range of Cardiology. The Fuzzy Rules have been trimmed down and the ANNs have been optimized in order to focus into each particular disease and produce results ready-to-be applied to real-world patients.

Evaluation Framework Based on Fuzzy Measured Method in Adaptive Learning Systems

ERIC Educational Resources Information Center

Ounaies, Houda Zouari; Jamoussi, Yassine; Ben Ghezala, Henda Hajjami

2008-01-01

Currently, e-learning systems are mainly web-based applications and tackle a wide range of users all over the world. Fitting learners' needs is considered as a key issue to guaranty the success of these systems. Many researches work on providing adaptive systems. Nevertheless, evaluation of the adaptivity is still in an exploratory phase.…

Differential flatness properties and adaptive control of the hypothalamic-pituitary-adrenal axis model

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos

2016-12-01

It is shown that the model of the hypothalamic-pituitary-adrenal gland axis is a differentially flat one and this permits to transform it to the so-called linear canonical form. For the new description of the system's dynamics the transformed control inputs contain unknown terms which depend on the system's parameters. To identify these terms an adaptive fuzzy approximator is used in the control loop. Thus an adaptive fuzzy control scheme is implemented in which the unknown or unmodeled system dynamics is approximated by neurofuzzy networks and next this information is used by a feedback controller that makes the state variables (CRH - corticotropin releasing hormone, adenocortocotropic hormone - ACTH, cortisol) of the hypothalamic-pituitary-adrenal gland axis model converge to the desirable levels (setpoints). This adaptive control scheme is exclusively implemented with the use of output feedback, while the state vector elements which are not directly measured are estimated with the use of a state observer that operates in the control loop. The learning rate of the adaptive fuzzy system is suitably computed from Lyapunov analysis, so as to assure that both the learning procedure for the unknown system's parameters, the dynamics of the observer and the dynamics of the control loop will remain stable. The performed Lyapunov stability analysis depends on two Riccati equations, one associated with the feedback controller and one associated with the state observer. Finally, it is proven that for the control scheme that comprises the feedback controller, the state observer and the neurofuzzy approximator, an H-infinity tracking performance can be succeeded.

Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement.

PubMed

Chiang, Shu-Yin; Kan, Yao-Chiang; Chen, Yun-Shan; Tu, Ying-Ching; Lin, Hsueh-Chun

2016-12-03

Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC.

Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement

PubMed Central

Chiang, Shu-Yin; Kan, Yao-Chiang; Chen, Yun-Shan; Tu, Ying-Ching; Lin, Hsueh-Chun

2016-01-01

Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC. PMID:27918482

Modeling of inter-neuronal coupling medium and its impact on neuronal synchronization

PubMed Central

Iqbal, Muhammad; Hong, Keum-Shik

2017-01-01

In this paper, modeling of the coupling medium between two neurons, the effects of the model parameters on the synchronization of those neurons, and compensation of coupling strength deficiency in synchronization are studied. Our study exploits the inter-neuronal coupling medium and investigates its intrinsic properties in order to get insight into neuronal-information transmittance and, there from, brain-information processing. A novel electrical model of the coupling medium that represents a well-known RLC circuit attributable to the coupling medium’s intrinsic resistive, inductive, and capacitive properties is derived. Surprisingly, the integration of such properties reveals the existence of a natural three-term control strategy, referred to in the literature as the proportional integral derivative (PID) controller, which can be responsible for synchronization between two neurons. Consequently, brain-information processing can rely on a large number of PID controllers based on the coupling medium properties responsible for the coherent behavior of neurons in a neural network. Herein, the effects of the coupling model (or natural PID controller) parameters are studied and, further, a supervisory mechanism is proposed that follows a learning and adaptation policy based on the particle swarm optimization algorithm for compensation of the coupling strength deficiency. PMID:28486505

A Hybrid Stochastic-Neuro-Fuzzy Model-Based System for In-Flight Gas Turbine Engine Diagnostics

DTIC Science & Technology

2001-04-05

Margin (ADM) and (ii) Fault Detection Margin (FDM). Key Words: ANFIS, Engine Health Monitoring , Gas Path Analysis, and Stochastic Analysis Adaptive Network...The paper illustrates the application of a hybrid Stochastic- Fuzzy -Inference Model-Based System (StoFIS) to fault diagnostics and prognostics for both...operational history monitored on-line by the engine health management (EHM) system. To capture the complex functional relationships between different

Improvement of cardiac function with device-based diaphragmatic stimulation in chronic heart failure patients: the randomized, open-label, crossover Epiphrenic II Pilot Trial.

PubMed

Beeler, Remo; Schoenenberger, Andreas W; Bauer, Peter; Kobza, Richard; Bergner, Michael; Mueller, Xavier; Schlaepfer, Reinhard; Zuber, Michel; Erne, Susanne; Erne, Paul

2014-03-01

Device-based pacing-induced diaphragmatic stimulation (PIDS) may have therapeutic potential for chronic heart failure (HF) patients. We studied the effects of PIDS on cardiac function and functional outcomes. In 24 chronic HF patients with CRT, an additional electrode was attached to the left diaphragm. Randomized into two groups, patients received the following PIDS modes for 3 weeks in a different sequence: (i) PIDS off (control group); (ii) PIDS 0 ms mode (PIDS simultaneously with ventricular CRT pulse); or (iii) PIDS optimized mode (PIDS with optimized delay to ventricular CRT pulse). For PIDS optimization, acoustic cardiography was used. Effects of each PIDS mode on dyspnoea, power during exercise testing, and LVEF were assessed. Dyspnoea improved with the PIDS 0 ms mode (P = 0.057) and the PIDS optimized mode (P = 0.034) as compared with the control group. Maximal power increased from median 100.5 W in the control group to 104.0 W in the PIDS 0 ms mode (P = 0.092) and 109.5 W in the PIDS optimized mode (P = 0.022). Median LVEF was 33.5% in the control group, 33.0% in the PIDS 0 ms mode, and 37.0% in the PIDS optimized mode (P = 0.763 and P = 0.009 as compared with the control group, respectively). PIDS was asymptomatic in all patients. PIDS improves dyspnoea, working capacity, and LVEF in chronic HF patients over a 3 week period in addition to CRT. This pilot study demonstrates proof of principle of an innovative technology which should be confirmed in a larger sample. NCT00769678. © 2013 The Authors. European Journal of Heart Failure © 2013 European Society of Cardiology.

Indirect adaptive soft computing based wavelet-embedded control paradigms for WT/PV/SOFC in a grid/charging station connected hybrid power system.

PubMed

Mumtaz, Sidra; Khan, Laiq; Ahmed, Saghir; Bader, Rabiah

2017-01-01

This paper focuses on the indirect adaptive tracking control of renewable energy sources in a grid-connected hybrid power system. The renewable energy systems have low efficiency and intermittent nature due to unpredictable meteorological conditions. The domestic load and the conventional charging stations behave in an uncertain manner. To operate the renewable energy sources efficiently for harvesting maximum power, instantaneous nonlinear dynamics should be captured online. A Chebyshev-wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control paradigm is proposed for variable speed wind turbine-permanent synchronous generator (VSWT-PMSG). A Hermite-wavelet incorporated NeuroFuzzy indirect adaptive MPPT control strategy for photovoltaic (PV) system to extract maximum power and indirect adaptive tracking control scheme for Solid Oxide Fuel Cell (SOFC) is developed. A comprehensive simulation test-bed for a grid-connected hybrid power system is developed in Matlab/Simulink. The robustness of the suggested indirect adaptive control paradigms are evaluated through simulation results in a grid-connected hybrid power system test-bed by comparison with conventional and intelligent control techniques. The simulation results validate the effectiveness of the proposed control paradigms.

Indirect adaptive soft computing based wavelet-embedded control paradigms for WT/PV/SOFC in a grid/charging station connected hybrid power system

PubMed Central

Khan, Laiq; Ahmed, Saghir; Bader, Rabiah

2017-01-01

This paper focuses on the indirect adaptive tracking control of renewable energy sources in a grid-connected hybrid power system. The renewable energy systems have low efficiency and intermittent nature due to unpredictable meteorological conditions. The domestic load and the conventional charging stations behave in an uncertain manner. To operate the renewable energy sources efficiently for harvesting maximum power, instantaneous nonlinear dynamics should be captured online. A Chebyshev-wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control paradigm is proposed for variable speed wind turbine-permanent synchronous generator (VSWT-PMSG). A Hermite-wavelet incorporated NeuroFuzzy indirect adaptive MPPT control strategy for photovoltaic (PV) system to extract maximum power and indirect adaptive tracking control scheme for Solid Oxide Fuel Cell (SOFC) is developed. A comprehensive simulation test-bed for a grid-connected hybrid power system is developed in Matlab/Simulink. The robustness of the suggested indirect adaptive control paradigms are evaluated through simulation results in a grid-connected hybrid power system test-bed by comparison with conventional and intelligent control techniques. The simulation results validate the effectiveness of the proposed control paradigms. PMID:28877191

Detecting Edges in Images by Use of Fuzzy Reasoning

NASA Technical Reports Server (NTRS)

Dominguez, Jesus A.; Klinko, Steve

2003-01-01

A method of processing digital image data to detect edges includes the use of fuzzy reasoning. The method is completely adaptive and does not require any advance knowledge of an image. During initial processing of image data at a low level of abstraction, the nature of the data is indeterminate. Fuzzy reasoning is used in the present method because it affords an ability to construct useful abstractions from approximate, incomplete, and otherwise imperfect sets of data. Humans are able to make some sense of even unfamiliar objects that have imperfect high-level representations. It appears that to perceive unfamiliar objects or to perceive familiar objects in imperfect images, humans apply heuristic algorithms to understand the images

Using Evolved Fuzzy Neural Networks for Injury Detection from Isokinetic Curves

NASA Astrophysics Data System (ADS)

Couchet, Jorge; Font, José María; Manrique, Daniel

In this paper we propose an evolutionary fuzzy neural networks system for extracting knowledge from a set of time series containing medical information. The series represent isokinetic curves obtained from a group of patients exercising the knee joint on an isokinetic dynamometer. The system has two parts: i) it analyses the time series input in order generate a simplified model of an isokinetic curve; ii) it applies a grammar-guided genetic program to obtain a knowledge base represented by a fuzzy neural network. Once the knowledge base has been generated, the system is able to perform knee injuries detection. The results suggest that evolved fuzzy neural networks perform better than non-evolutionary approaches and have a high accuracy rate during both the training and testing phases. Additionally, they are robust, as the system is able to self-adapt to changes in the problem without human intervention.

Neuro-fuzzy model for estimating race and gender from geometric distances of human face across pose

NASA Astrophysics Data System (ADS)

Nanaa, K.; Rahman, M. N. A.; Rizon, M.; Mohamad, F. S.; Mamat, M.

2018-03-01

Classifying human face based on race and gender is a vital process in face recognition. It contributes to an index database and eases 3D synthesis of the human face. Identifying race and gender based on intrinsic factor is problematic, which is more fitting to utilizing nonlinear model for estimating process. In this paper, we aim to estimate race and gender in varied head pose. For this purpose, we collect dataset from PICS and CAS-PEAL databases, detect the landmarks and rotate them to the frontal pose. After geometric distances are calculated, all of distance values will be normalized. Implementation is carried out by using Neural Network Model and Fuzzy Logic Model. These models are combined by using Adaptive Neuro-Fuzzy Model. The experimental results showed that the optimization of address fuzzy membership. Model gives a better assessment rate and found that estimating race contributing to a more accurate gender assessment.

Direct and indirect effects of screening for Chlamydia trachomatis on the prevention of pelvic inflammatory disease: a mathematical modeling study.

PubMed

Herzog, Sereina A; Heijne, Janneke C M; Scott, Pippa; Althaus, Christian L; Low, Nicola

2013-11-01

Pelvic inflammatory disease (PID) results from the ascending spread of microorganisms, including Chlamydia trachomatis, to the upper genital tract. Screening could improve outcomes by identifying and treating chlamydial infections before they progress to PID (direct effect) or by reducing chlamydia transmission (indirect effect). We developed a compartmental model that represents a hypothetical heterosexual population and explicitly incorporates progression from chlamydia to clinical PID. Chlamydia screening was introduced, with coverage increasing each year for 10 years. We estimated the separate contributions of the direct and indirect effects of screening on PID cases prevented per 100,000 women. We explored the influence of varying the time point at which clinical PID could occur and of increasing the risk of PID after repeated chlamydial infections. The probability of PID at baseline was 3.1% by age 25 years. After 5 years, the intervention scenario had prevented 187 PID cases per 100,000 women and after 10 years 956 PID cases per 100,000 women. At the start of screening, most PID cases were prevented by the direct effect. The indirect effect produced a small net increase in PID cases, which was outweighed by the effect of reduced chlamydia transmission after 2.2 years. The later that progression to PID occurs, the greater the contribution of the direct effect. Increasing the risk of PID with repeated chlamydial infection increases the number of PID cases prevented by screening. This study shows the separate roles of direct and indirect PID prevention and potential harms, which cannot be demonstrated in observational studies.

Effects of phase vector and history extension on prediction power of adaptive-network based fuzzy inference system (ANFIS) model for a real scale anaerobic wastewater treatment plant operating under unsteady state.

PubMed

Perendeci, Altinay; Arslan, Sever; Tanyolaç, Abdurrahman; Celebi, Serdar S

2009-10-01

A conceptual neural fuzzy model based on adaptive-network based fuzzy inference system, ANFIS, was proposed using available input on-line and off-line operational variables for a sugar factory anaerobic wastewater treatment plant operating under unsteady state to estimate the effluent chemical oxygen demand, COD. The predictive power of the developed model was improved as a new approach by adding the phase vector and the recent values of COD up to 5-10 days, longer than overall retention time of wastewater in the system. History of last 10 days for COD effluent with two-valued phase vector in the input variable matrix including all parameters had more predictive power. History of 7 days with two-valued phase vector in the matrix comprised of only on-line variables yielded fairly well estimations. The developed ANFIS model with phase vector and history extension has been able to adequately represent the behavior of the treatment system.

Prediction of Radical Scavenging Activities of Anthocyanins Applying Adaptive Neuro-Fuzzy Inference System (ANFIS) with Quantum Chemical Descriptors

PubMed Central

Jhin, Changho; Hwang, Keum Taek

2014-01-01

Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS) is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR) models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A) and electronegativity (χ) of flavylium cation, and ionization potential (I) of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively. PMID:25153627

Fetal ECG extraction via Type-2 adaptive neuro-fuzzy inference systems.

PubMed

Ahmadieh, Hajar; Asl, Babak Mohammadzadeh

2017-04-01

We proposed a noninvasive method for separating the fetal ECG (FECG) from maternal ECG (MECG) by using Type-2 adaptive neuro-fuzzy inference systems. The method can extract FECG components from abdominal signal by using one abdominal channel, including maternal and fetal cardiac signals and other environmental noise signals, and one chest channel. The proposed algorithm detects the nonlinear dynamics of the mother's body. So, the components of the MECG are estimated from the abdominal signal. By subtracting estimated mother cardiac signal from abdominal signal, fetal cardiac signal can be extracted. This algorithm was applied on synthetic ECG signals generated based on the models developed by McSharry et al. and Behar et al. and also on DaISy real database. In environments with high uncertainty, our method performs better than the Type-1 fuzzy method. Specifically, in evaluation of the algorithm with the synthetic data based on McSharry model, for input signals with SNR of -5dB, the SNR of the extracted FECG was improved by 38.38% in comparison with the Type-1 fuzzy method. Also, the results show that increasing the uncertainty or decreasing the input SNR leads to increasing the percentage of the improvement in SNR of the extracted FECG. For instance, when the SNR of the input signal decreases to -30dB, our proposed algorithm improves the SNR of the extracted FECG by 71.06% with respect to the Type-1 fuzzy method. The same results were obtained on synthetic data based on Behar model. Our results on real database reflect the success of the proposed method to separate the maternal and fetal heart signals even if their waves overlap in time. Moreover, the proposed algorithm was applied to the simulated fetal ECG with ectopic beats and achieved good results in separating FECG from MECG. The results show the superiority of the proposed Type-2 neuro-fuzzy inference method over the Type-1 neuro-fuzzy inference and the polynomial networks methods, which is due to its capability to capture the nonlinearities of the model better. Copyright © 2017 Elsevier B.V. All rights reserved.

The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic.

PubMed

Li, Ning; Martínez, José-Fernán; Hernández Díaz, Vicente

2015-08-10

Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters' dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively.

The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic

PubMed Central

Li, Ning; Martínez, José-Fernán; Díaz, Vicente Hernández

2015-01-01

Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters’ dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively. PMID:26266412

Modeling Belt-Servomechanism by Chebyshev Functional Recurrent Neuro-Fuzzy Network

NASA Astrophysics Data System (ADS)

Huang, Yuan-Ruey; Kang, Yuan; Chu, Ming-Hui; Chang, Yeon-Pun

A novel Chebyshev functional recurrent neuro-fuzzy (CFRNF) network is developed from a combination of the Takagi-Sugeno-Kang (TSK) fuzzy model and the Chebyshev recurrent neural network (CRNN). The CFRNF network can emulate the nonlinear dynamics of a servomechanism system. The system nonlinearity is addressed by enhancing the input dimensions of the consequent parts in the fuzzy rules due to functional expansion of a Chebyshev polynomial. The back propagation algorithm is used to adjust the parameters of the antecedent membership functions as well as those of consequent functions. To verify the performance of the proposed CFRNF, the experiment of the belt servomechanism is presented in this paper. Both of identification methods of adaptive neural fuzzy inference system (ANFIS) and recurrent neural network (RNN) are also studied for modeling of the belt servomechanism. The analysis and comparison results indicate that CFRNF makes identification of complex nonlinear dynamic systems easier. It is verified that the accuracy and convergence of the CFRNF are superior to those of ANFIS and RNN by the identification results of a belt servomechanism.

Fuzzy logic control system to provide autonomous collision avoidance for Mars rover vehicle

NASA Technical Reports Server (NTRS)

Murphy, Michael G.

1990-01-01

NASA is currently involved with planning unmanned missions to Mars to investigate the terrain and process soil samples in advance of a manned mission. A key issue involved in unmanned surface exploration on Mars is that of supporting autonomous maneuvering since radio communication involves lengthy delays. It is anticipated that specific target locations will be designated for sample gathering. In maneuvering autonomously from a starting position to a target position, the rover will need to avoid a variety of obstacles such as boulders or troughs that may block the shortest path to the target. The physical integrity of the rover needs to be maintained while minimizing the time and distance required to attain the target position. Fuzzy logic lends itself well to building reliable control systems that function in the presence of uncertainty or ambiguity. The following major issues are discussed: (1) the nature of fuzzy logic control systems and software tools to implement them; (2) collision avoidance in the presence of fuzzy parameters; and (3) techniques for adaptation in fuzzy logic control systems.

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Assessing urban adaptive capacity to climate change.

PubMed

Araya-Muñoz, Dahyann; Metzger, Marc J; Stuart, Neil; Wilson, A Meriwether W; Alvarez, Luis

2016-12-01

Despite the growing number of studies focusing on urban vulnerability to climate change, adaptive capacity, which is a key component of the IPCC definition of vulnerability, is rarely assessed quantitatively. We examine the capacity of adaptation in the Concepción Metropolitan Area, Chile. A flexible methodology based on spatial fuzzy modelling was developed to standardise and aggregate, through a stepwise approach, seventeen indicators derived from widely available census statistical data into an adaptive capacity index. The results indicate that all the municipalities in the CMA increased their level of adaptive capacity between 1992 and 2002. However, the relative differences between municipalities did not change significantly over the studied timeframe. Fuzzy overlay allowed us to standardise and to effectively aggregate indicators with differing ranges and granularities of attribute values into an overall index. It also provided a conceptually sound and reproducible means of exploring the interplay of many indicators that individually influence adaptive capacity. Furthermore, it captured the complex, aggregated and continued nature of the adaptive capacity, favouring to deal with gaps of data and knowledge associated with the concept of adaptive capacity. The resulting maps can help identify municipalities where adaptive capacity is weak and identify which components of adaptive capacity need strengthening. Identification of these capacity conditions can stimulate dialogue amongst policymakers and stakeholders regarding how to manage urban areas and how to prioritise resources for urban development in ways that can also improve adaptive capacity and thus reduce vulnerability to climate change. Copyright © 2016 Elsevier Ltd. All rights reserved.

A robust adaptive load frequency control for micro-grids.

PubMed

Khooban, Mohammad-Hassan; Niknam, Taher; Blaabjerg, Frede; Davari, Pooya; Dragicevic, Tomislav

2016-11-01

The goal of this study is to introduce a novel robust load frequency control (LFC) strategy for micro-grid(s) (MG(s)) in islanded mode operation. Admittedly, power generators in MG(s) cannot supply steady electric power output and sometimes cause unbalance between supply and demand. Battery energy storage system (BESS) is one of the effective solutions to these problems. Due to the high cost of the BESS, a new idea of Vehicle-to-Grid (V2G) is that a battery of Electric-Vehicle (EV) can be applied as a tantamount large-scale BESS in MG(s). As a result, a new robust control strategy for an islanded micro-grid (MG) is introduced that can consider electric vehicles׳ (EV(s)) effect. Moreover, in this paper, a new combination of the General Type II Fuzzy Logic Sets (GT2FLS) and the Modified Harmony Search Algorithm (MHSA) technique is applied for adaptive tuning of proportional-integral (PI) controller. Implementing General Type II Fuzzy Systems is computationally expensive. However, using a recently introduced α-plane representation, GT2FLS can be seen as a composition of several Interval Type II Fuzzy Logic Systems (IT2FLS) with a corresponding level of α for each. Real-data from an offshore wind farm in Sweden and solar radiation data in Aberdeen (United Kingdom) was used in order to examine the performance of the proposed novel controller. A comparison is made between the achieved results of Optimal Fuzzy-PI (OFPI) controller and those of Optimal Interval Type II Fuzzy-PI (IT2FPI) controller, which are of most recent advances in the area at hand. The Simulation results prove the successfulness and effectiveness of the proposed controller. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Akhavan, P.; Karimi, M.; Pahlavani, P.

2014-10-01

Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

Under-reporting of pelvic inflammatory disease in Hawaii: a comparison of state surveillance and hospitalization data.

PubMed

Pacheco, Misty; Sentell, Tetine; Katz, Alan R

2014-04-01

Hawaii is one of only 19 states for which pelvic inflammatory disease (PID) is a mandated notifiable disease. In order to assess the completeness of PID reporting, we compared the number of hospitalized PID cases in the state of Hawaii with the total number of PID cases reported to the Hawaii State Department of Health surveillance system from 2007 through 2010. While 828 unique PID cases were diagnosed in Hawaii hospitals, only 240 unique PID cases were reported through the state's surveillance system. Severe PID underreporting was seen despite mandatory reporting laws.

Clinical features that identify children with primary immunodeficiency diseases.

PubMed

Subbarayan, Anbezhil; Colarusso, Gloria; Hughes, Stephen M; Gennery, Andrew R; Slatter, Mary; Cant, Andrew J; Arkwright, Peter D

2011-05-01

The 10 warning signs of primary immunodeficiency diseases (PID) have been promoted by various organizations in Europe and the United States to predict PID. However, the ability of these warning signs to identify children with PID has not been rigorously tested. The main goal of this study was to determine the effectiveness of these 10 warning signs in predicting defined PID among children who presented to 2 tertiary pediatric immunodeficiency centers in the north of England. A retrospective survey of 563 children who presented to 2 pediatric immunodeficiency centers was undertaken. The clinical records of 430 patients with a defined PID and 133 patients for whom detailed investigations failed to establish a specific PID were reviewed. Overall, 96% of the children with PID were referred by hospital clinicians. The strongest identifiers of PID were a family history of immunodeficiency disease in addition to use of intravenous antibiotics for sepsis in children with neutrophil PID and failure to thrive in children with T-lymphocyte PID. With these 3 signs, 96% of patients with neutrophil and complement deficiencies and 89% of children with T-lymphocyte immunodeficiencies could be identified correctly. Family history was the only warning sign that identified children with B-lymphocyte PID. PID awareness initiatives should be targeted at hospital pediatricians and families with a history of PID rather than the general public. Our results provide the general pediatrician with a simple refinement of 10 warning signs for identifying children with underlying immunodeficiency diseases.

Direct adaptive fuzzy control of a translating piezoelectric flexible manipulator driven by a pneumatic rodless cylinder

NASA Astrophysics Data System (ADS)

Qiu, Zhi-cheng; Wang, Bin; Zhang, Xian-min; Han, Jian-da

2013-04-01

This study presents a novel translating piezoelectric flexible manipulator driven by a rodless cylinder. Simultaneous positioning control and vibration suppression of the flexible manipulator is accomplished by using a hybrid driving scheme composed of the pneumatic cylinder and a piezoelectric actuator. Pulse code modulation (PCM) method is utilized for the cylinder. First, the system dynamics model is derived, and its standard multiple input multiple output (MIMO) state-space representation is provided. Second, a composite proportional derivative (PD) control algorithms and a direct adaptive fuzzy control method are designed for the MIMO system. Also, a time delay compensation algorithm, bandstop and low-pass filters are utilized, under consideration of the control hysteresis and the caused high-frequency modal vibration due to the long stroke of the cylinder, gas compression and nonlinear factors of the pneumatic system. The convergence of the closed loop system is analyzed. Finally, experimental apparatus is constructed and experiments are conducted. The effectiveness of the designed controllers and the hybrid driving scheme is verified through simulation and experimental comparison studies. The numerical simulation and experimental results demonstrate that the proposed system scheme of employing the pneumatic drive and piezoelectric actuator can suppress the vibration and achieve the desired positioning location simultaneously. Furthermore, the adopted adaptive fuzzy control algorithms can significantly enhance the control performance.

Intelligent Paging Based Mobile User Tracking Using Fuzzy Logic

NASA Astrophysics Data System (ADS)

Saha, Sajal; Dutta, Raju; Debnath, Soumen; Mukhopadhyay, Asish K.

2010-11-01

In general, a mobile user travels in a predefined path that depends mostly on the user's characteristics. Thus, tracking the locations of a mobile user is one of the challenges for location management. In this paper, we introduce a movement pattern learning strategy system to track the user's movements using adaptive fuzzy logic. Our fuzzy inference system extracts patterns from the historical data record of the cell numbers along with the date and time stamp of the users occupying the cell. Implementation of this strategy has been evaluated with the real time user data which proves the efficiency and accuracy of the model. This mechanism not only reduces user location tracking costs, but also significantly decreases the call-loss rates and average paging delays.

MI-ANFIS: A Multiple Instance Adaptive Neuro-Fuzzy Inference System

DTIC Science & Technology

2015-08-02

AUTHORS 7. PERFORMING ORGANIZATION NAMES AND ADDRESSES 15. SUBJECT TERMS b. ABSTRACT 2 . REPORT TYPE 17. LIMITATION OF ABSTRACT 15. NUMBER OF...fuzzy logic can deal with the uncertainty of human cognition [ 2 ]. ANFIS offers an alternative to rules’ identification. While Mamdani [3] and Sugeno [4...dimensional vector with elements xpjk corresponding to features, i.e., Bp =  xp11 xp12 . . . xp1D xp21 xp22 . . . xp2D ... ... . . . ... xpMp1

Intelligent robust tracking control for a class of uncertain strict-feedback nonlinear systems.

PubMed

Chang, Yeong-Chan

2009-02-01

This paper addresses the problem of designing robust tracking controls for a large class of strict-feedback nonlinear systems involving plant uncertainties and external disturbances. The input and virtual input weighting matrices are perturbed by bounded time-varying uncertainties. An adaptive fuzzy-based (or neural-network-based) dynamic feedback tracking controller will be developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error should be as small as possible. First, the adaptive approximators with linearly parameterized models are designed, and a partitioned procedure with respect to the developed adaptive approximators is proposed such that the implementation of the fuzzy (or neural network) basis functions depends only on the state variables but does not depend on the tuning approximation parameters. Furthermore, we extend to design the nonlinearly parameterized adaptive approximators. Consequently, the intelligent robust tracking control schemes developed in this paper possess the properties of computational simplicity and easy implementation. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed control algorithms.

Adaptive estimation of nonlinear parameters of a nonholonomic spherical robot using a modified fuzzy-based speed gradient algorithm

NASA Astrophysics Data System (ADS)

Roozegar, Mehdi; Mahjoob, Mohammad J.; Ayati, Moosa

2017-05-01

This paper deals with adaptive estimation of the unknown parameters and states of a pendulum-driven spherical robot (PDSR), which is a nonlinear in parameters (NLP) chaotic system with parametric uncertainties. Firstly, the mathematical model of the robot is deduced by applying the Newton-Euler methodology for a system of rigid bodies. Then, based on the speed gradient (SG) algorithm, the states and unknown parameters of the robot are estimated online for different step length gains and initial conditions. The estimated parameters are updated adaptively according to the error between estimated and true state values. Since the errors of the estimated states and parameters as well as the convergence rates depend significantly on the value of step length gain, this gain should be chosen optimally. Hence, a heuristic fuzzy logic controller is employed to adjust the gain adaptively. Simulation results indicate that the proposed approach is highly encouraging for identification of this NLP chaotic system even if the initial conditions change and the uncertainties increase; therefore, it is reliable to be implemented on a real robot.

PINOID AGC kinases are necessary for phytochrome-mediated enhancement of hypocotyl phototropism in Arabidopsis.

PubMed

Haga, Ken; Hayashi, Ken-ichiro; Sakai, Tatsuya

2014-11-01

Several members of the AGCVIII kinase subfamily, which includes PINOID (PID), PID2, and WAVY ROOT GROWTH (WAG) proteins, have previously been shown to phosphorylate PIN-FORMED (PIN) auxin transporters and control the auxin flow in plants. PID has been proposed as a key component of the phototropin signaling pathway that induces phototropic responses, although the responses were not significantly impaired in the pid single and pid wag1 wag2 triple mutants. This raises questions about the functional roles of the PID family in phototropic responses. Here, we investigated hypocotyl phototropism in the pid pid2 wag1 wag2 quadruple mutant in detail to clarify the roles of the PID family in Arabidopsis (Arabidopsis thaliana). The pid quadruple mutants exhibited moderate responses in continuous light-induced phototropism with a decrease in growth rates of hypocotyls and normal responses in pulse-induced phototropism. However, they showed serious defects in enhancements of pulse-induced phototropic curvatures and lateral fluorescent auxin transport by red light pretreatment. Red light pretreatment significantly reduced the expression level of PID, and the constitutive expression of PID prevented pulse-induced phototropism, irrespective of red light pretreatment. This suggests that the PID family plays a significant role in phytochrome-mediated phototropic enhancement but not the phototropin signaling pathway. Red light treatment enhanced the intracellular accumulation of PIN proteins in response to the vesicle-trafficking inhibitor brefeldin A in addition to increasing their expression levels. Taken together, these results suggest that red light preirradiation enhances phototropic curvatures by up-regulation of PIN proteins, which are not being phosphorylated by the PID family. © 2014 American Society of Plant Biologists. All Rights Reserved.

PINOID AGC Kinases Are Necessary for Phytochrome-Mediated Enhancement of Hypocotyl Phototropism in Arabidopsis1[W][OPEN

PubMed Central

Haga, Ken; Hayashi, Ken-ichiro; Sakai, Tatsuya

2014-01-01

Several members of the AGCVIII kinase subfamily, which includes PINOID (PID), PID2, and WAVY ROOT GROWTH (WAG) proteins, have previously been shown to phosphorylate PIN-FORMED (PIN) auxin transporters and control the auxin flow in plants. PID has been proposed as a key component of the phototropin signaling pathway that induces phototropic responses, although the responses were not significantly impaired in the pid single and pid wag1 wag2 triple mutants. This raises questions about the functional roles of the PID family in phototropic responses. Here, we investigated hypocotyl phototropism in the pid pid2 wag1 wag2 quadruple mutant in detail to clarify the roles of the PID family in Arabidopsis (Arabidopsis thaliana). The pid quadruple mutants exhibited moderate responses in continuous light-induced phototropism with a decrease in growth rates of hypocotyls and normal responses in pulse-induced phototropism. However, they showed serious defects in enhancements of pulse-induced phototropic curvatures and lateral fluorescent auxin transport by red light pretreatment. Red light pretreatment significantly reduced the expression level of PID, and the constitutive expression of PID prevented pulse-induced phototropism, irrespective of red light pretreatment. This suggests that the PID family plays a significant role in phytochrome-mediated phototropic enhancement but not the phototropin signaling pathway. Red light treatment enhanced the intracellular accumulation of PIN proteins in response to the vesicle-trafficking inhibitor brefeldin A in addition to increasing their expression levels. Taken together, these results suggest that red light preirradiation enhances phototropic curvatures by up-regulation of PIN proteins, which are not being phosphorylated by the PID family. PMID:25281709

Further Studies on the Effect of SiN x Refractive Index and Emitter Sheet Resistance on Potential-Induced Degradation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oh, Jaewon; Dauksher, Bill; Bowden, Stuart

We present the impacts of silicon nitride (SiNx) antireflection coating refractive index and emitter sheet resistance on potential-induced degradation of the shunting type (PID-s). Previously, it has been shown that the cell becomes more PID-s-susceptible as the refractive index decreases or the emitter sheet resistance increases. To verify the effect of refractive index on PID-s, we fabricated cells with varying SiN x refractive index (1.87, 1.94, 2.05) on typical p-type base solar cells with ~60 Ω/sq emitters. However, none of these cells showed output power degradation, regardless of the refractive index. Further investigation of the emitter showed that the PID-smore » was suppressed at ~60 Ω/sq due to the extremely high surface phosphorus concentration (6 x 10 21 cm -3), as measured by secondary ion mass spectrometry. Furthermore, PID-s was observed on cells possessing a high emitter sheet resistance (~80 Ω/sq). In conclusion, the emitter surface phosphorus concentration plays an important role in determining PID-s susceptibility.« less

Further Studies on the Effect of SiN x Refractive Index and Emitter Sheet Resistance on Potential-Induced Degradation

DOE PAGES

Oh, Jaewon; Dauksher, Bill; Bowden, Stuart; ...

2017-01-11

We present the impacts of silicon nitride (SiNx) antireflection coating refractive index and emitter sheet resistance on potential-induced degradation of the shunting type (PID-s). Previously, it has been shown that the cell becomes more PID-s-susceptible as the refractive index decreases or the emitter sheet resistance increases. To verify the effect of refractive index on PID-s, we fabricated cells with varying SiN x refractive index (1.87, 1.94, 2.05) on typical p-type base solar cells with ~60 Ω/sq emitters. However, none of these cells showed output power degradation, regardless of the refractive index. Further investigation of the emitter showed that the PID-smore » was suppressed at ~60 Ω/sq due to the extremely high surface phosphorus concentration (6 x 10 21 cm -3), as measured by secondary ion mass spectrometry. Furthermore, PID-s was observed on cells possessing a high emitter sheet resistance (~80 Ω/sq). In conclusion, the emitter surface phosphorus concentration plays an important role in determining PID-s susceptibility.« less

Investigating the Personality Inventory for DSM-5 using self and spouse reports.

PubMed

Jopp, Andrew M; South, Susan C

2015-04-01

Two new clinical tools, the Personality Inventory for DSM-5 (PID-5) and its informant report version, the PID-5-IRF, were developed to assess personality pathology as described by the new trait-based model within Section III of DSM-5. The current study used both self and spousal reports to evaluate agreement between the PID-5 and the PID-5-IRF and to determine the extent to which these measures capture personality pathology as conceptualized in Section II of DSM-5. A nonclinical sample (N = 96 individuals) of recently married couples completed the self-report PID-5, the PID-5-IRF, and the SNAP-2 to assess self-reported DSM-IV PD criteria. Analyses found good to excellent agreement between spousal reports on the PID-5 and the PID-5-IRF for facets in the negative affectivity, detachment, and antagonism domains. In addition, both the PID-5 and the PID-5-IRF each individually accounted for a significant proportion of variance in self-reported DSM-IV PD criteria. Implications for the present findings are discussed.

On the Structure of Personality Disorder Traits: Conjoint Analyses of the CAT-PD, PID-5, and NEO-PI-3 Trait Models

PubMed Central

Wright, Aidan G.C.; Simms, Leonard J.

2014-01-01

The current study examines the relations among contemporary models of pathological and normal range personality traits. Specifically, we report on (a) conjoint exploratory factor analyses of the Computerized Adaptive Test of Personality Disorder static form (CAT-PD-SF) with the Personality Inventory for the DSM-5 (PID-5; Krueger et al., 2012) and NEO Personality Inventory-3 First Half (NEI-PI-3FH; McCrae & Costa, 2007), and (b) unfolding hierarchical analyses of the three measures in a large general psychiatric outpatient sample (N = 628; 64% Female). A five-factor solution provided conceptually coherent alignment among the CAT-PD-SF, PID-5, and NEO-PI-3FH scales. Hierarchical solutions suggested that higher-order factors bear strong resemblance to dimensions that emerge from structural models of psychopathology (e.g., Internalizing and Externalizing spectra). These results demonstrate that the CAT-PD-SF adheres to the consensual structure of broad trait domains at the five-factor level. Additionally, patterns of scale loadings further inform questions of structure and bipolarity of facet and domain level constructs. Finally, hierarchical analyses strengthen the argument for using broad dimensions that span normative and pathological functioning to scaffold a quantitatively derived phenotypic structure of psychopathology to orient future research on explanatory, etiological, and maintenance mechanisms. PMID:24588061

Convergent and discriminant validity of alternative measures of maladaptive personality traits.

PubMed

Crego, Cristina; Widiger, Thomas A

2016-12-01

The purpose of the current study was to test empirically the convergent and discriminant validity of 3 recently developed, alternative measures of maladaptive personality traits: the Personality Inventory for Diagnostic and Statistical Manual for Mental Disorders-Fifth Edition (DSM-5, PID-5; Krueger, Derringer, Markon, Watson, & Skodol, 2012), the Computerized Adaptive Test-Personality Disorder Static Form (CAT-PD-SF; Simms et al., 2011), and Five Factor Model Personality Disorder scales (FFMPD; Widiger, Lynam, Miller, & Oltmanns, 2012). These measures were constructed with different rationales and methods, yet the result was highly congruent. The PID-5 and CAT-PD-SF were administered to 286 community adults with current or a history of mental health treatment; the CAT-PD-SF and FFMPD scales to 262 such adults; and the PID-5 and FFMPD scales to 266. The results indicated good to excellent internal consistency, as well as good to excellent convergent and discriminant validity for most scales with a few notable exceptions. Suggestions for future research are provided, including the potential benefits of scales that are unique to a respective instrument, replication of a dependency factor, and exploration as to the basis for instances of questionable convergent or discriminant validity. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Utilizing interview and self-report assessment of the Five-Factor Model to examine convergence with the alternative model for personality disorders.

PubMed

Helle, Ashley C; Trull, Timothy J; Widiger, Thomas A; Mullins-Sweatt, Stephanie N

2017-07-01

An alternative model for personality disorders is included in Section III (Emerging Models and Measures) of Diagnostic and Statistical Manual of Mental Disorders, (5th ed.; DSM-5). The DSM-5 dimensional trait model is an extension of the Five-Factor Model (FFM; American Psychiatric Association, 2013). The Personality Inventory for DSM-5 (PID-5) assesses the 5 domains and 25 traits in the alternative model. The current study expands on recent research to examine the relationship of the PID-5 with an interview measure of the FFM. The Structured Interview for the Five Factor Model of Personality (SIFFM) assesses the 5 bipolar domains and 30 facets of the FFM. Research has indicated that the SIFFM captures maladaptive aspects of personality (as well as adaptive). The SIFFM, NEO PI-R, and PID-5 were administered to participants to examine their respective convergent and discriminant validity. Results provide evidence for the convergence of the 2 models using self-report and interview measures of the FFM. Clinical implications and future directions are discussed, particularly a call for the development of a structured interview for the assessment of the DSM-5 dimensional trait model. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Activity of insulin growth factors and shrimp neurosecretory organ extracts on a lepidopteran cell line.

PubMed

Hatt, P J; Liebon, C; Morinière, M; Oberlander, H; Porcheron, P

1997-01-01

Ecdysteroids, or molting hormones, have been proven to be key differentiation regulators for epidermal cells in the postembryonic development of arthropods. Regulators of cell proliferation, however, remain largely unknown. To date, no diffusible insect peptidic growth factors have been characterized. Molecules structurally related to insulin have been discovered in insects, as in other eucaryotes. We developed in vitro tests for the preliminary characterization of potential growth factors in arthropods by adapting the procedures designed to detect such factors in vertebrates to an insect cell line (IAL-PID2) established from imaginal discs of the Indian meal moth. We verified the ability of these tests to measure the proliferation of IAL-PID2 cells. We tested mammalian insulin and insulin-like growth factors (IGF-I, IGF-II). Following an arrest of cell proliferation by serum deprivation, IGF-I and IGF-II caused partial resumption of the cell cycle, evidenced by DNA synthesis. In contrast, the addition of 20-hydroxyecdysone arrested the proliferation of the IAL-PID2 cells. The cell line was then used in a test for functional characterization of potential growth factors originating from the penaeid shrimp, Penaeus vannamei. Crude extracts of neurosecretory and nervous tissues, eyestalks, and ventral neural chain compensated for serum deprivation and stimulated completion of mitosis. Arch.

Fuzzy adaptive iterative learning coordination control of second-order multi-agent systems with imprecise communication topology structure

NASA Astrophysics Data System (ADS)

Chen, Jiaxi; Li, Junmin

2018-02-01

In this paper, we investigate the perfect consensus problem for second-order linearly parameterised multi-agent systems (MAS) with imprecise communication topology structure. Takagi-Sugeno (T-S) fuzzy models are presented to describe the imprecise communication topology structure of leader-following MAS, and a distributed adaptive iterative learning control protocol is proposed with the dynamic of leader unknown to any of the agent. The proposed protocol guarantees that the follower agents can track the leader perfectly on [0,T] for the consensus problem. Under alignment condition, a sufficient condition of the consensus for closed-loop MAS is given based on Lyapunov stability theory. Finally, a numerical example and a multiple pendulum system are given to illustrate the effectiveness of the proposed algorithm.

Modeling of a 5-cell direct methanol fuel cell using adaptive-network-based fuzzy inference systems

NASA Astrophysics Data System (ADS)

Wang, Rongrong; Qi, Liang; Xie, Xiaofeng; Ding, Qingqing; Li, Chunwen; Ma, ChenChi M.

The methanol concentrations, temperature and current were considered as inputs, the cell voltage was taken as output, and the performance of a direct methanol fuel cell (DMFC) was modeled by adaptive-network-based fuzzy inference systems (ANFIS). The artificial neural network (ANN) and polynomial-based models were selected to be compared with the ANFIS in respect of quality and accuracy. Based on the ANFIS model obtained, the characteristics of the DMFC were studied. The results show that temperature and methanol concentration greatly affect the performance of the DMFC. Within a restricted current range, the methanol concentration does not greatly affect the stack voltage. In order to obtain higher fuel utilization efficiency, the methanol concentrations and temperatures should be adjusted according to the load on the system.

Mutations in CsPID encoding a Ser/Thr protein kinase are responsible for round leaf shape in cucumber (Cucumis sativus L.).

PubMed

Zhang, Chaowen; Chen, Feifan; Zhao, Ziyao; Hu, Liangliang; Liu, Hanqiang; Cheng, Zhihui; Weng, Yiqun; Chen, Peng; Li, Yuhong

2018-06-01

Two round-leaf mutants, rl-1 and rl-2, were identified from EMS-induced mutagenesis. High throughput sequencing and map-based cloning suggested CsPID encoding a Ser/Thr protein kinase as the most possible candidate for rl-1. Rl-2 was allelic to Rl-1. Leaf shape is an important plant architecture trait that is affected by plant hormones, especially auxin. In Arabidopsis, PINOID (PID), a regulator for the auxin polar transporter PIN (PIN-FORMED) affects leaf shape formation, but this function of PID in crop plants has not been well studied. From an EMS mutagenesis population, we identified two round-leaf (rl) mutants, C356 and C949. Segregation analysis suggested that both mutations were controlled by single recessive genes, rl-1 and rl-2, respectively. With map-based cloning, we show that CsPID as the candidate gene of rl-1; a non-synonymous SNP in the second exon of CsPID resulted in an amino acid substitution and the round leaf phenotype. As compared in the wild type plant, CsPID had significantly lower expression in the root, leaf and female flowers in C356, which may result in the less developed roots, round leaves and abnormal female flowers, respectively in the rl-1 mutant. Among the three copies of PID genes, CsPID, CsPID2 and CSPID2L (CsPID2-like) in the cucumber genome, CsPID was the only one with significantly differential expression in adult leaves between WT and C356 suggesting CsPID plays a main role in leaf shape formation. The rl-2 mutation in C949 was also cloned, which was due to another SNP in a nearby location of rl-1 in the same CsPID gene. The two round leaf mutants and the work presented herein provide a good foundation for understanding the molecular mechanisms of CsPID in cucumber leaf development.

PID1 (NYGGF4), a new growth-inhibitory gene in embryonal brain tumors and gliomas

PubMed Central

Erdreich-Epstein, Anat; Robison, Nathan; Ren, Xiuhai; Zhou, Hong; Xu, Jingying; Davidson, Tom B.; Schur, Mathew; Gilles, Floyd H.; Ji, Lingyun; Malvar, Jemily; Shackleford, Gregory M.; Margol, Ashley S.; Krieger, Mark D.; Judkins, Alexander R.; Jones, David T.W.; Pfister, Stefan; Kool, Marcel; Sposto, Richard; Asgharazadeh, Shahab

2014-01-01

Purpose We present here the first report of PID1 (Phosphotyrosine Interaction Domain containing 1; NYGGF4) in cancer. PID1 was identified in 2006 as a gene that modulates insulin signaling and mitochondrial function in adipocytes and muscle cells. Experimental Design and Results Using four independent medulloblastoma datasets, we show that mean PID1 mRNA levels were lower in unfavorable medulloblastomas (Groups 3 and 4, and anaplastic histology) compared with favorable medulloblastomas (SHH and WNT groups, and desmoplastic/nodular histology) and with fetal cerebellum. In two large independent glioma datasets PID1 mRNA was lower in glioblastomas (GBMs), the most malignant gliomas, compared to other astrocytomas, oligodendrogliomas and non-tumor brains. Neural and proneural GBM subtypes had higher PID1 mRNA compared to classical and mesenchymal GBM. Importantly, overall survival and radiation-free progression-free survival were longer in medulloblastoma patients with higher PID1 mRNA (univariate and multivariate analyses). Higher PID1 mRNA also correlated with longer overall survival in glioma and GBM patients. In cell culture, overexpression of PID1 inhibited colony formation in medulloblastoma, atypical teratoid rhabdoid tumor (ATRT) and GBM cell lines. Increasing PID1 also increased cell death and apoptosis, inhibited proliferation, induced mitochondrial depolarization, and decreased serum-mediated phosphorylation of AKT and ERK in medulloblastoma, ATRT and/or GBM cell lines, whereas siRNA to PID1 diminished mitochondrial depolarization. Conclusions These data are the first to link PID1 to cancer and suggest that PID1 may have a tumor inhibitory function in these pediatric and adult brain tumors. PMID:24300787

Adaptive control of base-isolated buildings using piezoelectric friction dampers against near-field earthquakes

NASA Astrophysics Data System (ADS)

Bitaraf, Maryam; Ozbulut, Osman E.; Hurlebaus, Stefan

2010-04-01

This paper investigates the effectiveness of two adaptive control strategies for modulating control force of piezoelectric friction dampers (PFDs) that are employed as semi-active devices in combination with laminated rubber bearings for seismic protection of buildings. The first controller developed in this study is a direct adaptive fuzzy logic controller. It consists of an upper-level and a sub-level direct fuzzy controller. In the hierarchical control scheme, higher-level controller modifies universe of discourse of both premise and consequent variables of the sub-level controller using scaling factors in order to determine command voltage of the damper according to current level of ground motion. The sub-level fuzzy controller employs isolation displacement and velocity as its premise variables and command voltage as its consequent variable. The second controller is based on the simple adaptive control (SAC) method, which is a type of direct adaptive control approach. The objective of the SAC method is to make the plant, the controlled system, track the behavior of the structure with the optimum performance. By using SAC strategy, any change in the characteristics of the structure or uncertainties in the modeling of the structure and in the external excitation would be considered because it continuously monitors its own performance to modify its parameters. Here, SAC methodology is employed to obtain the required force which results in the optimum performance of the structure. Then, the command voltage of the PFD is determined to generate the desired force. For comparison purposes, an optimal controller is also developed and considered in the simulations together with maximum passive operation of the friction damper. Time-history analyses of a base-isolated five-story building are performed to evaluate the performance of the controllers. Results reveal that developed adaptive controllers can successfully improve seismic response of the base-isolated buildings against various types of earthquakes.

Adaptive hybrid control of manipulators

NASA Technical Reports Server (NTRS)

Seraji, H.

1987-01-01

Simple methods for the design of adaptive force and position controllers for robot manipulators within the hybrid control architecuture is presented. The force controller is composed of an adaptive PID feedback controller, an auxiliary signal and a force feedforward term, and it achieves tracking of desired force setpoints in the constraint directions. The position controller consists of adaptive feedback and feedforward controllers and an auxiliary signal, and it accomplishes tracking of desired position trajectories in the free directions. The controllers are capable of compensating for dynamic cross-couplings that exist between the position and force control loops in the hybrid control architecture. The adaptive controllers do not require knowledge of the complex dynamic model or parameter values of the manipulator or the environment. The proposed control schemes are computationally fast and suitable for implementation in on-line control with high sampling rates.

A digital optical phase-locked loop for diode lasers based on field programmable gate array.

PubMed

Xu, Zhouxiang; Zhang, Xian; Huang, Kaikai; Lu, Xuanhui

2012-09-01

We have designed and implemented a highly digital optical phase-locked loop (OPLL) for diode lasers in atom interferometry. The three parts of controlling circuit in this OPLL, including phase and frequency detector (PFD), loop filter and proportional integral derivative (PID) controller, are implemented in a single field programmable gate array chip. A structure type compatible with the model MAX9382∕MCH12140 is chosen for PFD and pipeline and parallelism technology have been adapted in PID controller. Especially, high speed clock and twisted ring counter have been integrated in the most crucial part, the loop filter. This OPLL has the narrow beat note line width below 1 Hz, residual mean-square phase error of 0.14 rad(2) and transition time of 100 μs under 10 MHz frequency step. A main innovation of this design is the completely digitalization of the whole controlling circuit in OPLL for diode lasers.

A digital optical phase-locked loop for diode lasers based on field programmable gate array

NASA Astrophysics Data System (ADS)

Xu, Zhouxiang; Zhang, Xian; Huang, Kaikai; Lu, Xuanhui

2012-09-01

We have designed and implemented a highly digital optical phase-locked loop (OPLL) for diode lasers in atom interferometry. The three parts of controlling circuit in this OPLL, including phase and frequency detector (PFD), loop filter and proportional integral derivative (PID) controller, are implemented in a single field programmable gate array chip. A structure type compatible with the model MAX9382/MCH12140 is chosen for PFD and pipeline and parallelism technology have been adapted in PID controller. Especially, high speed clock and twisted ring counter have been integrated in the most crucial part, the loop filter. This OPLL has the narrow beat note line width below 1 Hz, residual mean-square phase error of 0.14 rad2 and transition time of 100 μs under 10 MHz frequency step. A main innovation of this design is the completely digitalization of the whole controlling circuit in OPLL for diode lasers.

Modeling a multivariable reactor and on-line model predictive control.

PubMed

Yu, D W; Yu, D L

2005-10-01

A nonlinear first principle model is developed for a laboratory-scaled multivariable chemical reactor rig in this paper and the on-line model predictive control (MPC) is implemented to the rig. The reactor has three variables-temperature, pH, and dissolved oxygen with nonlinear dynamics-and is therefore used as a pilot system for the biochemical industry. A nonlinear discrete-time model is derived for each of the three output variables and their model parameters are estimated from the real data using an adaptive optimization method. The developed model is used in a nonlinear MPC scheme. An accurate multistep-ahead prediction is obtained for MPC, where the extended Kalman filter is used to estimate system unknown states. The on-line control is implemented and a satisfactory tracking performance is achieved. The MPC is compared with three decentralized PID controllers and the advantage of the nonlinear MPC over the PID is clearly shown.

PINOID functions in root phototropism as a negative regulator

PubMed Central

Haga, Ken; Sakai, Tatsuya

2015-01-01

The PINOID (PID) family, which belongs to AGCVIII kinases, is known to be involved in the regulation of auxin efflux transporter PIN-FORMED (PIN) proteins through changes in the phosphorylation status. Recently, we demonstrated that the PID family is necessary for phytochrome-mediated phototropic enhancement in Arabidopsis hypocotyls and that the downregulation of PID expression by red-light pretreatment results in the promotion of the PIN-mediated auxin gradient during phototropic responses. However, whether PID participates in root phototropism in Arabidopsis seedlings has not been well studied. Here, we demonstrated that negative root phototropic responses are enhanced in the pid quadruple mutant and are severely impaired in transgenic plants expressing PID constitutively. The results indicate that the PID family functions in a negative root phototropism as a negative regulator. On the other hand, analysis with PID fused to a yellow fluorescent protein, VENUS, showed that unilateral blue-light irradiation causes a lower accumulation of PID proteins on the shaded side than on the irradiated side. This result suggests that the blue-light-mediated asymmetrical distribution of PID proteins may be one of the critical responses in phototropin-mediated signals during a negative root phototropism. Alternatively, such a transverse gradient of PID proteins may result from gravitropic stimulation produced by phototropic bending. PMID:26039488

PINOID functions in root phototropism as a negative regulator.

PubMed

Haga, Ken; Sakai, Tatsuya

2015-01-01

The PINOID (PID) family, which belongs to AGCVIII kinases, is known to be involved in the regulation of auxin efflux transporter PIN-formed (PIN) proteins through changes in the phosphorylation status. Recently, we demonstrated that the PID family is necessary for phytochrome-mediated phototropic enhancement in Arabidopsis hypocotyls and that the downregulation of PID expression by red-light pretreatment results in the promotion of the PIN-mediated auxin gradient during phototropic responses. However, whether PID participates in root phototropism in Arabidopsis seedlings has not been well studied. Here, we demonstrated that negative root phototropic responses are enhanced in the pid quadruple mutant and are severely impaired in transgenic plants expressing PID constitutively. The results indicate that the PID family functions in a negative root phototropism as a negative regulator. On the other hand, analysis with PID fused to a yellow fluorescent protein, VENUS, showed that unilateral blue-light irradiation causes a lower accumulation of PID proteins on the shaded side than on the irradiated side. This result suggests that the blue-light-mediated asymmetrical distribution of PID proteins may be one of the critical responses in phototropin-mediated signals during a negative root phototropism. Alternatively, such a transverse gradient of PID proteins may result from gravitropic stimulation produced by phototropic bending.

A Quantum Hybrid PSO Combined with Fuzzy k-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection.

PubMed

Iliyasu, Abdullah M; Fatichah, Chastine

2017-12-19

A quantum hybrid (QH) intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO) method with the intuitionistic rationality of traditional fuzzy k -nearest neighbours (Fuzzy k -NN) algorithm (known simply as the Q-Fuzzy approach) is proposed for efficient feature selection and classification of cells in cervical smeared (CS) images. From an initial multitude of 17 features describing the geometry, colour, and texture of the CS images, the QPSO stage of our proposed technique is used to select the best subset features (i.e., global best particles) that represent a pruned down collection of seven features. Using a dataset of almost 1000 images, performance evaluation of our proposed Q-Fuzzy approach assesses the impact of our feature selection on classification accuracy by way of three experimental scenarios that are compared alongside two other approaches: the All-features (i.e., classification without prior feature selection) and another hybrid technique combining the standard PSO algorithm with the Fuzzy k -NN technique (P-Fuzzy approach). In the first and second scenarios, we further divided the assessment criteria in terms of classification accuracy based on the choice of best features and those in terms of the different categories of the cervical cells. In the third scenario, we introduced new QH hybrid techniques, i.e., QPSO combined with other supervised learning methods, and compared the classification accuracy alongside our proposed Q-Fuzzy approach. Furthermore, we employed statistical approaches to establish qualitative agreement with regards to the feature selection in the experimental scenarios 1 and 3. The synergy between the QPSO and Fuzzy k -NN in the proposed Q-Fuzzy approach improves classification accuracy as manifest in the reduction in number cell features, which is crucial for effective cervical cancer detection and diagnosis.

Fuzzy Logic-Based Guaranteed Lifetime Protocol for Real-Time Wireless Sensor Networks.

PubMed

Shah, Babar; Iqbal, Farkhund; Abbas, Ali; Kim, Ki-Il

2015-08-18

Few techniques for guaranteeing a network lifetime have been proposed despite its great impact on network management. Moreover, since the existing schemes are mostly dependent on the combination of disparate parameters, they do not provide additional services, such as real-time communications and balanced energy consumption among sensor nodes; thus, the adaptability problems remain unresolved among nodes in wireless sensor networks (WSNs). To solve these problems, we propose a novel fuzzy logic model to provide real-time communication in a guaranteed WSN lifetime. The proposed fuzzy logic controller accepts the input descriptors energy, time and velocity to determine each node's role for the next duration and the next hop relay node for real-time packets. Through the simulation results, we verified that both the guaranteed network's lifetime and real-time delivery are efficiently ensured by the new fuzzy logic model. In more detail, the above-mentioned two performance metrics are improved up to 8%, as compared to our previous work, and 14% compared to existing schemes, respectively.

Fuzzy Modal Control Applied to Smart Composite Structure

NASA Astrophysics Data System (ADS)

Koroishi, E. H.; Faria, A. W.; Lara-Molina, F. A.; Steffen, V., Jr.

2015-07-01

This paper proposes an active vibration control technique, which is based on Fuzzy Modal Control, as applied to a piezoelectric actuator bonded to a composite structure forming a so-called smart composite structure. Fuzzy Modal Controllers were found to be well adapted for controlling structures with nonlinear behavior, whose characteristics change considerably with respect to time. The smart composite structure was modelled by using a so called mixed theory. This theory uses a single equivalent layer for the discretization of the mechanical displacement field and a layerwise representation of the electrical field. Temperature effects are neglected. Due to numerical reasons it was necessary to reduce the size of the model of the smart composite structure so that the design of the controllers and the estimator could be performed. The role of the Kalman Estimator in the present contribution is to estimate the modal states of the system, which are used by the Fuzzy Modal controllers. Simulation results illustrate the effectiveness of the proposed vibration control methodology for composite structures.

Change detection of bitemporal multispectral images based on FCM and D-S theory

NASA Astrophysics Data System (ADS)

Shi, Aiye; Gao, Guirong; Shen, Shaohong

2016-12-01

In this paper, we propose a change detection method of bitemporal multispectral images based on the D-S theory and fuzzy c-means (FCM) algorithm. Firstly, the uncertainty and certainty regions are determined by thresholding method applied to the magnitudes of difference image (MDI) and spectral angle information (SAI) of bitemporal images. Secondly, the FCM algorithm is applied to the MDI and SAI in the uncertainty region, respectively. Then, the basic probability assignment (BPA) functions of changed and unchanged classes are obtained by the fuzzy membership values from the FCM algorithm. In addition, the optimal value of fuzzy exponent of FCM is adaptively determined by conflict degree between the MDI and SAI in uncertainty region. Finally, the D-S theory is applied to obtain the new fuzzy partition matrix for uncertainty region and further the change map is obtained. Experiments on bitemporal Landsat TM images and bitemporal SPOT images validate that the proposed method is effective.

A new approach to preserve privacy data mining based on fuzzy theory in numerical database

NASA Astrophysics Data System (ADS)

Cui, Run; Kim, Hyoung Joong

2014-01-01

With the rapid development of information techniques, data mining approaches have become one of the most important tools to discover the in-deep associations of tuples in large-scale database. Hence how to protect the private information is quite a huge challenge, especially during the data mining procedure. In this paper, a new method is proposed for privacy protection which is based on fuzzy theory. The traditional fuzzy approach in this area will apply fuzzification to the data without considering its readability. A new style of obscured data expression is introduced to provide more details of the subsets without reducing the readability. Also we adopt a balance approach between the privacy level and utility when to achieve the suitable subgroups. An experiment is provided to show that this approach is suitable for the classification without a lower accuracy. In the future, this approach can be adapted to the data stream as the low computation complexity of the fuzzy function with a suitable modification.

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Fuzzy Logic-Based Guaranteed Lifetime Protocol for Real-Time Wireless Sensor Networks

PubMed Central

Shah, Babar; Iqbal, Farkhund; Abbas, Ali; Kim, Ki-Il

2015-01-01

Few techniques for guaranteeing a network lifetime have been proposed despite its great impact on network management. Moreover, since the existing schemes are mostly dependent on the combination of disparate parameters, they do not provide additional services, such as real-time communications and balanced energy consumption among sensor nodes; thus, the adaptability problems remain unresolved among nodes in wireless sensor networks (WSNs). To solve these problems, we propose a novel fuzzy logic model to provide real-time communication in a guaranteed WSN lifetime. The proposed fuzzy logic controller accepts the input descriptors energy, time and velocity to determine each node’s role for the next duration and the next hop relay node for real-time packets. Through the simulation results, we verified that both the guaranteed network’s lifetime and real-time delivery are efficiently ensured by the new fuzzy logic model. In more detail, the above-mentioned two performance metrics are improved up to 8%, as compared to our previous work, and 14% compared to existing schemes, respectively. PMID:26295238

[Nationwide survey on pediatric infectious disease organisation and antibiotic management in French hospitals].

PubMed

Angoulvant, F; Dubos, F; Cohen, R; Martinot, A

2015-05-01

Skills in pediatric infectious disease (PID) and antibiotic management are of critical importance in hospitals. This nationwide survey aimed to assess the characteristics, training, and tasks of PID consultants in French hospitals. The management of PID and antibiotic therapy was also analyzed in hospitals lacking PID consultants. An electronic survey linked to a dedicated website was sent to French hospitals with a pediatric department in June 2012. In hospitals where PID consultants were available, they were asked to answer the questionnaire. In hospitals lacking PID consultants, pediatricians were asked to send the questionnaire to their infectious disease consultant, if available. A total of 86 individual responses were received from 76 hospitals (including 26 academic hospitals). The existence of a PID consultant was declared in 53 hospitals. Responses were received from the person claiming to be "the" or "one of the" PID consultants in 46 cases, representing 39 centers. PID consultants had a degree in PID (46%) or antibiotic therapy (37%), or a complementary qualification as a specialist in infectious diseases (13%). The PID consultants worked in departments of general pediatrics (61%) and emergency medicine (15%), or neonatology (15%). They were involved in the Nosocomial Infection Prevention Committee (43%) or the Antimicrobial Therapy Committee (63%) and had teaching activities (65%). There was a specific unit with a PID label in 10% of the 39 centers reporting at least one consultant and consultations of infectious diseases took place in 28%. PID consultants are rare. Their important role in patient care should be recognized. Efforts should focus on recruiting and training such specialists. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Magnetic induction of hyperthermia by a modified self-learning fuzzy temperature controller

NASA Astrophysics Data System (ADS)

Wang, Wei-Cheng; Tai, Cheng-Chi

2017-07-01

The aim of this study involved developing a temperature controller for magnetic induction hyperthermia (MIH). A closed-loop controller was applied to track a reference model to guarantee a desired temperature response. The MIH system generated an alternating magnetic field to heat a high magnetic permeability material. This wireless induction heating had few side effects when it was extensively applied to cancer treatment. The effects of hyperthermia strongly depend on the precise control of temperature. However, during the treatment process, the control performance is degraded due to severe perturbations and parameter variations. In this study, a modified self-learning fuzzy logic controller (SLFLC) with a gain tuning mechanism was implemented to obtain high control performance in a wide range of treatment situations. This implementation was performed by appropriately altering the output scaling factor of a fuzzy inverse model to adjust the control rules. In this study, the proposed SLFLC was compared to the classical self-tuning fuzzy logic controller and fuzzy model reference learning control. Additionally, the proposed SLFLC was verified by conducting in vitro experiments with porcine liver. The experimental results indicated that the proposed controller showed greater robustness and excellent adaptability with respect to the temperature control of the MIH system.

An Indoor Pedestrian Positioning Method Using HMM with a Fuzzy Pattern Recognition Algorithm in a WLAN Fingerprint System

PubMed Central

Ni, Yepeng; Liu, Jianbo; Liu, Shan; Bai, Yaxin

2016-01-01

With the rapid development of smartphones and wireless networks, indoor location-based services have become more and more prevalent. Due to the sophisticated propagation of radio signals, the Received Signal Strength Indicator (RSSI) shows a significant variation during pedestrian walking, which introduces critical errors in deterministic indoor positioning. To solve this problem, we present a novel method to improve the indoor pedestrian positioning accuracy by embedding a fuzzy pattern recognition algorithm into a Hidden Markov Model. The fuzzy pattern recognition algorithm follows the rule that the RSSI fading has a positive correlation to the distance between the measuring point and the AP location even during a dynamic positioning measurement. Through this algorithm, we use the RSSI variation trend to replace the specific RSSI value to achieve a fuzzy positioning. The transition probability of the Hidden Markov Model is trained by the fuzzy pattern recognition algorithm with pedestrian trajectories. Using the Viterbi algorithm with the trained model, we can obtain a set of hidden location states. In our experiments, we demonstrate that, compared with the deterministic pattern matching algorithm, our method can greatly improve the positioning accuracy and shows robust environmental adaptability. PMID:27618053

Achieving realistic performance and decison-making capabilities in computer-generated air forces

NASA Astrophysics Data System (ADS)

Banks, Sheila B.; Stytz, Martin R.; Santos, Eugene, Jr.; Zurita, Vincent B.; Benslay, James L., Jr.

1997-07-01

For a computer-generated force (CGF) system to be useful in training environments, it must be able to operate at multiple skill levels, exhibit competency at assigned missions, and comply with current doctrine. Because of the rapid rate of change in distributed interactive simulation (DIS) and the expanding set of performance objectives for any computer- generated force, the system must also be modifiable at reasonable cost and incorporate mechanisms for learning. Therefore, CGF applications must have adaptable decision mechanisms and behaviors and perform automated incorporation of past reasoning and experience into its decision process. The CGF must also possess multiple skill levels for classes of entities, gracefully degrade its reasoning capability in response to system stress, possess an expandable modular knowledge structure, and perform adaptive mission planning. Furthermore, correctly performing individual entity behaviors is not sufficient. Issues related to complex inter-entity behavioral interactions, such as the need to maintain formation and share information, must also be considered. The CGF must also be able to acceptably respond to unforeseen circumstances and be able to make decisions in spite of uncertain information. Because of the need for increased complexity in the virtual battlespace, the CGF should exhibit complex, realistic behavior patterns within the battlespace. To achieve these necessary capabilities, an extensible software architecture, an expandable knowledge base, and an adaptable decision making mechanism are required. Our lab has addressed these issues in detail. The resulting DIS-compliant system is called the automated wingman (AW). The AW is based on fuzzy logic, the common object database (CODB) software architecture, and a hierarchical knowledge structure. We describe the techniques we used to enable us to make progress toward a CGF entity that satisfies the requirements presented above. We present our design and implementation of an adaptable decision making mechanism that uses multi-layered, fuzzy logic controlled situational analysis. Because our research indicates that fuzzy logic can perform poorly under certain circumstances, we combine fuzzy logic inferencing with adversarial game tree techniques for decision making in strategic and tactical engagements. We describe the approach we employed to achieve this fusion. We also describe the automated wingman's system architecture and knowledge base architecture.

Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images

NASA Astrophysics Data System (ADS)

Deng, He; Deng, Wankai; Sun, Xianping; Ye, Chaohui; Zhou, Xin

2016-10-01

Image enhancement techniques are able to improve the contrast and visual quality of magnetic resonance (MR) images. However, conventional methods cannot make up some deficiencies encountered by respective brain tumor MR imaging modes. In this paper, we propose an adaptive intuitionistic fuzzy sets-based scheme, called as AIFE, which takes information provided from different MR acquisitions and tries to enhance the normal and abnormal structural regions of the brain while displaying the enhanced results as a single image. The AIFE scheme firstly separates an input image into several sub images, then divides each sub image into object and background areas. After that, different novel fuzzification, hyperbolization and defuzzification operations are implemented on each object/background area, and finally an enhanced result is achieved via nonlinear fusion operators. The fuzzy implementations can be processed in parallel. Real data experiments demonstrate that the AIFE scheme is not only effectively useful to have information from images acquired with different MR sequences fused in a single image, but also has better enhancement performance when compared to conventional baseline algorithms. This indicates that the proposed AIFE scheme has potential for improving the detection and diagnosis of brain tumors.

Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning

ERIC Educational Resources Information Center

Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun

2012-01-01

With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy…

PINOID kinase regulates root gravitropism through modulation of PIN2-dependent basipetal auxin transport in Arabidopsis.

PubMed

Sukumar, Poornima; Edwards, Karin S; Rahman, Abidur; Delong, Alison; Muday, Gloria K

2009-06-01

Reversible protein phosphorylation is a key regulatory mechanism governing polar auxin transport. We characterized the auxin transport and gravitropic phenotypes of the pinoid-9 (pid-9) mutant of Arabidopsis (Arabidopsis thaliana) and tested the hypothesis that phosphorylation mediated by PID kinase and dephosphorylation regulated by the ROOTS CURL IN NAPHTHYLPHTHALAMIC ACID1 (RCN1) protein might antagonistically regulate root auxin transport and gravity response. Basipetal indole-3-acetic acid transport and gravitropism are reduced in pid-9 seedlings, while acropetal transport and lateral root development are unchanged. Treatment of wild-type seedlings with the protein kinase inhibitor staurosporine phenocopies the reduced auxin transport and gravity response of pid-9, while pid-9 is resistant to inhibition by staurosporine. Staurosporine and the phosphatase inhibitor, cantharidin, delay the asymmetric expression of DR5revGFP (green fluorescent protein) at the root tip after gravistimulation. Gravity response defects of rcn1 and pid-9 are partially rescued by treatment with staurosporine and cantharidin, respectively. The pid-9 rcn1 double mutant has a more rapid gravitropic response than rcn1. These data are consistent with a reciprocal regulation of gravitropism by RCN1 and PID. Furthermore, the effect of staurosporine is lost in pinformed2 (pin2). Our data suggest that reduced PID kinase function inhibits gravitropism and basipetal indole-3-acetic acid transport. However, in contrast to PID overexpression studies, we observed wild-type asymmetric membrane distribution of the PIN2 protein in both pid-9 and wild-type root tips, although PIN2 accumulates in endomembrane structures in pid-9 roots. Similarly, staurosporine-treated plants expressing a PIN2GFP fusion exhibit endomembrane accumulation of PIN2GFP, but no changes in membrane asymmetries were detected. Our data suggest that PID plays a limited role in root development; loss of PID activity alters auxin transport and gravitropism without causing an obvious change in cellular polarity.

Primary Immunodeficiencies: “New” Disease in an Old Country

PubMed Central

Lee, Pamela P W; Lau, Yu-Lung

2009-01-01

Primary immunodeficiency disorders (PIDs) are rare inborn errors of the immune system. Patients with PIDs are unique models that exemplify the functional and phenotypic consequences of various immune defects underlying infections, autoimmunity, lymphoproliferation, allergy and cancer. Over 150 PID syndromes were characterized in the past 60 years, with an ever growing list of new entities being discovered. Because of their rarity, multi-center collaboration for pooled data analysis and molecular studies is important to gain meaningful insights into the phenotypic and genetic diversities of PIDs. In this article, we summarize our research findings on PIDs in Chinese population in the past 20 years. Close collaboration among various immunology centers, cross-referrals and systematic data analysis constitute the foundation for research on PIDs. Future directions include establishment of a national PID registry, raising awareness of PIDs and securing sufficient resources for patient care and scientific research. PMID:20003815

A fuzzy logic intelligent diagnostic system for spacecraft integrated vehicle health management

NASA Technical Reports Server (NTRS)

Wu, G. Gordon

1995-01-01

Due to the complexity of future space missions and the large amount of data involved, greater autonomy in data processing is demanded for mission operations, training, and vehicle health management. In this paper, we develop a fuzzy logic intelligent diagnostic system to perform data reduction, data analysis, and fault diagnosis for spacecraft vehicle health management applications. The diagnostic system contains a data filter and an inference engine. The data filter is designed to intelligently select only the necessary data for analysis, while the inference engine is designed for failure detection, warning, and decision on corrective actions using fuzzy logic synthesis. Due to its adaptive nature and on-line learning ability, the diagnostic system is capable of dealing with environmental noise, uncertainties, conflict information, and sensor faults.

Extending and implementing the Persistent ID pillars

NASA Astrophysics Data System (ADS)

Car, Nicholas; Golodoniuc, Pavel; Klump, Jens

2017-04-01

The recent double decade anniversary of scholarly persistent identifier use has triggered journal special editions such as "20 Years of Persistent Identifiers". For such a publication, it is apt to consider the longevity of some persistent identifier (PID) mechanisms (Digital Object Identifiers) and the partial disappearance of others (Life Sciences IDs). We have previously postulated a set of "PID Pillars" [1] which are design principles aimed at ensuring PIDs can survive technology and social change and thus persist for the long term that we have drawn from our observations of PIDs at work over many years. The principles: describe how to ensure identifiers' system and organisation independence; codify the delivery of essential PID system functions; mandate a separation of PID functions from data delivery mechanisms; and require generation of policies detailing how change is handled. In this presentation, first we extend on our previous work of introducing the pillars by refining their descriptions, giving specific suggestions for each and presenting some work that addresses them. Second, we propose a baseline data model for persistent identifiers that, if used, would assist the separation of PID metadata and PID system functioning. This would allow PID system function specifics to change over time (e.g. resolver services or even resolution protocols) and yet preserve the PIDs themselves. Third, we detail our existing PID system — the PID Service [2] — that partially implements the pillars and describe both its successes and shortcomings. Finally, we describe our planned next-generation system that will aim to use the baseline data model and fully implement the pillars.

Hysteresis Analysis and Positioning Control for a Magnetic Shape Memory Actuator

PubMed Central

Lin, Jhih-Hong; Chiang, Mao-Hsiung

2015-01-01

Magnetic shape memory alloys (MSM alloys), a new kind of smart materials, have become a potential candidate in many engineering fields. MSMs have the advantage of bearing a huge strain, much larger than other materials. In addition, they also have fast response. These characteristics make MSM a good choice in micro engineering. However, MSMs display the obvious hysteresis phenomenon of nonlinear behavior. Thus the difficulty in using the MSM element as a positioning actuator is increased due to the hysteresis. In this paper, the hysteresis phenomenon of the MSM actuator is analyzed, and the closed-loop positioning control is also implemented experimentally. For that, a modified fuzzy sliding mode control (MFSMC) is proposed. The MFSMC and the PID control are used to design the controllers for realizing the positioning control. The experimental results are compared under different experimental conditions, such as different frequency, amplitude, and loading. The experimental results show that the precise positioning control of MFSMC can be achieved satisfactorily. PMID:25853405

Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy.

PubMed

Liu, Hui; Li, Yingzi; Zhang, Yingxu; Chen, Yifu; Song, Zihang; Wang, Zhenyu; Zhang, Suoxin; Qian, Jianqiang

2018-01-01

Proportional-integral-derivative (PID) parameters play a vital role in the imaging process of an atomic force microscope (AFM). Traditional parameter tuning methods require a lot of manpower and it is difficult to set PID parameters in unattended working environments. In this manuscript, an intelligent tuning method of PID parameters based on iterative learning control is proposed to self-adjust PID parameters of the AFM according to the sample topography. This method gets enough information about the output signals of PID controller and tracking error, which will be used to calculate the proper PID parameters, by repeated line scanning until convergence before normal scanning to learn the topography. Subsequently, the appropriate PID parameters are obtained by fitting method and then applied to the normal scanning process. The feasibility of the method is demonstrated by the convergence analysis. Simulations and experimental results indicate that the proposed method can intelligently tune PID parameters of the AFM for imaging different topographies and thus achieve good tracking performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fuzzy intelligent quality monitoring model for X-ray image processing.

PubMed

Khalatbari, Azadeh; Jenab, Kouroush

2009-01-01

Today's imaging diagnosis needs to adapt modern techniques of quality engineering to maintain and improve its accuracy and reliability in health care system. One of the main factors that influences diagnostic accuracy of plain film X-ray on detecting pathology is the level of film exposure. If the level of film exposure is not adequate, a normal body structure may be interpretated as pathology and vice versa. This not only influences the patient management but also has an impact on health care cost and patient's quality of life. Therefore, providing an accurate and high quality image is the first step toward an excellent patient management in any health care system. In this paper, we study these techniques and also present a fuzzy intelligent quality monitoring model, which can be used to keep variables from degrading the image quality. The variables derived from chemical activity, cleaning procedures, maintenance, and monitoring may not be sensed, measured, or calculated precisely due to uncertain situations. Therefore, the gamma-level fuzzy Bayesian model for quality monitoring of an image processing is proposed. In order to apply the Bayesian concept, the fuzzy quality characteristics are assumed as fuzzy random variables. Using the fuzzy quality characteristics, the newly developed model calculates the degradation risk for image processing. A numerical example is also presented to demonstrate the application of the model.

Modeling and simulation of evacuation behavior using fuzzy logic in a goal finding application

NASA Astrophysics Data System (ADS)

Sharma, Sharad; Ogunlana, Kola; Sree, Swetha

2016-05-01

Modeling and simulation has been widely used as a training and educational tool for depicting different evacuation strategies and damage control decisions during evacuation. However, there are few simulation environments that can include human behavior with low to high levels of fidelity. It is well known that crowd stampede induced by panic leads to fatalities as people are crushed or trampled. Our proposed goal finding application can be used to model situations that are difficult to test in real-life due to safety considerations. It is able to include agent characteristics and behaviors. Findings of this model are very encouraging as agents are able to assume various roles to utilize fuzzy logic on the way to reaching their goals. Fuzzy logic is used to model stress, panic and the uncertainty of emotions. The fuzzy rules link these parts together while feeding into behavioral rules. The contributions of this paper lies in our approach of utilizing fuzzy logic to show learning and adaptive behavior of agents in a goal finding application. The proposed application will aid in running multiple evacuation drills for what-if scenarios by incorporating human behavioral characteristics that can scale from a room to building. Our results show that the inclusion of fuzzy attributes made the evacuation time of the agents closer to the real time drills.

A Novel Technique for Performing PID Susceptibility Screening during the Solar Cell Fabrication Process

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oh, Jaewon; Dahal, Som; Dauksher, Bill

2016-11-21

Various characterization techniques have historically been developed in order to screen potential induced degradation (PID)-susceptible cells, but those techniques require final solar cells. We present a new characterization technique for screening PID-susceptible cells during the cell fabrication process. Illuminated Lock-In Thermography (ILIT) was used to image PID shunting of the cell without metallization and clearly showed PID-affected areas. PID-susceptible cells can be screened by ILIT, and the sample structure can advantageously be simplified as long as the sample has the silicon nitride antireflection coating and an aluminum back surface field.

Soft Real-Time PID Control on a VME Computer

NASA Technical Reports Server (NTRS)

Karayan, Vahag; Sander, Stanley; Cageao, Richard

2007-01-01

microPID (uPID) is a computer program for real-time proportional + integral + derivative (PID) control of a translation stage in a Fourier-transform ultraviolet spectrometer. microPID implements a PID control loop over a position profile at sampling rate of 8 kHz (sampling period 125microseconds). The software runs in a strippeddown Linux operating system on a VersaModule Eurocard (VME) computer operating in real-time priority queue using an embedded controller, a 16-bit digital-to-analog converter (D/A) board, and a laser-positioning board (LPB). microPID consists of three main parts: (1) VME device-driver routines, (2) software that administers a custom protocol for serial communication with a control computer, and (3) a loop section that obtains the current position from an LPB-driver routine, calculates the ideal position from the profile, and calculates a new voltage command by use of an embedded PID routine all within each sampling period. The voltage command is sent to the D/A board to control the stage. microPID uses special kernel headers to obtain microsecond timing resolution. Inasmuch as microPID implements a single-threaded process and all other processes are disabled, the Linux operating system acts as a soft real-time system.

Measure of librarian pressure using fuzzy inference system: A case study in Longyan University

NASA Astrophysics Data System (ADS)

Huang, Jian-Jing

2014-10-01

As the hierarchy of middle managers in college's librarian. They may own much work pressure from their mind. How to adapt psychological problem, control the emotion and keep a good relationship in their work place, it becomes an important issue. Especially, they work in China mainland environment. How estimate the librarians work pressure and improve the quality of service in college libraries. Those are another serious issues. In this article, the authors would like discuss how can we use fuzzy inference to test librarian work pressure.

Design of a modified adaptive neuro fuzzy inference system classifier for medical diagnosis of Pima Indians Diabetes

NASA Astrophysics Data System (ADS)

Sagir, Abdu Masanawa; Sathasivam, Saratha

2017-08-01

Medical diagnosis is the process of determining which disease or medical condition explains a person's determinable signs and symptoms. Diagnosis of most of the diseases is very expensive as many tests are required for predictions. This paper aims to introduce an improved hybrid approach for training the adaptive network based fuzzy inference system with Modified Levenberg-Marquardt algorithm using analytical derivation scheme for computation of Jacobian matrix. The goal is to investigate how certain diseases are affected by patient's characteristics and measurement such as abnormalities or a decision about presence or absence of a disease. To achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system to classify and predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. The proposed hybridised intelligent system was tested with Pima Indian Diabetes dataset obtained from the University of California at Irvine's (UCI) machine learning repository. The proposed method's performance was evaluated based on training and test datasets. In addition, an attempt was done to specify the effectiveness of the performance measuring total accuracy, sensitivity and specificity. In comparison, the proposed method achieves superior performance when compared to conventional ANFIS based gradient descent algorithm and some related existing methods. The software used for the implementation is MATLAB R2014a (version 8.3) and executed in PC Intel Pentium IV E7400 processor with 2.80 GHz speed and 2.0 GB of RAM.

Exploiting Fractional Order PID Controller Methods in Improving the Performance of Integer Order PID Controllers: A GA Based Approach

NASA Astrophysics Data System (ADS)

Mukherjee, Bijoy K.; Metia, Santanu

2009-10-01

The paper is divided into three parts. The first part gives a brief introduction to the overall paper, to fractional order PID (PIλDμ) controllers and to Genetic Algorithm (GA). In the second part, first it has been studied how the performance of an integer order PID controller deteriorates when implemented with lossy capacitors in its analog realization. Thereafter it has been shown that the lossy capacitors can be effectively modeled by fractional order terms. Then, a novel GA based method has been proposed to tune the controller parameters such that the original performance is retained even though realized with the same lossy capacitors. Simulation results have been presented to validate the usefulness of the method. Some Ziegler-Nichols type tuning rules for design of fractional order PID controllers have been proposed in the literature [11]. In the third part, a novel GA based method has been proposed which shows how equivalent integer order PID controllers can be obtained which will give performance level similar to those of the fractional order PID controllers thereby removing the complexity involved in the implementation of the latter. It has been shown with extensive simulation results that the equivalent integer order PID controllers more or less retain the robustness and iso-damping properties of the original fractional order PID controllers. Simulation results also show that the equivalent integer order PID controllers are more robust than the normal Ziegler-Nichols tuned PID controllers.

Potential-induced degradation in photovoltaic modules: a critical review

DOE PAGES

Luo, Wei; Khoo, Yong Sheng; Hacke, Peter; ...

2016-11-21

Potential-induced degradation (PID) has received considerable attention in recent years due to its detrimental impact on photovoltaic (PV) module performance under field conditions. Both crystalline silicon (c-Si) and thin-film PV modules are susceptible to PID. While extensive studies have already been conducted in this area, the understanding of the PID phenomena is still incomplete and it remains a major problem in the PV industry. Herein, a critical review of the available literature is given to serve as a one-stop source for understanding the current status of PID research. This article also aims to provide an overview of future research pathsmore » to address PID-related issues. This paper consists of three parts. In the first part, the modelling of leakage current paths in the module package is discussed. The PID mechanisms in both c-Si and thin-film PV modules are also comprehensively reviewed. The second part summarizes various test methods to evaluate PV modules for PID. The last part focuses on studies related to PID in the omnipresent p-type c-Si PV modules. The dependence of temperature, humidity and voltage on the progression of PID is examined. Preventive measures against PID at the cell, module and system levels are illustrated. Moreover, PID recovery in standard p-type c-Si PV modules is also studied. Most of the findings from p-type c-Si PV modules are also applicable to other PV module technologies.« less

How effective are the 6 European Society of Immunodeficiency warning signs for primary immunodeficiency disease?

PubMed

Arslan, Sevket; Ucar, Ramazan; Caliskaner, Ahmet Zafer; Reisli, Ismail; Guner, Sukru Nail; Sayar, Esra Hazar; Baloglu, Ismail

2016-02-01

The European Society of Immunodeficiency (ESID) developed 6 warning signs to promote the awareness of adult primary immunodeficiency disease (PID). To screen adult patients for the presence of PID using these 6 warning signs to determine the effectiveness of this protocol. Questions related to the ESID warning signs for adult PID were added to the standard outpatient clinic file system and asked of 3,510 patients who were admitted to our clinic for any reason. Patients with signs and/or suspicion of PID based on their medical history underwent immunologic investigation. In total, 24 patients were diagnosed as having a PID. The most common reason that patients with PID were admitted was frequent infection (n=18 [75%]), and the most common PID subgroup was common variable immunodeficiency (n=12 [50%]). Twenty patients with PID had at least one positive finding according to the ESID warning signs. Two patients with gastrointestinal concerns and 2 with dermatologic symptoms were also diagnosed as having a PID, although they did not have any of the ESID warning signs. The ESID warning signs do not specify the need for symptoms to diagnose a PIDs and do not include a comprehensive list of all signs and symptoms of PIDs. As a result, more than infection-centric questions are needed to identify adult patients with immunodeficiencies. Copyright © 2016 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Pelvic inflammatory disease.

PubMed

Hanna, L; Highleyman, L

1996-03-01

Pelvic inflammatory disease (PID) is a generic term relating to a broad range of conditions. The term is used to describe infections of the fallopian tubes, uterus, ovaries, or peritoneum. PID is a potentially life-threatening condition in any woman, but HIV-positive women are at serious risk of severe complications or death. PID is caused when infection-producing organisms spread upwards from the vagina through the cervix to the upper reproductive organs. Untreated sexually transmitted diseases are a leading cause of PID. Consequences include chronic pelvic pain, abdominal abscesses, inflammation of the covering of the liver, sepsis, and death. Sterility may also result from PID. PID is generally treated with a combination of antibiotics, and it is crucial to treat other concurrent infections as well. Early treatment of PID in HIV-positive women is essential.

Research on Potential Induced Degradation (PID) of PV Modules in Different Typical Climate Regions

NASA Astrophysics Data System (ADS)

Daoren, Gong; Yingnan, Chen; Gang, Sun; Wenjing, Wang; Zhenshuang, Ji

2018-03-01

Potential Induced Degradation (PID) is one of the most important factors effecting the performances of Photovoltaic (PV) modules and PV systems in recent years. In this paper the PID phenomena of the PV power plant in different typical climate regions were studied and some experimental PID simulations were carried out in order to find out the factors effecting the performance by PID. The results show that the typical PID phenomena are easy to occur in cells close to the border of the PV module. PID phenomena can appear in PV power plants under different climate conditions, but the effecting degrees on module performance are different depending on temperature, humidity and other parameters. We also find the maximum power would recover in some degree after positive-bias voltage duration.

Adaptive control paradigm for photovoltaic and solid oxide fuel cell in a grid-integrated hybrid renewable energy system.

PubMed

Mumtaz, Sidra; Khan, Laiq

2017-01-01

The hybrid power system (HPS) is an emerging power generation scheme due to the plentiful availability of renewable energy sources. Renewable energy sources are characterized as highly intermittent in nature due to meteorological conditions, while the domestic load also behaves in a quite uncertain manner. In this scenario, to maintain the balance between generation and load, the development of an intelligent and adaptive control algorithm has preoccupied power engineers and researchers. This paper proposes a Hermite wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control of photovoltaic (PV) systems to extract maximum power and a Hermite wavelet incorporated NeuroFuzzy indirect adaptive control of Solid Oxide Fuel Cells (SOFC) to obtain a swift response in a grid-connected hybrid power system. A comprehensive simulation testbed for a grid-connected hybrid power system (wind turbine, PV cells, SOFC, electrolyzer, battery storage system, supercapacitor (SC), micro-turbine (MT) and domestic load) is developed in Matlab/Simulink. The robustness and superiority of the proposed indirect adaptive control paradigm are evaluated through simulation results in a grid-connected hybrid power system testbed by comparison with a conventional PI (proportional and integral) control system. The simulation results verify the effectiveness of the proposed control paradigm.

Adaptive control paradigm for photovoltaic and solid oxide fuel cell in a grid-integrated hybrid renewable energy system

PubMed Central

Khan, Laiq

2017-01-01

The hybrid power system (HPS) is an emerging power generation scheme due to the plentiful availability of renewable energy sources. Renewable energy sources are characterized as highly intermittent in nature due to meteorological conditions, while the domestic load also behaves in a quite uncertain manner. In this scenario, to maintain the balance between generation and load, the development of an intelligent and adaptive control algorithm has preoccupied power engineers and researchers. This paper proposes a Hermite wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control of photovoltaic (PV) systems to extract maximum power and a Hermite wavelet incorporated NeuroFuzzy indirect adaptive control of Solid Oxide Fuel Cells (SOFC) to obtain a swift response in a grid-connected hybrid power system. A comprehensive simulation testbed for a grid-connected hybrid power system (wind turbine, PV cells, SOFC, electrolyzer, battery storage system, supercapacitor (SC), micro-turbine (MT) and domestic load) is developed in Matlab/Simulink. The robustness and superiority of the proposed indirect adaptive control paradigm are evaluated through simulation results in a grid-connected hybrid power system testbed by comparison with a conventional PI (proportional and integral) control system. The simulation results verify the effectiveness of the proposed control paradigm. PMID:28329015

Can fuzzy logic bring complex problems into focus? Modeling imprecise factors in environmental policy

DOE Office of Scientific and Technical Information (OSTI.GOV)

McKone, Thomas E.; Deshpande, Ashok W.

2004-06-14

In modeling complex environmental problems, we often fail to make precise statements about inputs and outcome. In this case the fuzzy logic method native to the human mind provides a useful way to get at these problems. Fuzzy logic represents a significant change in both the approach to and outcome of environmental evaluations. Risk assessment is currently based on the implicit premise that probability theory provides the necessary and sufficient tools for dealing with uncertainty and variability. The key advantage of fuzzy methods is the way they reflect the human mind in its remarkable ability to store and process informationmore » which is consistently imprecise, uncertain, and resistant to classification. Our case study illustrates the ability of fuzzy logic to integrate statistical measurements with imprecise health goals. But we submit that fuzzy logic and probability theory are complementary and not competitive. In the world of soft computing, fuzzy logic has been widely used and has often been the ''smart'' behind smart machines. But it will require more effort and case studies to establish its niche in risk assessment or other types of impact assessment. Although we often hear complaints about ''bright lines,'' could we adapt to a system that relaxes these lines to fuzzy gradations? Would decision makers and the public accept expressions of water or air quality goals in linguistic terms with computed degrees of certainty? Resistance is likely. In many regions, such as the US and European Union, it is likely that both decision makers and members of the public are more comfortable with our current system in which government agencies avoid confronting uncertainties by setting guidelines that are crisp and often fail to communicate uncertainty. But some day perhaps a more comprehensive approach that includes exposure surveys, toxicological data, epidemiological studies coupled with fuzzy modeling will go a long way in resolving some of the conflict, divisiveness, and controversy in the current regulatory paradigm.« less

Tracking fuzzy borders using geodesic curves with application to liver segmentation on planning CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yuan, Yading, E-mail: yading.yuan@mssm.edu; Chao, Ming; Sheu, Ren-Dih

Purpose: This work aims to develop a robust and efficient method to track the fuzzy borders between liver and the abutted organs where automatic liver segmentation usually suffers, and to investigate its applications in automatic liver segmentation on noncontrast-enhanced planning computed tomography (CT) images. Methods: In order to track the fuzzy liver–chestwall and liver–heart borders where oversegmentation is often found, a starting point and an ending point were first identified on the coronal view images; the fuzzy border was then determined as a geodesic curve constructed by minimizing the gradient-weighted path length between these two points near the fuzzy border.more » The minimization of path length was numerically solved by fast-marching method. The resultant fuzzy borders were incorporated into the authors’ automatic segmentation scheme, in which the liver was initially estimated by a patient-specific adaptive thresholding and then refined by a geodesic active contour model. By using planning CT images of 15 liver patients treated with stereotactic body radiation therapy, the liver contours extracted by the proposed computerized scheme were compared with those manually delineated by a radiation oncologist. Results: The proposed automatic liver segmentation method yielded an average Dice similarity coefficient of 0.930 ± 0.015, whereas it was 0.912 ± 0.020 if the fuzzy border tracking was not used. The application of fuzzy border tracking was found to significantly improve the segmentation performance. The mean liver volume obtained by the proposed method was 1727 cm{sup 3}, whereas it was 1719 cm{sup 3} for manual-outlined volumes. The computer-generated liver volumes achieved excellent agreement with manual-outlined volumes with correlation coefficient of 0.98. Conclusions: The proposed method was shown to provide accurate segmentation for liver in the planning CT images where contrast agent is not applied. The authors’ results also clearly demonstrated that the application of tracking the fuzzy borders could significantly reduce contour leakage during active contour evolution.« less

An Implementation Method of the Fractional-Order PID Control System Considering the Memory Constraint and its Application to the Temperature Control of Heat Plate

NASA Astrophysics Data System (ADS)

Sasano, Koji; Okajima, Hiroshi; Matsunaga, Nobutomo

Recently, the fractional order PID (FO-PID) control, which is the extension of the PID control, has been focused on. Even though the FO-PID requires the high-order filter, it is difficult to realize the high-order filter due to the memory limitation of digital computer. For implementation of FO-PID, approximation of the fractional integrator and differentiator are required. Short memory principle (SMP) is one of the effective approximation methods. However, there is a disadvantage that the approximated filter with SMP cannot eliminate the steady-state error. For this problem, we introduce the distributed implementation of the integrator and the dynamic quantizer to make the efficient use of permissible memory. The objective of this study is to clarify how to implement the accurate FO-PID with limited memories. In this paper, we propose the implementation method of FO-PID with memory constraint using dynamic quantizer. And the trade off between approximation of fractional elements and quantized data size are examined so as to close to the ideal FO-PID responses. The effectiveness of proposed method is evaluated by numerical example and experiment in the temperature control of heat plate.

Pelvic Inflammatory Disease (PID)

MedlinePlus

... a serious condition, in women. 1 in 8 women with a history of PID experience difficulties getting pregnant. You can prevent PID if you know how to protect yourself. What is PID? Pelvic inflammatory disease is an infection of a woman’s reproductive organs. It is a complication often caused ...

Pneumatic artificial muscle actuators for compliant robotic manipulators

NASA Astrophysics Data System (ADS)

Robinson, Ryan Michael

Robotic systems are increasingly being utilized in applications that require interaction with humans. In order to enable safe physical human-robot interaction, light weight and compliant manipulation are desirable. These requirements are problematic for many conventional actuation systems, which are often heavy, and typically use high stiffness to achieve high performance, leading to large impact forces upon collision. However, pneumatic artificial muscles (PAMs) are actuators that can satisfy these safety requirements while offering power-to-weight ratios comparable to those of conventional actuators. PAMs are extremely lightweight actuators that produce force in response to pressurization. These muscles demonstrate natural compliance, but have a nonlinear force-contraction profile that complicates modeling and control. This body of research presents solutions to the challenges associated with the implementation of PAMs as actuators in robotic manipulators, particularly with regard to modeling, design, and control. An existing PAM force balance model was modified to incorporate elliptic end geometry and a hyper-elastic constitutive relationship, dramatically improving predictions of PAM behavior at high contraction. Utilizing this improved model, two proof-of-concept PAM-driven manipulators were designed and constructed; design features included parallel placement of actuators and a tendon-link joint design. Genetic algorithm search heuristics were employed to determine an optimal joint geometry; allowing a manipulator to achieve a desired torque profile while minimizing the required PAM pressure. Performance of the manipulators was evaluated in both simulation and experiment employing various linear and nonlinear control strategies. These included output feedback techniques, such as proportional-integral-derivative (PID) and fuzzy logic, a model-based control for computed torque, and more advanced controllers, such as sliding mode, adaptive sliding mode, and adaptive neural network control. Results demonstrated the benefits of an accurate model in model-based control, and the advantages of adaptive neural network control when a model is unavailable or variations in payload are expected. Lastly, a variable recruitment strategy was applied to a group of parallel muscles actuating a common joint. Increased manipulator efficiency was observed when fewer PAMs were activated, justifying the use of variable recruitment strategies. Overall, this research demonstrates the benefits of pneumatic artificial muscles as actuators in robotics applications. It demonstrates that PAM-based manipulators can be well-modeled and can achieve high tracking accuracy over a wide range of payloads and inputs while maintaining natural compliance.

The Personality Inventory for DSM-5 Short Form (PID-5-SF): psychometric properties and association with big five traits and pathological beliefs in a Norwegian population.

PubMed

Thimm, Jens C; Jordan, Stian; Bach, Bo

2016-12-07

With the publication of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), an alternative model for personality disorders based on personality dysfunction and pathological personality traits was introduced. The Personality Inventory for DSM-5 (PID-5) is a 220-item self-report inventory designed to assess the personality traits of this model. Recently, a short 100-item version of the PID-5 (PID-5-SF) has been developed. The aim of this study was to investigate the score reliability and structure of the Norwegian PID-5-SF. Further, criterion validity with the five factor model of personality (FFM) and pathological personality beliefs was examined. A derivation sample of university students (N = 503) completed the PID-5, the Big Five Inventory (BFI), and the Personality Beliefs Questionnaire - Short Form (PBQ-SF), whereas a replication sample of 127 students completed the PID-5-SF along with the aforementioned measures. The short PID-5 showed overall good score reliability and structural validity. The associations with FFM traits and pathological personality beliefs were conceptually coherent and similar for the two forms of the PID-5. The results suggest that the Norwegian PID-5 short form is a reliable and efficient measure of the trait criterion of the alternative model for personality disorders in DSM-5.

Reliability and validity of the personality inventory for DSM-5 (PID-5): predicting DSM-IV personality disorders and psychopathy in community-dwelling Italian adults.

PubMed

Fossati, Andrea; Krueger, Robert F; Markon, Kristian E; Borroni, Serena; Maffei, Cesare

2013-12-01

In order to assess the internal consistency, factor structure, and ability to recover DSM-IV personality disorders (PDs) of the Personality Inventory for DSM-5 (PID-5) scales, 710 Italian adult community dwelling volunteers were administered the Italian translation of the PID-5, as well as the Italian translation of the Personality Diagnostic Questionnaire-4+ (PDQ-4+). Cronbach's alpha values were >.70 for all PID-5 facet scales and greater than .90 for all PID-5 domain scales. Parallel analysis and confirmatory factor analysis supported the theoretical five-factor model of the PID-5 trait scales. Regression analyses showed that both PID-5 trait and domain scales explained a substantial amount of variance in the PDQ-4+ PD scales, with the exception of the Passive-Aggressive PD scale. When the PID-5 was administered to a second independent sample of 389 Italian adult community dwelling volunteers, the basic psychometric properties of the scale were replicated. In this second sample, the PID-5 trait and domain scales proved to be significant predictors of psychopathy measures. As a whole, the results of the present study support the hypothesis that the PID-5 is a reliable instrument which is able to recover DSM-IV PDs, as well as to capture personality pathology that is not included in the DSM-IV (namely, psychopathy).

Whole-Exome Sequencing to Identify Novel Biological Pathways Associated With Infertility After Pelvic Inflammatory Disease.

PubMed

Taylor, Brandie D; Zheng, Xiaojing; Darville, Toni; Zhong, Wujuan; Konganti, Kranti; Abiodun-Ojo, Olayinka; Ness, Roberta B; O'Connell, Catherine M; Haggerty, Catherine L

2017-01-01

Ideal management of sexually transmitted infections (STI) may require risk markers for pathology or vaccine development. Previously, we identified common genetic variants associated with chlamydial pelvic inflammatory disease (PID) and reduced fecundity. As this explains only a proportion of the long-term morbidity risk, we used whole-exome sequencing to identify biological pathways that may be associated with STI-related infertility. We obtained stored DNA from 43 non-Hispanic black women with PID from the PID Evaluation and Clinical Health Study. Infertility was assessed at a mean of 84 months. Principal component analysis revealed no population stratification. Potential covariates did not significantly differ between groups. Sequencing kernel association test was used to examine associations between aggregates of variants on a single gene and infertility. The results from the sequencing kernel association test were used to choose "focus genes" (P < 0.01; n = 150) for subsequent Ingenuity Pathway Analysis to identify "gene sets" that are enriched in biologically relevant pathways. Pathway analysis revealed that focus genes were enriched in canonical pathways including, IL-1 signaling, P2Y purinergic receptor signaling, and bone morphogenic protein signaling. Focus genes were enriched in pathways that impact innate and adaptive immunity, protein kinase A activity, cellular growth, and DNA repair. These may alter host resistance or immunopathology after infection. Targeted sequencing of biological pathways identified in this study may provide insight into STI-related infertility.

Impact of PID on industrial rooftop PV-installations

NASA Astrophysics Data System (ADS)

Buerhop, Claudia; Fecher, Frank W.; Pickel, Tobias; Patel, Tirth; Zetzmann, Cornelia; Camus, Christian; Hauch, Jens; Brabec, Christoph J.

2017-08-01

Potential induced degradation (PID) causes severe damage and financial losses even in modern PV-installations. In Germany, approximately 19% of PV-installations suffer from PID and resulting power loss. This paper focuses on the impact of PID in real installations and how different evaluated time intervals influence the performance ratio (PR) and the determined degradation rate. The analysis focuses exemplarily on a 314 kWp PV-system in the Atlantic coastal climate. IR-imaging is used for identifying PID without operation interruption. Historic electric performance data are available from a monitoring system for several years on system level, string level as well as punctually measured module string IV- curves. The data sets are combined for understanding the PID behavior of this PV plant. The number of PID affected cells within a string varies strongly between 1 to 22% with the string position on the building complex. With increasing number of PID-affected cells the performance ratio decreases down to 60% for daily and monthly periods. Local differences in PID evolution rates are identified. An average PR-reduction of -3.65% per year is found for the PV-plant. On the string level the degradation rate varied up to 8.8% per year depending on the string position and the time period. The analysis reveals that PID generation and evolution in roof-top installations on industrial buildings with locally varying operation conditions can be fairly complex. The results yield that local operating conditions, e.g. ambient weather conditions as well as surrounding conditions on an industrial building, seem to have a dominating impact on the PID evolution rate.

Connes distance function on fuzzy sphere and the connection between geometry and statistics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Devi, Yendrembam Chaoba, E-mail: chaoba@bose.res.in; Chakraborty, Biswajit, E-mail: biswajit@bose.res.in; Prajapat, Shivraj, E-mail: shraprajapat@gmail.com

An algorithm to compute Connes spectral distance, adaptable to the Hilbert-Schmidt operatorial formulation of non-commutative quantum mechanics, was developed earlier by introducing the appropriate spectral triple and used to compute infinitesimal distances in the Moyal plane, revealing a deep connection between geometry and statistics. In this paper, using the same algorithm, the Connes spectral distance has been calculated in the Hilbert-Schmidt operatorial formulation for the fuzzy sphere whose spatial coordinates satisfy the su(2) algebra. This has been computed for both the discrete and the Perelemov’s SU(2) coherent state. Here also, we get a connection between geometry and statistics which ismore » shown by computing the infinitesimal distance between mixed states on the quantum Hilbert space of a particular fuzzy sphere, indexed by n ∈ ℤ/2.« less

Prediction on carbon dioxide emissions based on fuzzy rules

NASA Astrophysics Data System (ADS)

Pauzi, Herrini; Abdullah, Lazim

2014-06-01

There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

Analysis and Synthesis of Memory-Based Fuzzy Sliding Mode Controllers.

PubMed

Zhang, Jinhui; Lin, Yujuan; Feng, Gang

2015-12-01

This paper addresses the sliding mode control problem for a class of Takagi-Sugeno fuzzy systems with matched uncertainties. Different from the conventional memoryless sliding surface, a memory-based sliding surface is proposed which consists of not only the current state but also the delayed state. Both robust and adaptive fuzzy sliding mode controllers are designed based on the proposed memory-based sliding surface. It is shown that the sliding surface can be reached and the closed-loop control system is asymptotically stable. Furthermore, to reduce the chattering, some continuous sliding mode controllers are also presented. Finally, the ball and beam system is used to illustrate the advantages and effectiveness of the proposed approaches. It can be seen that, with the proposed control approaches, not only can the stability be guaranteed, but also its transient performance can be improved significantly.

Multi-objective Optimization Design of Gear Reducer Based on Adaptive Genetic Algorithms

NASA Astrophysics Data System (ADS)

Li, Rui; Chang, Tian; Wang, Jianwei; Wei, Xiaopeng; Wang, Jinming

2008-11-01

An adaptive Genetic Algorithm (GA) is introduced to solve the multi-objective optimized design of the reducer. Firstly, according to the structure, strength, etc. in a reducer, a multi-objective optimized model of the helical gear reducer is established. And then an adaptive GA based on a fuzzy controller is introduced, aiming at the characteristics of multi-objective, multi-parameter, multi-constraint conditions. Finally, a numerical example is illustrated to show the advantages of this approach and the effectiveness of an adaptive genetic algorithm used in optimized design of a reducer.

Finite-Time Adaptive Control for a Class of Nonlinear Systems With Nonstrict Feedback Structure.

PubMed

Sun, Yumei; Chen, Bing; Lin, Chong; Wang, Honghong

2017-09-18

This paper focuses on finite-time adaptive neural tracking control for nonlinear systems in nonstrict feedback form. A semiglobal finite-time practical stability criterion is first proposed. Correspondingly, the finite-time adaptive neural control strategy is given by using this criterion. Unlike the existing results on adaptive neural/fuzzy control, the proposed adaptive neural controller guarantees that the tracking error converges to a sufficiently small domain around the origin in finite time, and other closed-loop signals are bounded. At last, two examples are used to test the validity of our results.

A universal heliostat control system

NASA Astrophysics Data System (ADS)

Gross, Fabian; Geiger, Mark; Buck, Reiner

2017-06-01

This paper describes the development of a universal heliostat control system as part of the AutoR project [1]. The system can control multiple receivers and heliostat types in a single application. The system offers support for multiple operators on different machines and is designed to be as adaptive as possible. Thus, the system can be used for different heliostat field setups with only minor adaptations of the system's source code. This is achieved by extensive usage of modern programming techniques like reflection and dependency injection. Furthermore, the system features co-simulation of a ray tracer, a reference PID-controller implementation for open volumetric receivers and methods for heliostat calibration and monitoring.

Defect inspection in hot slab surface: multi-source CCD imaging based fuzzy-rough sets method

NASA Astrophysics Data System (ADS)

Zhao, Liming; Zhang, Yi; Xu, Xiaodong; Xiao, Hong; Huang, Chao

2016-09-01

To provide an accurate surface defects inspection method and make the automation of robust image region of interests(ROI) delineation strategy a reality in production line, a multi-source CCD imaging based fuzzy-rough sets method is proposed for hot slab surface quality assessment. The applicability of the presented method and the devised system are mainly tied to the surface quality inspection for strip, billet and slab surface etcetera. In this work we take into account the complementary advantages in two common machine vision (MV) systems(line array CCD traditional scanning imaging (LS-imaging) and area array CCD laser three-dimensional (3D) scanning imaging (AL-imaging)), and through establishing the model of fuzzy-rough sets in the detection system the seeds for relative fuzzy connectedness(RFC) delineation for ROI can placed adaptively, which introduces the upper and lower approximation sets for RIO definition, and by which the boundary region can be delineated by RFC region competitive classification mechanism. For the first time, a Multi-source CCD imaging based fuzzy-rough sets strategy is attempted for CC-slab surface defects inspection that allows an automatic way of AI algorithms and powerful ROI delineation strategies to be applied to the MV inspection field.

A new intuitionism: Meaning, memory, and development in Fuzzy-Trace Theory

PubMed Central

Reyna, Valerie F.

2014-01-01

Combining meaning, memory, and development, the perennially popular topic of intuition can be approached in a new way. Fuzzy-trace theory integrates these topics by distinguishing between meaning-based gist representations, which support fuzzy (yet advanced) intuition, and superficial verbatim representations of information, which support precise analysis. Here, I review the counterintuitive findings that led to the development of the theory and its most recent extensions to the neuroscience of risky decision making. These findings include memory interference (worse verbatim memory is associated with better reasoning); nonnumerical framing (framing effects increase when numbers are deleted from decision problems); developmental decreases in gray matter and increases in brain connectivity; developmental reversals in memory, judgment, and decision making (heuristics and biases based on gist increase from childhood to adulthood, challenging conceptions of rationality); and selective attention effects that provide critical tests comparing fuzzy-trace theory, expected utility theory, and its variants (e.g., prospect theory). Surprising implications for judgment and decision making in real life are also discussed, notably, that adaptive decision making relies mainly on gist-based intuition in law, medicine, and public health. PMID:25530822

Airline Passenger Profiling Based on Fuzzy Deep Machine Learning.

PubMed

Zheng, Yu-Jun; Sheng, Wei-Guo; Sun, Xing-Ming; Chen, Sheng-Yong

2017-12-01

Passenger profiling plays a vital part of commercial aviation security, but classical methods become very inefficient in handling the rapidly increasing amounts of electronic records. This paper proposes a deep learning approach to passenger profiling. The center of our approach is a Pythagorean fuzzy deep Boltzmann machine (PFDBM), whose parameters are expressed by Pythagorean fuzzy numbers such that each neuron can learn how a feature affects the production of the correct output from both the positive and negative sides. We propose a hybrid algorithm combining a gradient-based method and an evolutionary algorithm for training the PFDBM. Based on the novel learning model, we develop a deep neural network (DNN) for classifying normal passengers and potential attackers, and further develop an integrated DNN for identifying group attackers whose individual features are insufficient to reveal the abnormality. Experiments on data sets from Air China show that our approach provides much higher learning ability and classification accuracy than existing profilers. It is expected that the fuzzy deep learning approach can be adapted for a variety of complex pattern analysis tasks.

A new intuitionism: Meaning, memory, and development in Fuzzy-Trace Theory.

PubMed

Reyna, Valerie F

2012-05-01

Combining meaning, memory, and development, the perennially popular topic of intuition can be approached in a new way. Fuzzy-trace theory integrates these topics by distinguishing between meaning-based gist representations, which support fuzzy (yet advanced) intuition, and superficial verbatim representations of information, which support precise analysis. Here, I review the counterintuitive findings that led to the development of the theory and its most recent extensions to the neuroscience of risky decision making. These findings include memory interference (worse verbatim memory is associated with better reasoning); nonnumerical framing (framing effects increase when numbers are deleted from decision problems); developmental decreases in gray matter and increases in brain connectivity; developmental reversals in memory, judgment, and decision making (heuristics and biases based on gist increase from childhood to adulthood, challenging conceptions of rationality); and selective attention effects that provide critical tests comparing fuzzy-trace theory, expected utility theory, and its variants (e.g., prospect theory). Surprising implications for judgment and decision making in real life are also discussed, notably, that adaptive decision making relies mainly on gist-based intuition in law, medicine, and public health.

Pelvic inflammatory disease and sepsis.

PubMed

Dulin, Judy D; Akers, Mary C

2003-03-01

Pelvic inflammatory disease affects approximately 1 million women per year in the United States alone and has a variety of causative organisms. Because the diagnosis of PID is based on clinical judgment, health care providers need to be guided by the CDC recommendations for diagnosing and treating PID. Because presenting symptoms are often vague, the health care provider should assess female patients for risky behaviors that may lead to PID and should use screening data when making clinical judgments and differential diagnoses. Whenever possible, female patients with PID should be treated as outpatients. If diagnosis and treatment are not performed in a timely manner, PID may cause sepsis, septic shock, and even death. Even if they survive, as many as 15% to 20% of these women experience long-term sequelae of PID, such as ectopic pregnancy, tubo-ovarian abscess, infertility, dyspareunia, and chronic pelvic pain. The best treatments for PID are interventions that lead to prevention and early detection. The critical care nurse has an important role in recognizing the variables that may lead to PID-related sepsis and in encouraging health-seeking and health-maintenance behaviors among women with these diagnoses.

The Construct Validity of the Dutch Personality Inventory for DSM-5 Personality Disorders (PID-5) in a Clinical Sample.

PubMed

Bastiaens, Tim; Claes, Laurence; Smits, Dirk; De Clercq, Barbara; De Fruyt, Filip; Rossi, Gina; Vanwalleghem, Dominique; Vermote, Rudi; Lowyck, Benedicte; Claes, Stephan; De Hert, Marc

2016-02-01

The factor structure and the convergent validity of the Personality Inventory for DSM-5 (PID-5), a self-report questionnaire designed to measure personality pathology as advocated in the fifth edition, Section III of Diagnostic and Statistical Manual of Mental Disorders (DSM-5), are already demonstrated in general population samples, but need replication in clinical samples. In 240 Flemish inpatients, we examined the factor structure of the PID-5 by means of exploratory structural equation modeling. Additionally, we investigated differences in PID-5 higher order domain scores according to gender, age and educational level, and explored convergent and discriminant validity by relating the PID-5 with the Dimensional Assessment of Personality Pathology-Basic Questionnaire and by comparing PID-5 scores of inpatients with and without a DSM-IV categorical personality disorder diagnosis. Our results confirmed the original five-factor structure of the PID-5. The reliability and the convergent and discriminant validity of the PID-5 proved to be adequate. Implications for future research are discussed. © The Author(s) 2015.

IUD users may have higher risk of contracting PID, studies find; pill may have protective effect.

PubMed

1980-01-01

The incidence of pelvic inflammatory disease (PID), which can lead to serious reproductive impairment, infertility, or ectopic pregnancy, is increasing worldwide. About 500,000 women in their childbearing years contract PID for the first time each year in the U.S., and the incidence among young women appears to be increasing. Sexually transmitted diseases are responsible for many PID cases worldwide, while in developing countries postpartum and postabortion sepsis and endemic diseases such as filariasis and schistosomiasis may also be implicated. Some increase of PID is associated with legal abortion and IUD insertion, while pill use appears to protect women from PID. A worldwide review of medical literature indicates that the risk of PID is 3.5 times greater for IUD users than for nonusers, although several variables other than IUD use affect PID risk, such as age, race, socioeconomic status, and sexual activity. The risk for never pregnant IUD users in a Swedish study was 11.8 per 100 woman years, compared to 3.4 for ever-pregnant IUD users. There was no difference in relative risk for users of the pill or barrier methods, or for non-contraceptors. Another study reported a higher relative risk of PID related to duration of IUD use.

Risk of psychiatric disorders following pelvic inflammatory disease: a nationwide population-based retrospective cohort study.

PubMed

Shen, Cheng-Che; Yang, Albert C; Hung, Jeng-Hsiu; Hu, Li-Yu; Chiang, Yung-Yen; Tsai, Shih-Jen

2016-01-01

Pelvic inflammatory disease (PID) a common infection in women that is associated with significant morbidity and is a major cause of infertility. A clear temporal causal relationship between PID and psychiatric disorders has not been well established. We used a nationwide population-based retrospective cohort study to explore the relationship between PID and the subsequent development of psychiatric disorders. We identified subjects who were newly diagnosed with PID between 1 January 2000 and 31 December 2002 in the Taiwan National Health Insurance Research Database. A comparison cohort was constructed for patients without PID. A total of 21 930 PID and 21 930 matched control patients were observed until diagnosed with psychiatric disorders, or until death, withdrawal from the NHI system, or until 31 December 2009. Adjusted hazard ratio (HR) of bipolar disorder, depressive disorder, anxiety disorder and sleep disorder in subjects with PID were significantly higher (HR: 2.671, 2.173, 2.006 and 2.251, respectively) than that of the controls during the follow-up. PID may increase the risk of subsequent newly diagnosed bipolar disorder, depressive disorder, anxiety disorder and sleep disorder, which will impair life quality. Our findings highlight that clinicians should pay particular attention to psychiatric comorbidities in PID patients.

Analysis of Bioactive Amino Acids from Fish Hydrolysates with a New Bioinformatic Intelligent System Approach.

PubMed

Elaziz, Mohamed Abd; Hemdan, Ahmed Monem; Hassanien, AboulElla; Oliva, Diego; Xiong, Shengwu

2017-09-07

The current economics of the fish protein industry demand rapid, accurate and expressive prediction algorithms at every step of protein production especially with the challenge of global climate change. This help to predict and analyze functional and nutritional quality then consequently control food allergies in hyper allergic patients. As, it is quite expensive and time-consuming to know these concentrations by the lab experimental tests, especially to conduct large-scale projects. Therefore, this paper introduced a new intelligent algorithm using adaptive neuro-fuzzy inference system based on whale optimization algorithm. This algorithm is used to predict the concentration levels of bioactive amino acids in fish protein hydrolysates at different times during the year. The whale optimization algorithm is used to determine the optimal parameters in adaptive neuro-fuzzy inference system. The results of proposed algorithm are compared with others and it is indicated the higher performance of the proposed algorithm.

A zero phase adaptive fuzzy Kalman filter for physiological tremor suppression in robotically assisted minimally invasive surgery.

PubMed

Sang, Hongqiang; Yang, Chenghao; Liu, Fen; Yun, Jintian; Jin, Guoguang; Chen, Fa

2016-12-01

Hand physiological tremor of surgeons can cause vibration at the surgical instrument tip, which may make it difficult for the surgeon to perform fine manipulations of tissue, needles, and sutures. A zero phase adaptive fuzzy Kalman filter (ZPAFKF) is proposed to suppress hand tremor and vibration of a robotic surgical system. The involuntary motion can be reduced by adding a compensating signal that has the same magnitude and frequency but opposite phase with the tremor signal. Simulations and experiments using different filters were performed. Results show that the proposed filter can avoid the loss of useful motion information and time delay, and better suppress minor and varying tremor. The ZPAFKF can provide less error, preferred accuracy, better tremor estimation, and more desirable compensation performance, to suppress hand tremor and decrease vibration at the surgical instrument tip. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Application of fuzzy adaptive control to a MIMO nonlinear time-delay pump-valve system.

PubMed

Lai, Zhounian; Wu, Peng; Wu, Dazhuan

2015-07-01

In this paper, a control strategy to balance the reliability against efficiency is introduced to overcome the common off-design operation problem in pump-valve systems. The pump-valve system is a nonlinear multi-input-multi-output (MIMO) system with time delays which cannot be accurately measured but can be approximately modeled using Bernoulli Principle. A fuzzy adaptive controller is applied to approximate system parameters and achieve the control of delay-free model since the system model is inaccurate and the direct feedback linearization method cannot be applied. An extended Smith predictor is introduced to compensate time delays of the system using the inaccurate system model. The experiment is carried out to verify the effectiveness of the control strategy whose results show that the control performance is well achieved. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Analysis prediction of Indonesian banks (BCA, BNI, MANDIRI) using adaptive neuro-fuzzy inference system (ANFIS) and investment strategies

NASA Astrophysics Data System (ADS)

Trianto, Andriantama Budi; Hadi, I. M.; Liong, The Houw; Purqon, Acep

2015-09-01

Indonesian economical development is growing well. It has effect for their invesment in Banks and the stock market. In this study, we perform prediction for the three blue chips of Indonesian bank i.e. BCA, BNI, and MANDIRI by using the method of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Takagi-Sugeno rules and Generalized bell (Gbell) as the membership function. Our results show that ANFIS perform good prediction with RMSE for BCA of 27, BNI of 5.29, and MANDIRI of 13.41, respectively. Furthermore, we develop an active strategy to gain more benefit. We compare between passive strategy versus active strategy. Our results shows that for the passive strategy gains 13 million rupiah, while for the active strategy gains 47 million rupiah in one year. The active investment strategy significantly shows gaining multiple benefit than the passive one.

Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages

NASA Astrophysics Data System (ADS)

Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.

2013-09-01

This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.

Prediction of ultrasonic pulse velocity for enhanced peat bricks using adaptive neuro-fuzzy methodology.

PubMed

Motamedi, Shervin; Roy, Chandrabhushan; Shamshirband, Shahaboddin; Hashim, Roslan; Petković, Dalibor; Song, Ki-Il

2015-08-01

Ultrasonic pulse velocity is affected by defects in material structure. This study applied soft computing techniques to predict the ultrasonic pulse velocity for various peats and cement content mixtures for several curing periods. First, this investigation constructed a process to simulate the ultrasonic pulse velocity with adaptive neuro-fuzzy inference system. Then, an ANFIS network with neurons was developed. The input and output layers consisted of four and one neurons, respectively. The four inputs were cement, peat, sand content (%) and curing period (days). The simulation results showed efficient performance of the proposed system. The ANFIS and experimental results were compared through the coefficient of determination and root-mean-square error. In conclusion, use of ANFIS network enhances prediction and generation of strength. The simulation results confirmed the effectiveness of the suggested strategies. Copyright © 2015 Elsevier B.V. All rights reserved.

Utility of the Personality Inventory for DSM-5-Brief Form (PID-5-BF) in the Measurement of Maladaptive Personality and Psychopathology.

PubMed

Anderson, Jaime L; Sellbom, Martin; Salekin, Randall T

2018-07-01

The Diagnostic and Statistical Manual of Mental Disorders-Fifth edition ( DSM-5) Personality and Personality Disorders workgroup developed the Personality Inventory for the DSM-5 (PID-5) for the assessment of the alternative trait model for DSM-5. Along with this measure, the American Psychiatric Association published an abbreviated version, the PID-5-Brief form (PID-5-BF). Although this measure is available on the DSM-5 website for use, only two studies have evaluated its psychometric properties and validity and no studies have examined the U.S. version of this measure. The current study evaluated the reliability, factor structure, and construct validity of PID-5-BF scale scores. This included an evaluation of the scales' associations with Section II PDs, a well-validated dimensional measure of personality psychopathology, and broad externalizing and internalizing psychopathology measures. We found support for the reliability of PID-5-BF scales as well as for the factor structure of the measure. Furthermore, a series of correlation and regression analyses showed conceptually expected associations between PID-5-BF and external criterion variables. Finally, we compared the correlations with external criterion measures to those of the full-length PID-5 and PID-5-Short form. Intraclass correlation analyses revealed a comparable pattern of correlations across all three measures, thereby supporting the use of the PID-5-BF as a screening measure of dimensional maladaptive personality traits.

Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Azmi, Nur Iffah Mohamed; Arifin Mat Piah, Kamal; Yusoff, Wan Azhar Wan; Romlay, Fadhlur Rahman Mohd

2018-03-01

Controller that uses PID parameters requires a good tuning method in order to improve the control system performance. Tuning PID control method is divided into two namely the classical methods and the methods of artificial intelligence. Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. This research aims to improve the PSO-PID tuning algorithms by integrating the tuning process with the Variable Weight Grey- Taguchi Design of Experiment (DOE) method. This is done by conducting the DOE on the two PSO optimizing parameters: the particle velocity limit and the weight distribution factor. Computer simulations and physical experiments were conducted by using the proposed PSO- PID with the Variable Weight Grey-Taguchi DOE and the classical Ziegler-Nichols methods. They are implemented on the hydraulic positioning system. Simulation results show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE has reduced the rise time by 48.13% and settling time by 48.57% compared to the Ziegler-Nichols method. Furthermore, the physical experiment results also show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE tuning method responds better than Ziegler-Nichols tuning. In conclusion, this research has improved the PSO-PID parameter by applying the PSO-PID algorithm together with the Variable Weight Grey-Taguchi DOE method as a tuning method in the hydraulic positioning system.

A new fuzzy sliding mode controller for vibration control systems using integrated-structure smart dampers

NASA Astrophysics Data System (ADS)

Dzung Nguyen, Sy; Kim, Wanho; Park, Jhinha; Choi, Seung-Bok

2017-04-01

Vibration control systems using smart dampers (SmDs) such as magnetorheological and electrorheological dampers (MRD and ERD), which are classified as the integrated structure-SmD control systems (ISSmDCSs), have been actively researched and widely used. This work proposes a new controller for a class of ISSmDCSs in which high accuracy of SmD models as well as increment of control ability to deal with uncertainty and time delay are to be expected. In order to achieve this goal, two formualtion steps are required; a non-parametric SmD model based on an adaptive neuro-fuzzy inference system (ANFIS) and a novel fuzzy sliding mode controller (FSMC) which can weaken the model error of the ISSmDCSs and hence provide enhanced vibration control performances. As for the formulation of the proposed controller, first, an ANFIS controller is desgned to identify SmDs using the improved control algorithm named improved establishing neuro-fuzzy system (establishing neuro-fuzzy system). Second, a new control law for the FSMC is designed via Lyapunov stability analysis. An application to a semi-active MRD vehicle suspension system is then undertaken to illustrate and evaluate the effectiveness of the proposed control method. It is demonstrated through an experimental realization that the FSMC proposed in this work shows superior vibration control performance of the vehicle suspension compared to other surveyed controller which have similar structures to the FSMC, such as fuzzy logic and sliding mode control.

Quick fuzzy backpropagation algorithm.

PubMed

Nikov, A; Stoeva, S

2001-03-01

A modification of the fuzzy backpropagation (FBP) algorithm called QuickFBP algorithm is proposed, where the computation of the net function is significantly quicker. It is proved that the FBP algorithm is of exponential time complexity, while the QuickFBP algorithm is of polynomial time complexity. Convergence conditions of the QuickFBP, resp. the FBP algorithm are defined and proved for: (1) single output neural networks in case of training patterns with different targets; and (2) multiple output neural networks in case of training patterns with equivalued target vector. They support the automation of the weights training process (quasi-unsupervised learning) establishing the target value(s) depending on the network's input values. In these cases the simulation results confirm the convergence of both algorithms. An example with a large-sized neural network illustrates the significantly greater training speed of the QuickFBP rather than the FBP algorithm. The adaptation of an interactive web system to users on the basis of the QuickFBP algorithm is presented. Since the QuickFBP algorithm ensures quasi-unsupervised learning, this implies its broad applicability in areas of adaptive and adaptable interactive systems, data mining, etc. applications.

Adaptive neuro fuzzy inference system-based power estimation method for CMOS VLSI circuits

NASA Astrophysics Data System (ADS)

Vellingiri, Govindaraj; Jayabalan, Ramesh

2018-03-01

Recent advancements in very large scale integration (VLSI) technologies have made it feasible to integrate millions of transistors on a single chip. This greatly increases the circuit complexity and hence there is a growing need for less-tedious and low-cost power estimation techniques. The proposed work employs Back-Propagation Neural Network (BPNN) and Adaptive Neuro Fuzzy Inference System (ANFIS), which are capable of estimating the power precisely for the complementary metal oxide semiconductor (CMOS) VLSI circuits, without requiring any knowledge on circuit structure and interconnections. The ANFIS to power estimation application is relatively new. Power estimation using ANFIS is carried out by creating initial FIS modes using hybrid optimisation and back-propagation (BP) techniques employing constant and linear methods. It is inferred that ANFIS with the hybrid optimisation technique employing the linear method produces better results in terms of testing error that varies from 0% to 0.86% when compared to BPNN as it takes the initial fuzzy model and tunes it by means of a hybrid technique combining gradient descent BP and mean least-squares optimisation algorithms. ANFIS is the best suited for power estimation application with a low RMSE of 0.0002075 and a high coefficient of determination (R) of 0.99961.

An intelligent algorithm for identification of optimum mix of demographic features for trust in medical centers in Iran.

PubMed

Yazdanparast, R; Zadeh, S Abdolhossein; Dadras, D; Azadeh, A

2018-06-01

Healthcare quality is affected by various factors including trust. Patients' trust to healthcare providers is one of the most important factors for treatment outcomes. The presented study identifies optimum mixture of patient demographic features with respect to trust in three large and busy medical centers in Tehran, Iran. The presented algorithm is composed of adaptive neuro-fuzzy inference system and statistical methods. It is used to deal with data and environmental uncertainty. The required data are collected from three large hospitals using standard questionnaires. The reliability and validity of the collected data is evaluated using Cronbach's Alpha, factor analysis and statistical tests. The results of this study indicate that middle age patients with low level of education and moderate illness severity and young patients with high level of education, moderate illness severity and moderate to weak financial status have the highest trust to the considered medical centers. To the best of our knowledge this the first study that investigates patient demographic features using adaptive neuro-fuzzy inference system in healthcare sector. Second, it is a practical approach for continuous improvement of trust features in medical centers. Third, it deals with the existing uncertainty through the unique neuro-fuzzy approach. Copyright © 2018 Elsevier B.V. All rights reserved.

Relative Wave Energy based Adaptive Neuro-Fuzzy Inference System model for the Estimation of Depth of Anaesthesia.

PubMed

Benzy, V K; Jasmin, E A; Koshy, Rachel Cherian; Amal, Frank; Indiradevi, K P

2018-01-01

The advancement in medical research and intelligent modeling techniques has lead to the developments in anaesthesia management. The present study is targeted to estimate the depth of anaesthesia using cognitive signal processing and intelligent modeling techniques. The neurophysiological signal that reflects cognitive state of anaesthetic drugs is the electroencephalogram signal. The information available on electroencephalogram signals during anaesthesia are drawn by extracting relative wave energy features from the anaesthetic electroencephalogram signals. Discrete wavelet transform is used to decomposes the electroencephalogram signals into four levels and then relative wave energy is computed from approximate and detail coefficients of sub-band signals. Relative wave energy is extracted to find out the degree of importance of different electroencephalogram frequency bands associated with different anaesthetic phases awake, induction, maintenance and recovery. The Kruskal-Wallis statistical test is applied on the relative wave energy features to check the discriminating capability of relative wave energy features as awake, light anaesthesia, moderate anaesthesia and deep anaesthesia. A novel depth of anaesthesia index is generated by implementing a Adaptive neuro-fuzzy inference system based fuzzy c-means clustering algorithm which uses relative wave energy features as inputs. Finally, the generated depth of anaesthesia index is compared with a commercially available depth of anaesthesia monitor Bispectral index.

Adaptive neuro-fuzzy inference systems for semi-automatic discrimination between seismic events: a study in Tehran region

NASA Astrophysics Data System (ADS)

Vasheghani Farahani, Jamileh; Zare, Mehdi; Lucas, Caro

2012-04-01

Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289 signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are almost 223 earthquakes with M ≤ 2.2 included in this database. Data sets from the south, east, and southeast of the city of Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results confirmed that the proposed ANFIS model has good potential for determining seismic events.

Urban Growth Modeling Using Anfis Algorithm: a Case Study for Sanandaj City, Iran

NASA Astrophysics Data System (ADS)

Mohammady, S.; Delavar, M. R.; Pijanowski, B. C.

2013-10-01

Global urban population has increased from 22.9% in 1985 to 47% in 2010. In spite of the tendency for urbanization worldwide, only about 2% of Earth's land surface is covered by cities. Urban population in Iran is increasing due to social and economic development. The proportion of the population living in Iran urban areas has consistently increased from about 31% in 1956 to 68.4% in 2006. Migration of the rural population to cities and population growth in cities have caused many problems, such as irregular growth of cities, improper placement of infrastructure and urban services. Air and environmental pollution, resource degradation and insufficient infrastructure, are the results of poor urban planning that have negative impact on the environment or livelihoods of people living in cities. These issues are a consequence of improper land use planning. Models have been employed to assist in our understanding of relations between land use and its subsequent effects. Different models for urban growth modeling have been developed. Methods from computational intelligence have made great contributions in all specific application domains and hybrid algorithms research as a part of them has become a big trend in computational intelligence. Artificial Neural Network (ANN) has the capability to deal with imprecise data by training, while fuzzy logic can deal with the uncertainty of human cognition. ANN learns from scratch by adjusting the interconnections between layers and Fuzzy Inference Systems (FIS) is a popular computing framework based on the concept of fuzzy set theory, fuzzy logic, and fuzzy reasoning. Fuzzy logic has many advantages such as flexibility and at the other sides, one of the biggest problems in fuzzy logic application is the location and shape and of membership function for each fuzzy variable which is generally being solved by trial and error method. In contrast, numerical computation and learning are the advantages of neural network, however, it is not easy to obtain the optimal structure. Since, in this type of fuzzy logic, neural network has been used, therefore, by using a learning algorithm the parameters have been changed until reach the optimal solution. Adaptive Neuro Fuzzy Inference System (ANFIS) computing due to ability to understand nonlinear structures is a popular framework for solving complex problems. Fusion of ANN and FIS has attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. In this research, an ANFIS method has been developed for modeling land use change and interpreting the relationship between the drivers of urbanization. Our study area is the city of Sanandaj located in the west of Iran. Landsat images acquired in 2000 and 2006 have been used for model development and calibration. The parameters used in this study include distance to major roads, distance to residential regions, elevation, number of urban pixels in a 3 by 3 neighborhood and distance to green space. Percent Correct Match (PCM) and Figure of Merit were used to assess model goodness of fit were 93.77% and 64.30%, respectively.

Power-Constrained Fuzzy Logic Control of Video Streaming over a Wireless Interconnect

NASA Astrophysics Data System (ADS)

Razavi, Rouzbeh; Fleury, Martin; Ghanbari, Mohammed

2008-12-01

Wireless communication of video, with Bluetooth as an example, represents a compromise between channel conditions, display and decode deadlines, and energy constraints. This paper proposes fuzzy logic control (FLC) of automatic repeat request (ARQ) as a way of reconciling these factors, with a 40% saving in power in the worst channel conditions from economizing on transmissions when channel errors occur. Whatever the channel conditions are, FLC is shown to outperform the default Bluetooth scheme and an alternative Bluetooth-adaptive ARQ scheme in terms of reduced packet loss and delay, as well as improved video quality.

A digital optical phase-locked loop for diode lasers based on field programmable gate array

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu Zhouxiang; Zhang Xian; Huang Kaikai

2012-09-15

We have designed and implemented a highly digital optical phase-locked loop (OPLL) for diode lasers in atom interferometry. The three parts of controlling circuit in this OPLL, including phase and frequency detector (PFD), loop filter and proportional integral derivative (PID) controller, are implemented in a single field programmable gate array chip. A structure type compatible with the model MAX9382/MCH12140 is chosen for PFD and pipeline and parallelism technology have been adapted in PID controller. Especially, high speed clock and twisted ring counter have been integrated in the most crucial part, the loop filter. This OPLL has the narrow beat notemore » line width below 1 Hz, residual mean-square phase error of 0.14 rad{sup 2} and transition time of 100 {mu}s under 10 MHz frequency step. A main innovation of this design is the completely digitalization of the whole controlling circuit in OPLL for diode lasers.« less

The application of neural network PID controller to control the light gasoline etherification

NASA Astrophysics Data System (ADS)

Cheng, Huanxin; Zhang, Yimin; Kong, Lingling; Meng, Xiangyong

2017-06-01

Light gasoline etherification technology can effectively improve the quality of gasoline, which is environmental- friendly and economical. By combining BP neural network and PID control and using BP neural network self-learning ability for online parameter tuning, this method optimizes the parameters of PID controller and applies this to the Fcc gas flow control to achieve the control of the final product- heavy oil concentration. Finally, through MATLAB simulation, it is found that the PID control based on BP neural network has better controlling effect than traditional PID control.

Design and implementation of a 2-DOF PID compensation for magnetic levitation systems.

PubMed

Ghosh, Arun; Rakesh Krishnan, T; Tejaswy, Pailla; Mandal, Abhisek; Pradhan, Jatin K; Ranasingh, Subhakant

2014-07-01

This paper employs a 2-DOF (degree of freedom) PID controller for compensating a physical magnetic levitation system. It is shown that because of having a feedforward gain in the proposed 2-DOF PID control, the transient performance of the compensated system can be changed in a desired manner unlike the conventional 1-DOF PID control. It is also shown that for a choice of PID parameters, although the theoretical loop robustness is the same for both the compensated systems, in real-time, 2-DOF PID control may provide superior robustness if a suitable choice of the feedforward parameter is made. The results are verified through simulations and experiments. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Temperature and melt solid interface control during crystal growth

NASA Technical Reports Server (NTRS)

Batur, Celal

1990-01-01

Findings on the adaptive control of a transparent Bridgman crystal growth furnace are summarized. The task of the process controller is to establish a user specified axial temperature profile by controlling the temperatures in eight heating zones. The furnace controller is built around a computer. Adaptive PID (Proportional Integral Derivative) and Pole Placement control algorithms are applied. The need for adaptive controller stems from the fact that the zone dynamics changes with respect to time. The controller was tested extensively on the Lead Bromide crystal growth. Several different temperature profiles and ampoule's translational rates are tried. The feasibility of solid liquid interface quantification by image processing was determined. The interface is observed by a color video camera and the image data file is processed to determine if the interface is flat, convex or concave.

Moderately virulent African swine fever virus infection: blood cell changes and infective virus distribution among blood components.

PubMed

Genovesi, E V; Knudsen, R C; Whyard, T C; Mebus, C A

1988-03-01

Blood samples of pigs infected with a moderately virulent African swine fever virus (ASFV) isolate, obtained from the Dominican Republic (DR-II), were monitored temporally for viremia, infective ASFV association with major blood components, differential changes in blood cell composition, and plasma antibodies to ASFV. After intranasal/oral virus inoculation, pigs underwent acute infection and illness that resolved. Acute illness began on postinoculation day (PID) 4 and continued to PID 11, and pigs were febrile, with maximal infective ASFV titers detected in blood. By PID 11, initial antibody titers to ASFV antigens were detected in plasma. The WBC numbers were maintained near preinoculation counts; however, lymphocyte counts decreased slightly with a compensatory increment in neutrophil and monocyte numbers. From PID 11 to PID 25, rectal temperatures gradually returned to preinoculation values, titers of viremia began to decrease, plasma antibody to ASFV antigens increased to peak titers, and WBC numbers increased slightly. Percentages of lymphocytes returned to preinoculation values, neutrophil percentages decreased to slightly below preinoculation values, monocyte percentages were mildly increased, and eosinophil percentages were unaffected. From PID 25 to PID 46, titers of viremia further decreased, and plasma titers of antibodies to ASFV antigens remained high. In pigs with DR-II viremia (PID 4 to PID 46), most viral infectivity (greater than 95%) was RBC associated. Plasma contained less than 1% infectivity, and less than 0.1% of virus was in the WBC fraction (monocytes, lymphocytes, and granulocytes). After PID 46, viremia was no longer detectable.

An adaptive control scheme for a flexible manipulator

NASA Technical Reports Server (NTRS)

Yang, T. C.; Yang, J. C. S.; Kudva, P.

1987-01-01

The problem of controlling a single link flexible manipulator is considered. A self-tuning adaptive control scheme is proposed which consists of a least squares on-line parameter identification of an equivalent linear model followed by a tuning of the gains of a pole placement controller using the parameter estimates. Since the initial parameter values for this model are assumed unknown, the use of arbitrarily chosen initial parameter estimates in the adaptive controller would result in undesirable transient effects. Hence, the initial stage control is carried out with a PID controller. Once the identified parameters have converged, control is transferred to the adaptive controller. Naturally, the relevant issues in this scheme are tests for parameter convergence and minimization of overshoots during control switch-over. To demonstrate the effectiveness of the proposed scheme, simulation results are presented with an analytical nonlinear dynamic model of a single link flexible manipulator.

Prediction of flood abnormalities for improved public safety using a modified adaptive neuro-fuzzy inference system.

PubMed

Aqil, M; Kita, I; Yano, A; Nishiyama, S

2006-01-01

It is widely accepted that an efficient flood alarm system may significantly improve public safety and mitigate economical damages caused by inundations. In this paper, a modified adaptive neuro-fuzzy system is proposed to modify the traditional neuro-fuzzy model. This new method employs a rule-correction based algorithm to replace the error back propagation algorithm that is employed by the traditional neuro-fuzzy method in backward pass calculation. The final value obtained during the backward pass calculation using the rule-correction algorithm is then considered as a mapping function of the learning mechanism of the modified neuro-fuzzy system. Effectiveness of the proposed identification technique is demonstrated through a simulation study on the flood series of the Citarum River in Indonesia. The first four-year data (1987 to 1990) was used for model training/calibration, while the other remaining data (1991 to 2002) was used for testing the model. The number of antecedent flows that should be included in the input variables was determined by two statistical methods, i.e. autocorrelation and partial autocorrelation between the variables. Performance accuracy of the model was evaluated in terms of two statistical indices, i.e. mean average percentage error and root mean square error. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach, and evolving graphical features, and can be adopted for any similar situation to predict the streamflow. The main data processing includes gauging station selection, input generation, lead-time selection/generation, and length of prediction. This program enables users to process the flood data, to train/test the model using various input options, and to visualize results. The program code consists of a set of files, which can be modified as well to match other purposes. This program may also serve as a tool for real-time flood monitoring and process control. The results indicate that the modified neuro-fuzzy model applied to the flood prediction seems to have reached encouraging results for the river basin under examination. The comparison of the modified neuro-fuzzy predictions with the observed data was satisfactory, where the error resulted from the testing period was varied between 2.632% and 5.560%. Thus, this program may also serve as a tool for real-time flood monitoring and process control.

How Reasoning, Judgment, and Decision Making are Colored by Gist-based Intuition: A Fuzzy-Trace Theory Approach

PubMed Central

Corbin, Jonathan C.; Reyna, Valerie F.; Weldon, Rebecca B.; Brainerd, Charles J.

2015-01-01

Fuzzy-trace theory distinguishes verbatim (literal, exact) from gist (meaningful) representations, predicting that reliance on gist increases with experience and expertise. Thus, many judgment-and-decision-making biases increase with development, such that cognition is colored by context in ways that violate logical coherence and probability theories. Nevertheless, this increase in gist-based intuition is adaptive: Gist is stable, less sensitive to interference, and easier to manipulate. Moreover, gist captures the functionally significant essence of information, supporting healthier and more robust decision processes. We describe how fuzzy-trace theory accounts for judgment-and-decision making phenomena, predicting the paradoxical arc of these processes with the development of experience and expertise. We present data linking gist memory processes to gist processing in decision making and provide illustrations of gist reliance in medicine, public health, and intelligence analysis. PMID:26664820

How Reasoning, Judgment, and Decision Making are Colored by Gist-based Intuition: A Fuzzy-Trace Theory Approach.

PubMed

Corbin, Jonathan C; Reyna, Valerie F; Weldon, Rebecca B; Brainerd, Charles J

2015-12-01

Fuzzy-trace theory distinguishes verbatim (literal, exact) from gist (meaningful) representations, predicting that reliance on gist increases with experience and expertise. Thus, many judgment-and-decision-making biases increase with development, such that cognition is colored by context in ways that violate logical coherence and probability theories. Nevertheless, this increase in gist-based intuition is adaptive: Gist is stable, less sensitive to interference, and easier to manipulate. Moreover, gist captures the functionally significant essence of information, supporting healthier and more robust decision processes. We describe how fuzzy-trace theory accounts for judgment-and-decision making phenomena, predicting the paradoxical arc of these processes with the development of experience and expertise. We present data linking gist memory processes to gist processing in decision making and provide illustrations of gist reliance in medicine, public health, and intelligence analysis.

Some fuzzy techniques for staff selection process: A survey

NASA Astrophysics Data System (ADS)

Md Saad, R.; Ahmad, M. Z.; Abu, M. S.; Jusoh, M. S.

2013-04-01

With high level of business competition, it is vital to have flexible staff that are able to adapt themselves with work circumstances. However, staff selection process is not an easy task to be solved, even when it is tackled in a simplified version containing only a single criterion and a homogeneous skill. When multiple criteria and various skills are involved, the problem becomes much more complicated. In adddition, there are some information that could not be measured precisely. This is patently obvious when dealing with opinions, thoughts, feelings, believes, etc. One possible tool to handle this issue is by using fuzzy set theory. Therefore, the objective of this paper is to review the existing fuzzy techniques for solving staff selection process. It classifies several existing research methods and identifies areas where there is a gap and need further research. Finally, this paper concludes by suggesting new ideas for future research based on the gaps identified.

Intelligent control based on fuzzy logic and neural net theory

NASA Technical Reports Server (NTRS)

Lee, Chuen-Chien

1991-01-01

In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

Fuzzy Adaptive Compensation Control of Uncertain Stochastic Nonlinear Systems With Actuator Failures and Input Hysteresis.

PubMed

Wang, Jianhui; Liu, Zhi; Chen, C L Philip; Zhang, Yun

2017-10-12

Hysteresis exists ubiquitously in physical actuators. Besides, actuator failures/faults may also occur in practice. Both effects would deteriorate the transient tracking performance, and even trigger instability. In this paper, we consider the problem of compensating for actuator failures and input hysteresis by proposing a fuzzy control scheme for stochastic nonlinear systems. Compared with the existing research on stochastic nonlinear uncertain systems, it is found that how to guarantee a prescribed transient tracking performance when taking into account actuator failures and hysteresis simultaneously also remains to be answered. Our proposed control scheme is designed on the basis of the fuzzy logic system and backstepping techniques for this purpose. It is proven that all the signals remain bounded and the tracking error is ensured to be within a preestablished bound with the failures of hysteretic actuator. Finally, simulations are provided to illustrate the effectiveness of the obtained theoretical results.

The fuzzy cube and causal efficacy: representation of concomitant mechanisms in stroke.

PubMed

Jobe, Thomas H.; Helgason, Cathy M.

1998-04-01

Twentieth century medical science has embraced nineteenth century Boolean probability theory based upon two-valued Aristotelian logic. With the later addition of bit-based, von Neumann structured computational architectures, an epistemology based on randomness has led to a bivalent epidemiological methodology that dominates medical decision making. In contrast, fuzzy logic, based on twentieth century multi-valued logic, and computational structures that are content addressed and adaptively modified, has advanced a new scientific paradigm for the twenty-first century. Diseases such as stroke involve multiple concomitant causal factors that are difficult to represent using conventional statistical methods. We tested which paradigm best represented this complex multi-causal clinical phenomenon-stroke. We show that the fuzzy logic paradigm better represented clinical complexity in cerebrovascular disease than current probability theory based methodology. We believe this finding is generalizable to all of clinical science since multiple concomitant causal factors are involved in nearly all known pathological processes.

A curvature-based weighted fuzzy c-means algorithm for point clouds de-noising

NASA Astrophysics Data System (ADS)

Cui, Xin; Li, Shipeng; Yan, Xiutian; He, Xinhua

2018-04-01

In order to remove the noise of three-dimensional scattered point cloud and smooth the data without damnify the sharp geometric feature simultaneity, a novel algorithm is proposed in this paper. The feature-preserving weight is added to fuzzy c-means algorithm which invented a curvature weighted fuzzy c-means clustering algorithm. Firstly, the large-scale outliers are removed by the statistics of r radius neighboring points. Then, the algorithm estimates the curvature of the point cloud data by using conicoid parabolic fitting method and calculates the curvature feature value. Finally, the proposed clustering algorithm is adapted to calculate the weighted cluster centers. The cluster centers are regarded as the new points. The experimental results show that this approach is efficient to different scale and intensities of noise in point cloud with a high precision, and perform a feature-preserving nature at the same time. Also it is robust enough to different noise model.

A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller

NASA Astrophysics Data System (ADS)

Amor, Mohamed Anis Ben; Oda, Takeshi; Watanabe, Shigeyoshi

This paper is concerned with the development of a car-steering model for traffic simulation. Our focus in this paper is to propose a model of the steering behavior of a human driver for different driving scenarios. These scenarios are modeled in a unified framework using the idea of target position. The proposed approach deals with the driver’s approximation and decision-making mechanisms in tracking a target position by means of fuzzy set theory. The main novelty in this paper lies in the development of a learning algorithm that has the intention to imitate the driver’s self-learning from his driving experience and to mimic his maneuvers on the steering wheel, using linear networks as local approximators in the corresponding fuzzy areas. Results obtained from the simulation of an obstacle avoidance scenario show the capability of the model to carry out a human-like behavior with emphasis on learned skills.

Pinoid kinase regulates root gravitropism through modulation of PIN2-dependent basipetal auxin transport in Arabidopsis thaliana

NASA Astrophysics Data System (ADS)

Muday, Gloria; Sukumar, Poornima; Edwards, Karin; Delong, Alison; Rahman, Abidur

Reversible protein phosphorylation is a key regulatory mechanism governing polar auxin transport. We tested the hypothesis that PINOID (PID)-mediated phosphorylation and RCN1- regulated dephosphorylation might antagonistically regulate auxin transport and gravity response in seedling roots. Here we show that basipetal IAA transport and gravitropism are reduced in pid mutant seedlings, while acropetal transport and lateral root development are unchanged. Treatment of wild-type seedlings with the protein kinase inhibitor, staurosporine, phenocopied the reduced auxin transport and gravity response of pid-9 and reduced formation of asymmetric DR5-revGFP expression at the root tip after reorientation relative to gravity. Gravitropism and auxin transport in pid are resistant to further inhibition by staurosporine. Gravity response defects of rcn1 and pid-9 are partially rescued by treatment with staurosporine or the phosphatase inhibitor, cantharidin, respectively, and in the pid-9 rcn1 double mutant. Furthermore, the effect of staurosporine is lost in pin2, and a PIN2::GFP fusion protein accumulates in endomembrane compartments after staurosporine treatment. In the pid-9 mutant, immunological techniques find a similar PIN2 localization. These data suggest that staurosporine inhibits gravitropism and basipetal IAA transport by blocking PID action and altering PIN2 localization and support the model that PID and RCN1 reciprocally regulate root gravitropic curvature.

The Personality Inventory for DSM-5 Brief Form: Evidence for Reliability and Construct Validity in a Sample of Community-Dwelling Italian Adolescents.

PubMed

Fossati, Andrea; Somma, Antonella; Borroni, Serena; Markon, Kristian E; Krueger, Robert F

2017-07-01

To assess the reliability and construct validity of the Personality Inventory for DSM-5 Brief Form (PID-5-BF) among adolescents, 877 Italian high school students were administered the PID-5-BF. Participants were administered also the Measure of Disordered Personality Functioning (MDPF) as a criterion measure. In the full sample, Cronbach's alpha values for the PID-5-BF scales ranged from .59 (Detachment) to .77 (Psychoticism); in addition, all PID-5-BF scales showed mean interitem correlation values in the .22 to .40 range. Cronbach's alpha values for the PID-5-BF total score was .83 (mean interitem r = .16). Although 2-month test-retest reliability could be assessed only in a small ( n = 42) subsample of participants, all PID-5-BF scale scores showed adequate temporal stability, as indexed by intraclass r values ranging from .78 (Negative Affectivity) to .97 (Detachment), all ps <.001. Exploratory structural equation modeling analyses provided at least moderate support for the a priori model of PID-5-BF items. Multiple regression analyses showed that PID-5-BF scales predicted a nonnegligible amount of variance in MDPF Non-Cooperativeness, adjusted R 2 = .17, p < .001, and Non-Coping scales, adjusted R 2 = .32, p < .001. Similarly, the PID-5-BF total score was a significant predictor of both MDPF Non-Coping, and Non-Cooperativeness scales.

Treatment of Acute Pelvic Inflammatory Disease

PubMed Central

Sweet, Richard L.

2011-01-01

Pelvic inflammatory disease (PID), one of the most common infections in nonpregnant women of reproductive age, remains an important public health problem. It is associated with major long-term sequelae, including tubal factor infertility, ectopic pregnancy, and chronic pelvic pain. In addition, treatment of acute PID and its complications incurs substantial health care costs. Prevention of these long-term sequelae is dependent upon development of treatment strategies based on knowledge of the microbiologic etiology of acute PID. It is well accepted that acute PID is a polymicrobic infection. The sexually transmitted organisms, Neisseria gonorrhoeae and Chlamydia trachomatis, are present in many cases, and microorganisms comprising the endogenous vaginal and cervical flora are frequently associated with PID. This includes anaerobic and facultative bacteria, similar to those associated with bacterial vaginosis. Genital tract mycoplasmas, most importantly Mycoplasma genitalium, have recently also been implicated as a cause of acute PID. As a consequence, treatment regimens for acute PID should provide broad spectrum coverage that is effective against these microorganisms. PMID:22228985

Supply-demand mismatch transients in susceptible peri-infarct hot zones explain the origins of spreading injury depolarizations.

PubMed

von Bornstädt, Daniel; Houben, Thijs; Seidel, Jessica L; Zheng, Yi; Dilekoz, Ergin; Qin, Tao; Sandow, Nora; Kura, Sreekanth; Eikermann-Haerter, Katharina; Endres, Matthias; Boas, David A; Moskowitz, Michael A; Lo, Eng H; Dreier, Jens P; Woitzik, Johannes; Sakadžić, Sava; Ayata, Cenk

2015-03-04

Peri-infarct depolarizations (PIDs) are seemingly spontaneous spreading depression-like waves that negatively impact tissue outcome in both experimental and human stroke. Factors triggering PIDs are unknown. Here, we show that somatosensory activation of peri-infarct cortex triggers PIDs when the activated cortex is within a critical range of ischemia. We show that the mechanism involves increased oxygen utilization within the activated cortex, worsening the supply-demand mismatch. We support the concept by clinical data showing that mismatch predisposes stroke patients to PIDs as well. Conversely, transient worsening of mismatch by episodic hypoxemia or hypotension also reproducibly triggers PIDs. Therefore, PIDs are triggered upon supply-demand mismatch transients in metastable peri-infarct hot zones due to increased demand or reduced supply. Based on the data, we propose that minimizing sensory stimulation and hypoxic or hypotensive transients in stroke and brain injury would reduce PID incidence and their adverse impact on outcome. Copyright © 2015 Elsevier Inc. All rights reserved.

Longitudinal control of aircraft dynamics based on optimization of PID parameters

NASA Astrophysics Data System (ADS)

Deepa, S. N.; Sudha, G.

2016-03-01

Recent years many flight control systems and industries are employing PID controllers to improve the dynamic behavior of the characteristics. In this paper, PID controller is developed to improve the stability and performance of general aviation aircraft system. Designing the optimum PID controller parameters for a pitch control aircraft is important in expanding the flight safety envelope. Mathematical model is developed to describe the longitudinal pitch control of an aircraft. The PID controller is designed based on the dynamic modeling of an aircraft system. Different tuning methods namely Zeigler-Nichols method (ZN), Modified Zeigler-Nichols method, Tyreus-Luyben tuning, Astrom-Hagglund tuning methods are employed. The time domain specifications of different tuning methods are compared to obtain the optimum parameters value. The results prove that PID controller tuned by Zeigler-Nichols for aircraft pitch control dynamics is better in stability and performance in all conditions. Future research work of obtaining optimum PID controller parameters using artificial intelligence techniques should be carried out.

Supply-demand mismatch transients in susceptible peri-infarct hot zones explain the origin of spreading injury depolarizations

PubMed Central

von Bornstädt, Daniel; Houben, Thijs; Seidel, Jessica; Zheng, Yi; Dilekoz, Ergin; Qin, Tao; Sandow, Nora; Kura, Sreekanth; Eikermann-Haerter, Katharina; Endres, Matthias; Boas, David A.; Moskowitz, Michael A.; Lo, Eng H.; Dreier, Jens P.; Woitzik, Johannes; Sakadžić, Sava; Ayata, Cenk

2015-01-01

SUMMARY Peri-infarct depolarizations (PIDs) are seemingly spontaneous spreading depression-like waves that negatively impact tissue outcome in both experimental and human stroke. Factors triggering PIDs are unknown. Here, we show that somatosensory activation of peri-infarct cortex triggers PIDs when the activated cortex is within a critical range of ischemia. We show that the mechanism involves increased oxygen utilization within the activated cortex, worsening the supply-demand mismatch. We support the concept by clinical data showing that mismatch predisposes to PIDs in human stroke as well. Conversely, transient worsening of mismatch by episodic hypoxemia or hypotension also reproducibly triggers PIDs. Therefore, PIDs are triggered upon supply-demand mismatch transients in metastable peri-infarct hot zones due to increased demand or reduced supply. Based on the data, we propose that minimizing sensory stimulation and hypoxic or hypotensive transients in stroke and brain injury would reduce PID incidence and their adverse impact on outcome. PMID:25741731

[Acute renal insufficiency caused by phenyl-indane-dione. Apropos of 1 case].

PubMed

Horellou, M F; Feiss, P; Voultoury, J C; Gay, R

1978-01-01

One case of Phenindione (PID) adverse reaction is reported. The patient showed a typical picture of immunological reaction to the drug. In spite of severe bacteremia, she recovered. Only 33 cases of PID intolerance are reported in the literature. In all these patients, renal failure occurred. Superinfection is the most frequent cause of death. PID adverse reaction should be evoqued in the presence of signs such a fever, asthenia, anorexia and cutaneous reaction. The PID should be stopped immediatly but renal failure yet develops. During a PID treatment, frequent evaluation of blood azotemia, creatinine and proteinuria should be performed.

[Blood anticoagulation system and urine fibrinolysis in newborn infants with suppurative-inflammatory diseases].

PubMed

Samsygina, G A; Vykhristiuk, O F

1989-01-01

The anticoagulative blood system, blood and urine fibrinolysis were studied in 95 children with pyo-inflammatory diseases (PID) and in 56 normal neonates aged 2 to 28 days. The patients afflicted with PID were distributed into 3 groups; group I included patients with uneventful localized PID, group II consisted of patients with grave PID, and group III of patients with sepsis. Hemostasis and urine fibrinolysis were compared according to 20 indicators. The intensity and involvement of certain components of the fibrinolytic and anticoagulative blood systems in PID turned out different and were dependent on the disease gravity.

State Estimation for Landing Maneuver on High Performance Aircraft

NASA Astrophysics Data System (ADS)

Suresh, P. S.; Sura, Niranjan K.; Shankar, K.

2018-01-01

State estimation methods are popular means for validating aerodynamic database on aircraft flight maneuver performance characteristics. In this work, the state estimation method during landing maneuver is explored for the first of its kind, using upper diagonal adaptive extended Kalman filter (UD-AEKF) with fuzzy based adaptive tunning of process noise matrix. The mathematical model for symmetrical landing maneuver consists of non-linear flight mechanics equation representing Aircraft longitudinal dynamics. The UD-AEKF algorithm is implemented in MATLAB environment and the states with bias is considered to be the initial conditions just prior to the flare. The measurement data is obtained from a non-linear 6 DOF pilot in loop simulation using FORTRAN. These simulated measurement data is additively mixed with process and measurement noises, which are used as an input for UD-AEKF. Then, the governing states that dictate the landing loads at the instant of touch down are compared. The method is verified using flight data wherein, the vertical acceleration at the aircraft center of gravity (CG) is compared. Two possible outcome of purely relying on the aircraft measured data is highlighted. It is observed that, with the implementation of adaptive fuzzy logic based extended Kalman filter tuned to adapt for aircraft landing dynamics, the methodology improves the data quality of the states that are sourced from noisy measurements.

Comparison of the population excess fraction of Chlamydia trachomatis infection on pelvic inflammatory disease at 12-months in the presence and absence of chlamydia testing and treatment: Systematic review and retrospective cohort analysis

PubMed Central

Turner, Katy M. E.; Leung, Stella; Yu, B. Nancy; Frølund, Maria; Benfield, Thomas; Blanchard, James; Westh, Henrik; Ward, Helen

2017-01-01

Background The impact of Chlamydia trachomatis (chlamydia) control on the incidence of pelvic inflammatory disease (PID) is theoretically limited by the proportion of PID caused by chlamydia. We estimate the population excess fraction (PEF) of treated chlamydia infection on PID at 12-months in settings with widespread chlamydia control (testing and treatment) and compare this to the estimated PEF of untreated chlamydia. Methods We used two large retrospective population-based cohorts of women of reproductive age from settings with widespread chlamydia control to calculate the PEF of treated chlamydia on PID at 12-months. We undertook a systematic review to identify further studies that reported the risk of PID in women who were tested for chlamydia (infected and uninfected). We used the same method to calculate the PEF in eligible studies then compared all estimates of PEF. Results The systematic review identified a single study, a randomised controlled trial of chlamydia screening (POPI-RCT). In the presence of testing and treatment <10% of PID at 12-months was attributable to treated (baseline) chlamydia infections (Manitoba: 8.86%(95%CI 7.15–10.75); Denmark: 3.84%(3.26–4.45); screened-arm POPI-RCT: 0.99%(0.00–29.06)). In the absence of active chlamydia treatment 26.44%(11.57–46.32) of PID at 12-months was attributable to untreated (baseline) chlamydia infections (deferred-arm POPI-RCT). The PEFs suggest that eradicating baseline chlamydia infections could prevent 484 cases of PID at 12-months per 100,000 women in the untreated setting and 13–184 cases of PID per 100,000 tested women in the presence of testing and treatment. Conclusion Testing and treating chlamydia reduced the PEF of chlamydia on PID by 65% compared to the untreated setting. But in the presence of testing and treatment over 90% of PID could not be attributed to a baseline chlamydia infection. More information is needed about the aetiology of PID to develop effective strategies for improving the reproductive health of women. PMID:28199392

Pelvic Inflammatory Disease (PID)

MedlinePlus

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Hospitalisations for pelvic inflammatory disease temporally related to a diagnosis of Chlamydia or gonorrhoea: a retrospective cohort study.

PubMed

Reekie, Joanne; Donovan, Basil; Guy, Rebecca; Hocking, Jane S; Jorm, Louisa; Kaldor, John M; Mak, Donna B; Preen, David; Pearson, Sallie; Roberts, Christine L; Stewart, Louise; Wand, Handan; Ward, James; Liu, Bette

2014-01-01

The presence and severity of pelvic inflammatory disease (PID) symptoms are thought to vary by microbiological etiology but there is limited empirical evidence. We sought to estimate and compare the rates of hospitalisation for PID temporally related to diagnoses of gonorrhoea and chlamydia. All women, aged 15-45 years in the Australian state of New South Wales (NSW), with a diagnosis of chlamydia or gonorrhoea between 01/07/2000 and 31/12/2008 were followed by record linkage for up to one year after their chlamydia or gonorrhoea diagnosis for hospitalisations for PID. Standardised incidence ratios compared the incidence of PID hospitalisations to the age-equivalent NSW population. A total of 38,193 women had a chlamydia diagnosis, of which 483 were hospitalised for PID; incidence rate (IR) 13.9 per 1000 person-years of follow-up (PYFU) (95%CI 12.6-15.1). In contrast, 1015 had a gonorrhoea diagnosis, of which 45 were hospitalised for PID (IR 50.8 per 1000 PYFU, 95%CI 36.0-65.6). The annual incidence of PID hospitalisation temporally related to a chlamydia or gonorrhoea diagnosis was 27.0 (95%CI 24.4-29.8) and 96.6 (95%CI 64.7-138.8) times greater, respectively, than the age-equivalent NSW female population. Younger age, socio-economic disadvantage, having a diagnosis prior to 2005 and having a prior birth were also associated with being hospitalised for PID. Chlamydia and gonorrhoea are both associated with large increases in the risk of PID hospitalisation. Our data suggest the risk of PID hospitalisation is much higher for gonorrhoea than chlamydia; however, further research is needed to confirm this finding.

Comparison of 3 different postimplant dosimetry methods following permanent {sup 125}I prostate seed brachytherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marcu, Loredana G., E-mail: loredana@marcunet.com; Faculty of Science, University of Oradea; School of Chemistry and Physics, University of Adelaide, South Australia

2013-10-01

Postimplant dosimetry (PID) after Iodine-125 ({sup 125}I) implant of the prostate should offer a reliable qualitative assessment. So far, there is no consensus regarding the optimum PID method, though the latest literature is in favor of magnetic resonance imaging (MRI). This study aims to simultaneously compare 3 PID techniques: (1) MRI-computed tomography (CT) fusion; (2) ultrasound (US)-CT fusion; and (3) manual target delineation on CT. The study comprised 10 patients with prostate cancer. CT/MR scans with urinary catheters in place for PID were done either on day 0 or day 1 postimplantation. The main parameter evaluated and compared among methodsmore » was target D90. The results show that CT-based D90s are lower than US-CT D90s (median difference,−6.85%), whereas MR-CT PID gives higher D90 than US-CT PID (median difference, 4.25%). Manual contouring on CT images tends to overestimate the prostate volume compared with transrectal ultrasound (TRUS) (median difference, 23.33%), whereas on US images the target is overestimated compared with MR-based contouring (median difference, 13.25%). Although there are certain differences among the results given by various PID techniques, the differences are statistically insignificant for this small group of patients. Any dosimetric comparison between 2 PID techniques should also account for the limitations of each technique, to allow for an accurate quantification of data. Given that PID after permanent radioactive seed implant is mandatory for quality assurance, any imaging method–based PID (MR-CT, US-CT, and CT) available in a radiotherapy department can be indicative of the quality of the procedure.« less

Profile of the Pediatric Infectious Disease Workforce in 2015.

PubMed

Yeh, Sylvia H; Vijayan, Vini; Hahn, Andrea; Ruch-Ross, Holly; Kirkwood, Suzanne; Phillips, Terri Christene; Harrison, Christopher J

2017-12-22

Almost 20 years have elapsed since the last workforce survey of pediatric infectious disease (PID) subspecialists was conducted in 1997-1998. The American Academy of Pediatrics Section on Infectious Diseases in collaboration with the Pediatric Infectious Diseases Society sought to assess the status of the current PID workforce. A Web-based survey conducted in 2015 collected data on demographics, practice patterns, and job satisfaction among the PID workforce, and identified factors related to job placement among recent fellowship graduates. Of 946 respondents (48% response rate), 50% were female. The average age was 51 years (range, 29-88 years); 63% were employed by an academic center/hospital, and 85% provided direct patient care; and 18% were not current PID practitioners. Of the 138 (21%) respondents who had completed a PID fellowship within the previous 5 years, 83% applied for <5 PID positions; 43% reported that their first position was created specifically for them; 47% had 1 job offer, and 41% had 2 or 3 job offers; 82% were employed within 6 months; and 74% remained at the institution of their first job. Respondents who were practicing PID full-time or part-time (n = 778) indicated desiring more focused training in immunodeficiencies (31%), transplant-related care (31%), and travel/tropical medicine (28%). Overall, 70% of the respondents would "definitely" or "probably" choose PID again. Most respondents were satisfied with their career choice in PID. Most of the recent fellowship graduates were employed within 6 months after training. We identified potential areas in which the PID community can focus efforts to maintain the pipeline and improve satisfaction among its physicians. © The Author 2017. Published by Oxford University Press on behalf of The Journal of the Pediatric Infectious Diseases Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

PSO-based PID Speed Control of Traveling Wave Ultrasonic Motor under Temperature Disturbance

NASA Astrophysics Data System (ADS)

Arifin Mat Piah, Kamal; Yusoff, Wan Azhar Wan; Azmi, Nur Iffah Mohamed; Romlay, Fadhlur Rahman Mohd

2018-03-01

Traveling wave ultrasonic motors (TWUSMs) have a time varying dynamics characteristics. Temperature rise in TWUSMs remains a problem particularly in sustaining optimum speed performance. In this study, a PID controller is used to control the speed of TWUSM under temperature disturbance. Prior to developing the controller, a linear approximation model which relates the speed to the temperature is developed based on the experimental data. Two tuning methods are used to determine PID parameters: conventional Ziegler-Nichols(ZN) and particle swarm optimization (PSO). The comparison of speed control performance between PSO-PID and ZN-PID is presented. Modelling, simulation and experimental work is carried out utilizing Fukoku-Shinsei USR60 as the chosen TWUSM. The results of the analyses and experimental work reveal that PID tuning using PSO-based optimization has the advantage over the conventional Ziegler-Nichols method.

Fuzzy logic applied to prospecting for areas for installation of wood panel industries.

PubMed

Dos Santos, Alexandre Rosa; Paterlini, Ewerthon Mattos; Fiedler, Nilton Cesar; Ribeiro, Carlos Antonio Alvares Soares; Lorenzon, Alexandre Simões; Domingues, Getulio Fonseca; Marcatti, Gustavo Eduardo; de Castro, Nero Lemos Martins; Teixeira, Thaisa Ribeiro; Dos Santos, Gleissy Mary Amaral Dino Alves; Juvanhol, Ronie Silva; Branco, Elvis Ricardo Figueira; Mota, Pedro Henrique Santos; da Silva, Lilianne Gomes; Pirovani, Daiani Bernardo; de Jesus, Waldir Cintra; Santos, Ana Carolina de Albuquerque; Leite, Helio Garcia; Iwakiri, Setsuo

2017-05-15

Prospecting for suitable areas for forestry operations, where the objective is a reduction in production and transportation costs, as well as the maximization of profits and available resources, constitutes an optimization problem. However, fuzzy logic is an alternative method for solving this problem. In the context of prospecting for suitable areas for the installation of wood panel industries, we propose applying fuzzy logic analysis for simulating the planting of different species and eucalyptus hybrids in Espírito Santo State, Brazil. The necessary methodological steps for this study are as follows: a) agriclimatological zoning of different species and eucalyptus hybrids; b) the selection of the vector variables; c) the application of the Euclidean distance to the vector variables; d) the application of fuzzy logic to matrix variables of the Euclidean distance; and e) the application of overlap fuzzy logic to locate areas for installation of wood panel industries. Among all the species and hybrids, Corymbia citriodora showed the highest percentage values for the combined very good and good classes, with 8.60%, followed by Eucalyptus grandis with 8.52%, Eucalyptus urophylla with 8.35% and Urograndis with 8.34%. The fuzzy logic analysis afforded flexibility in prospecting for suitable areas for the installation of wood panel industries in the Espírito Santo State can bring great economic and social benefits to the local population with the generation of jobs, income, tax revenues and GDP increase for the State and municipalities involved. The proposed methodology can be adapted to other areas and agricultural crops. Copyright © 2017 Elsevier Ltd. All rights reserved.

Adaptive inferential sensors based on evolving fuzzy models.

PubMed

Angelov, Plamen; Kordon, Arthur

2010-04-01

A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/transparency. The proposed new methodology makes possible inferential sensors to recalibrate automatically, which reduces significantly the life-cycle efforts for their maintenance. This is achieved by the adaptive and flexible open-structure EFM used. The novelty of this paper lies in the following: (1) the overall concept of inferential sensors with evolving and self-developing structure from the data streams; (2) the new methodology for online automatic selection of input variables that are most relevant for the prediction; (3) the technique to detect automatically a shift in the data pattern using the age of the clusters (and fuzzy rules); (4) the online standardization technique used by the learning procedure of the evolving model; and (5) the application of this innovative approach to several real-life industrial processes from the chemical industry (evolving inferential sensors, namely, eSensors, were used for predicting the chemical properties of different products in The Dow Chemical Company, Freeport, TX). It should be noted, however, that the methodology and conclusions of this paper are valid for the broader area of chemical and process industries in general. The results demonstrate that well-interpretable and with-simple-structure inferential sensors can automatically be designed from the data stream in real time, which predict various process variables of interest. The proposed approach can be used as a basis for the development of a new generation of adaptive and evolving inferential sensors that can address the challenges of the modern advanced process industry.

The maladaptive personality traits of the Personality Inventory for DSM-5 (PID-5) in relation to the HEXACO personality factors and schizotypy/dissociation.

PubMed

Ashton, Michael C; Lee, Kibeom; de Vries, Reinout E; Hendrickse, Joshua; Born, Marise Ph

2012-10-01

The Personality Inventory for DSM-5 (PID-5), a new measure of maladaptive personality traits, has recently been developed by the DSM-5 Personality and Personality Disorders Workgroup. The PID-5 variables were examined within the seven-factor space defined by the six HEXACO factors and the Schizotypy/Dissociation factor (Ashton & Lee, 2012) using participant samples from Canada (N = 378) and the Netherlands (N = 476). Extension analyses showed that several PID-5 facet-level scales represented each of the Honesty-Humility, Emotionality, Extraversion, Conscientiousness, and Schizotypy/Dissociation factors. In contrast, only one PID-5 scale loaded strongly on HEXACO Agreeableness, and no PID-5 scales loaded strongly on Openness to Experience. In addition, a joint factor analysis involving the PID-5 variables and facets of the Five-Factor Model was conducted in the Canadian sample and recovered a set of seven factors corresponding rather closely to the HEXACO factors plus Schizotypy/Dissociation. The authors discuss implications for the assessment and structure of normal and abnormal personality.

What is the Value Proposition of Persistent Identifiers?

NASA Astrophysics Data System (ADS)

Klump, Jens; Huber, Robert

2017-04-01

Persistent identifiers (PID) are widely used today in scientific communication and documentation. Global unique identification plus persistent resolution of links to referenced digital research objects have been strong selling points for PID Systems as enabling technical infrastructures. Novel applications of PID Systems in research now go beyond the identification of file based objects such as literature or data sets and include the identification of dynamically changing datasets accessed through web services, physical objects, persons and organisations. But not only do we see more use cases but also a proliferation of identifier systems. An analysis of PID Systems used by 1381 repositories listed in the Registry of Research Data Repositories (re3data.org, status of 14 Dec 2015) showed that many disciplinary data repositories make use of PID that are not among the systems promoted by the libraries and publishers (DOI, PURL, ARK). This indicates that a number of communities have developed their own PID Systems. This begs the question, do we need more identifier systems? What makes their value proposition more appealing than those of already existing systems? On the other hand, some of these new use cases deal with entities outside the digital domain, the original scope of application for PIDs. It is therefore necessary to critically appraise the value propositions of available PID Systems and compare these against the requirements of new use cases for PID. Undoubtedly, DOI are the most used persistent identifier in scholarly communication. It was originally designed "to link customers with publishers, facilitate electronic commerce, and enable copyright management systems." Today, the DOI system is described as providing "a technical and social infrastructure for the registration and use of persistent interoperable identifiers for use on digital networks". This example shows how value propositions can change over time. Additional value can be gained by cross-linking between PID Systems, thus allowing new scholarly documentation and evaluation methods such as documenting the track record of researchers in publications and successful funding proposals, apply advanced bibliometric approaches, estimate the output and impact of funding, assess the reuse and subsequent impact of data publications, demonstrate the efficient use of research infrastructures, etc. This recombination of systems raise a series of new expectations and each stakeholder group may have its own vision of the benefits and value proposition of PIDs, which might be in conflict with others. New PID applications will arise with the application of PID Systems to semantic web technologies and to the Internet of Things, which extend PID applications to beyond digital objects to concepts and things, respectively, raising yet again their own expectations and value propositions. What are we trying to identify? What is the purpose served by identifying it? What are the implications for semantic web technologies? How certain can we be about the identity of an object and its state changes over time (Ship of Theseus Paradox)? In this presentation we will discuss a number of PID use cases and match these against the value propositions offered by a selection of PID Systems.

Evolving fuzzy rules for relaxed-criteria negotiation.

PubMed

Sim, Kwang Mong

2008-12-01

In the literature on automated negotiation, very few negotiation agents are designed with the flexibility to slightly relax their negotiation criteria to reach a consensus more rapidly and with more certainty. Furthermore, these relaxed-criteria negotiation agents were not equipped with the ability to enhance their performance by learning and evolving their relaxed-criteria negotiation rules. The impetus of this work is designing market-driven negotiation agents (MDAs) that not only have the flexibility of relaxing bargaining criteria using fuzzy rules, but can also evolve their structures by learning new relaxed-criteria fuzzy rules to improve their negotiation outcomes as they participate in negotiations in more e-markets. To this end, an evolutionary algorithm for adapting and evolving relaxed-criteria fuzzy rules was developed. Implementing the idea in a testbed, two kinds of experiments for evaluating and comparing EvEMDAs (MDAs with relaxed-criteria rules that are evolved using the evolutionary algorithm) and EMDAs (MDAs with relaxed-criteria rules that are manually constructed) were carried out through stochastic simulations. Empirical results show that: 1) EvEMDAs generally outperformed EMDAs in different types of e-markets and 2) the negotiation outcomes of EvEMDAs generally improved as they negotiated in more e-markets.

Numerical Simulation and Optimization of Directional Solidification Process of Single Crystal Superalloy Casting

PubMed Central

Zhang, Hang; Xu, Qingyan; Liu, Baicheng

2014-01-01

The rapid development of numerical modeling techniques has led to more accurate results in modeling metal solidification processes. In this study, the cellular automaton-finite difference (CA-FD) method was used to simulate the directional solidification (DS) process of single crystal (SX) superalloy blade samples. Experiments were carried out to validate the simulation results. Meanwhile, an intelligent model based on fuzzy control theory was built to optimize the complicate DS process. Several key parameters, such as mushy zone width and temperature difference at the cast-mold interface, were recognized as the input variables. The input variables were functioned with the multivariable fuzzy rule to get the output adjustment of withdrawal rate (v) (a key technological parameter). The multivariable fuzzy rule was built, based on the structure feature of casting, such as the relationship between section area, and the delay time of the temperature change response by changing v, and the professional experience of the operator as well. Then, the fuzzy controlling model coupled with CA-FD method could be used to optimize v in real-time during the manufacturing process. The optimized process was proven to be more flexible and adaptive for a steady and stray-grain free DS process. PMID:28788535

Enhancement of brain tumor MR images based on intuitionistic fuzzy sets

NASA Astrophysics Data System (ADS)

Deng, Wankai; Deng, He; Cheng, Lifang

2015-12-01

Brain tumor is one of the most fatal cancers, especially high-grade gliomas are among the most deadly. However, brain tumor MR images usually have the disadvantages of low resolution and contrast when compared with the optical images. Consequently, we present a novel adaptive intuitionistic fuzzy enhancement scheme by combining a nonlinear fuzzy filtering operation with fusion operators, for the enhancement of brain tumor MR images in this paper. The presented scheme consists of the following six steps: Firstly, the image is divided into several sub-images. Secondly, for each sub-image, object and background areas are separated by a simple threshold. Thirdly, respective intuitionistic fuzzy generators of object and background areas are constructed based on the modified restricted equivalence function. Fourthly, different suitable operations are performed on respective membership functions of object and background areas. Fifthly, the membership plane is inversely transformed into the image plane. Finally, an enhanced image is obtained through fusion operators. The comparison and evaluation of enhancement performance demonstrate that the presented scheme is helpful to determine the abnormal functional areas, guide the operation, judge the prognosis, and plan the radiotherapy by enhancing the fine detail of MR images.

Hybrid clustering based fuzzy structure for vibration control - Part 1: A novel algorithm for building neuro-fuzzy system

NASA Astrophysics Data System (ADS)

Nguyen, Sy Dzung; Nguyen, Quoc Hung; Choi, Seung-Bok

2015-01-01

This paper presents a new algorithm for building an adaptive neuro-fuzzy inference system (ANFIS) from a training data set called B-ANFIS. In order to increase accuracy of the model, the following issues are executed. Firstly, a data merging rule is proposed to build and perform a data-clustering strategy. Subsequently, a combination of clustering processes in the input data space and in the joint input-output data space is presented. Crucial reason of this task is to overcome problems related to initialization and contradictory fuzzy rules, which usually happen when building ANFIS. The clustering process in the input data space is accomplished based on a proposed merging-possibilistic clustering (MPC) algorithm. The effectiveness of this process is evaluated to resume a clustering process in the joint input-output data space. The optimal parameters obtained after completion of the clustering process are used to build ANFIS. Simulations based on a numerical data, 'Daily Data of Stock A', and measured data sets of a smart damper are performed to analyze and estimate accuracy. In addition, convergence and robustness of the proposed algorithm are investigated based on both theoretical and testing approaches.

A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy

NASA Astrophysics Data System (ADS)

Li, Yongbo; Xu, Minqiang; Wang, Rixin; Huang, Wenhu

2016-01-01

This paper presents a new rolling bearing fault diagnosis method based on local mean decomposition (LMD), improved multiscale fuzzy entropy (IMFE), Laplacian score (LS) and improved support vector machine based binary tree (ISVM-BT). When the fault occurs in rolling bearings, the measured vibration signal is a multi-component amplitude-modulated and frequency-modulated (AM-FM) signal. LMD, a new self-adaptive time-frequency analysis method can decompose any complicated signal into a series of product functions (PFs), each of which is exactly a mono-component AM-FM signal. Hence, LMD is introduced to preprocess the vibration signal. Furthermore, IMFE that is designed to avoid the inaccurate estimation of fuzzy entropy can be utilized to quantify the complexity and self-similarity of time series for a range of scales based on fuzzy entropy. Besides, the LS approach is introduced to refine the fault features by sorting the scale factors. Subsequently, the obtained features are fed into the multi-fault classifier ISVM-BT to automatically fulfill the fault pattern identifications. The experimental results validate the effectiveness of the methodology and demonstrate that proposed algorithm can be applied to recognize the different categories and severities of rolling bearings.

Intelligent manipulation technique for multi-branch robotic systems

NASA Technical Reports Server (NTRS)

Chen, Alexander Y. K.; Chen, Eugene Y. S.

1990-01-01

New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system.

An Artificial Intelligence Approach for Modeling and Prediction of Water Diffusion Inside a Carbon Nanotube

PubMed Central

2009-01-01

Modeling of water flow in carbon nanotubes is still a challenge for the classic models of fluid dynamics. In this investigation, an adaptive-network-based fuzzy inference system (ANFIS) is presented to solve this problem. The proposed ANFIS approach can construct an input–output mapping based on both human knowledge in the form of fuzzy if-then rules and stipulated input–output data pairs. Good performance of the designed ANFIS ensures its capability as a promising tool for modeling and prediction of fluid flow at nanoscale where the continuum models of fluid dynamics tend to break down. PMID:20596382

An Artificial Intelligence Approach for Modeling and Prediction of Water Diffusion Inside a Carbon Nanotube.

PubMed

Ahadian, Samad; Kawazoe, Yoshiyuki

2009-06-04

Modeling of water flow in carbon nanotubes is still a challenge for the classic models of fluid dynamics. In this investigation, an adaptive-network-based fuzzy inference system (ANFIS) is presented to solve this problem. The proposed ANFIS approach can construct an input-output mapping based on both human knowledge in the form of fuzzy if-then rules and stipulated input-output data pairs. Good performance of the designed ANFIS ensures its capability as a promising tool for modeling and prediction of fluid flow at nanoscale where the continuum models of fluid dynamics tend to break down.

Decentralized adaptive control of manipulators - Theory, simulation, and experimentation

NASA Technical Reports Server (NTRS)

Seraji, Homayoun

1989-01-01

The author presents a simple decentralized adaptive-control scheme for multijoint robot manipulators based on the independent joint control concept. The control objective is to achieve accurate tracking of desired joint trajectories. The proposed control scheme does not use the complex manipulator dynamic model, and each joint is controlled simply by a PID (proportional-integral-derivative) feedback controller and a position-velocity-acceleration feedforward controller, both with adjustable gains. Simulation results are given for a two-link direct-drive manipulator under adaptive independent joint control. The results illustrate trajectory tracking under coupled dynamics and varying payload. The proposed scheme is implemented on a MicroVAX II computer for motion control of the three major joints of a PUMA 560 arm. Experimental results are presented to demonstrate that trajectory tracking is achieved despite coupled nonlinear joint dynamics.

Potential-Induced Degradation-Delamination Mode in Crystalline Silicon Modules: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hacke, Peter L; Kempe, Michael D; Wohlgemuth, John

A test sequence producing potential-induced degradation-delamination (PID-d) in crystalline silicon modules has been tested and found comparable under visual inspection to cell/encapsulant delamination seen in some fielded modules. Four commercial modules were put through this sequence, 85 degrees C, 85%, 1000 h damp heat, followed by an intensive PID stress sequence of 72 degrees C, 95% RH, and -1000 V, with the module face grounded using a metal foil. The 60 cell c-Si modules exhibiting the highest current transfer (4.4 center dot 10-4 A) exhibited PID-d at the first inspection after 156 h of PID stress. Effects promoting PID-d aremore » reduced adhesion caused by damp heat, sodium migration further reducing adhesion to the cells, and gaseous products of electrochemical reactions driven by the applied system voltage. A new work item proposal for an IEC test standard to evaluate for PID-d is anticipated.« less

Cost-effectiveness of alternative outpatient pelvic inflammatory disease treatment strategies.

PubMed

Smith, Kenneth J; Ness, Roberta B; Wiesenfeld, Harold C; Roberts, Mark S

2007-12-01

Effectiveness differences between outpatient pelvic inflammatory disease (PID) treatment regimens are uncertain, but significant differences in cost exist. To examine the influence of antibiotic costs on PID therapy cost-effectiveness. The authors used a Markov decision model to estimate the cost-effectiveness of recommended antibiotic regimens for PID and performed a value of information analysis to guide future research. Antibiotic costs vary between USD 43 and USD188. Pairwise comparisons, assuming a hypothetical 1% relative risk reduction in PID complications with the more expensive regimen, showed economically reasonable cost-effectiveness ratios. Value of information and sample size considerations support further investigation to detect 10% PID complication rate differences between regimens with >or=USD 50 cost differences. Within the cost range of recommended regimens, use of more expensive antibiotics would be economically reasonable if relatively small decreases in PID complication rates exist. Further investigation of effectiveness differences between regimens is needed.

Cascade control of superheated steam temperature with neuro-PID controller.

PubMed

Zhang, Jianhua; Zhang, Fenfang; Ren, Mifeng; Hou, Guolian; Fang, Fang

2012-11-01

In this paper, an improved cascade control methodology for superheated processes is developed, in which the primary PID controller is implemented by neural networks trained by minimizing error entropy criterion. The entropy of the tracking error can be estimated recursively by utilizing receding horizon window technique. The measurable disturbances in superheated processes are input to the neuro-PID controller besides the sequences of tracking error in outer loop control system, hence, feedback control is combined with feedforward control in the proposed neuro-PID controller. The convergent condition of the neural networks is analyzed. The implementation procedures of the proposed cascade control approach are summarized. Compared with the neuro-PID controller using minimizing squared error criterion, the proposed neuro-PID controller using minimizing error entropy criterion may decrease fluctuations of the superheated steam temperature. A simulation example shows the advantages of the proposed method. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

[Motion control of moving mirror based on fixed-mirror adjustment in FTIR spectrometer].

PubMed

Li, Zhong-bing; Xu, Xian-ze; Le, Yi; Xu, Feng-qiu; Li, Jun-wei

2012-08-01

The performance of the uniform motion of the moving mirror, which is the only constant motion part in FTIR spectrometer, and the performance of the alignment of the fixed mirror play a key role in FTIR spectrometer, and affect the interference effect and the quality of the spectrogram and may restrict the precision and resolution of the instrument directly. The present article focuses on the research on the uniform motion of the moving mirror and the alignment of the fixed mirror. In order to improve the FTIR spectrometer, the maglev support system was designed for the moving mirror and the phase detection technology was adopted to adjust the tilt angle between the moving mirror and the fixed mirror. This paper also introduces an improved fuzzy PID control algorithm to get the accurate speed of the moving mirror and realize the control strategy from both hardware design and algorithm. The results show that the development of the moving mirror motion control system gets sufficient accuracy and real-time, which can ensure the uniform motion of the moving mirror and the alignment of the fixed mirror.

Wireless Mid-Infrared Spectroscopy Sensor Network for Automatic Carbon Dioxide Fertilization in a Greenhouse Environment.

PubMed

Wang, Jianing; Niu, Xintao; Zheng, Lingjiao; Zheng, Chuantao; Wang, Yiding

2016-11-18

In this paper, a wireless mid-infrared spectroscopy sensor network was designed and implemented for carbon dioxide fertilization in a greenhouse environment. A mid-infrared carbon dioxide (CO₂) sensor based on non-dispersive infrared (NDIR) with the functionalities of wireless communication and anti-condensation prevention was realized as the sensor node. Smart transmission power regulation was applied in the wireless sensor network, according to the Received Signal Strength Indication (RSSI), to realize high communication stability and low-power consumption deployment. Besides real-time monitoring, this system also provides a CO₂ control facility for manual and automatic control through a LabVIEW platform. According to simulations and field tests, the implemented sensor node has a satisfying anti-condensation ability and reliable measurement performance on CO₂ concentrations ranging from 30 ppm to 5000 ppm. As an application, based on the Fuzzy proportional, integral, and derivative (PID) algorithm realized on a LabVIEW platform, the CO₂ concentration was regulated to some desired concentrations, such as 800 ppm and 1200 ppm, in 30 min with a controlled fluctuation of <±35 ppm in an acre of greenhouse.

Real-time seam tracking control system based on line laser visions

NASA Astrophysics Data System (ADS)

Zou, Yanbiao; Wang, Yanbo; Zhou, Weilin; Chen, Xiangzhi

2018-07-01

A set of six-degree-of-freedom robotic welding automatic tracking platform was designed in this study to realize the real-time tracking of weld seams. Moreover, the feature point tracking method and the adaptive fuzzy control algorithm in the welding process were studied and analyzed. A laser vision sensor and its measuring principle were designed and studied, respectively. Before welding, the initial coordinate values of the feature points were obtained using morphological methods. After welding, the target tracking method based on Gaussian kernel was used to extract the real-time feature points of the weld. An adaptive fuzzy controller was designed to input the deviation value of the feature points and the change rate of the deviation into the controller. The quantization factors, scale factor, and weight function were adjusted in real time. The input and output domains, fuzzy rules, and membership functions were constantly updated to generate a series of smooth bias robot voltage. Three groups of experiments were conducted on different types of curve welds in a strong arc and splash noise environment using the welding current of 120 A short-circuit Metal Active Gas (MAG) Arc Welding. The tracking error was less than 0.32 mm and the sensor's metrical frequency can be up to 20 Hz. The end of the torch run smooth during welding. Weld trajectory can be tracked accurately, thereby satisfying the requirements of welding applications.

FIR: An Effective Scheme for Extracting Useful Metadata from Social Media.

PubMed

Chen, Long-Sheng; Lin, Zue-Cheng; Chang, Jing-Rong

2015-11-01

Recently, the use of social media for health information exchange is expanding among patients, physicians, and other health care professionals. In medical areas, social media allows non-experts to access, interpret, and generate medical information for their own care and the care of others. Researchers paid much attention on social media in medical educations, patient-pharmacist communications, adverse drug reactions detection, impacts of social media on medicine and healthcare, and so on. However, relatively few papers discuss how to extract useful knowledge from a huge amount of textual comments in social media effectively. Therefore, this study aims to propose a Fuzzy adaptive resonance theory network based Information Retrieval (FIR) scheme by combining Fuzzy adaptive resonance theory (ART) network, Latent Semantic Indexing (LSI), and association rules (AR) discovery to extract knowledge from social media. In our FIR scheme, Fuzzy ART network firstly has been employed to segment comments. Next, for each customer segment, we use LSI technique to retrieve important keywords. Then, in order to make the extracted keywords understandable, association rules mining is presented to organize these extracted keywords to build metadata. These extracted useful voices of customers will be transformed into design needs by using Quality Function Deployment (QFD) for further decision making. Unlike conventional information retrieval techniques which acquire too many keywords to get key points, our FIR scheme can extract understandable metadata from social media.

Fuzzy logic path planning system for collision avoidance by an autonomous rover vehicle

NASA Technical Reports Server (NTRS)

Murphy, Michael G.

1993-01-01

The Space Exploration Initiative of the United States will make great demands upon NASA and its limited resources. One aspect of great importance will be providing for autonomous (unmanned) operation of vehicles and/or subsystems in space flight and surface exploration. An additional, complicating factor is that much of the need for autonomy of operation will take place under conditions of great uncertainty or ambiguity. Issues in developing an autonomous collision avoidance subsystem within a path planning system for application in a remote, hostile environment that does not lend itself well to remote manipulation by Earth-based telecommunications is addressed. A good focus is unmanned surface exploration of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. Four major issues addressed are (1) avoidance of a fuzzy moving obstacle; (2) backoff from a deadend in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system. Examples of the need for collision avoidance by an autonomous rover vehicle on the surface of Mars with a moving obstacle would be wind-blown debris, surface flow or anomalies due to subsurface disturbances, another vehicle, etc. The other issues of backoff, sensor fusion, and adaptive learning are important in the overall path planning system.

Appraisal of adaptive neuro-fuzzy computing technique for estimating anti-obesity properties of a medicinal plant.

PubMed

Kazemipoor, Mahnaz; Hajifaraji, Majid; Radzi, Che Wan Jasimah Bt Wan Mohamed; Shamshirband, Shahaboddin; Petković, Dalibor; Mat Kiah, Miss Laiha

2015-01-01

This research examines the precision of an adaptive neuro-fuzzy computing technique in estimating the anti-obesity property of a potent medicinal plant in a clinical dietary intervention. Even though a number of mathematical functions such as SPSS analysis have been proposed for modeling the anti-obesity properties estimation in terms of reduction in body mass index (BMI), body fat percentage, and body weight loss, there are still disadvantages of the models like very demanding in terms of calculation time. Since it is a very crucial problem, in this paper a process was constructed which simulates the anti-obesity activities of caraway (Carum carvi) a traditional medicine on obese women with adaptive neuro-fuzzy inference (ANFIS) method. The ANFIS results are compared with the support vector regression (SVR) results using root-mean-square error (RMSE) and coefficient of determination (R(2)). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The following statistical characteristics are obtained for BMI loss estimation: RMSE=0.032118 and R(2)=0.9964 in ANFIS testing and RMSE=0.47287 and R(2)=0.361 in SVR testing. For fat loss estimation: RMSE=0.23787 and R(2)=0.8599 in ANFIS testing and RMSE=0.32822 and R(2)=0.7814 in SVR testing. For weight loss estimation: RMSE=0.00000035601 and R(2)=1 in ANFIS testing and RMSE=0.17192 and R(2)=0.6607 in SVR testing. Because of that, it can be applied for practical purposes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Increased risk of borderline ovarian tumors in women with a history of pelvic inflammatory disease: A nationwide population-based cohort study.

PubMed

Rasmussen, Christina B; Jensen, Allan; Albieri, Vanna; Andersen, Klaus K; Kjaer, Susanne K

2016-11-01

Some studies suggest that pelvic inflammatory disease (PID) is a potential risk factor for ovarian cancer. However, only few studies have investigated the association between PID and risk of borderline ovarian tumors. We conducted a population-based cohort study to investigate the association between PID and risk of borderline ovarian tumors. Using various nationwide Danish registries we identified all women in Denmark during 1978-2012, who were born during 1940-1970 (n=1,318,925). Of these, 81,263 women were diagnosed with PID in the study period, and 2736 women had a borderline ovarian tumor (1290 serous and 1344 mucinous). Hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between PID and risk of borderline tumors were estimated using Cox regression models with adjustment for potential confounders. A history of PID was associated with an increased risk of borderline ovarian tumors (HR=1.39; 95% CI: 1.19-1.61). However, histotype-specific analyses revealed significant variation in risk as PID was only associated with an increased risk of serous borderline tumors (HR=1.85; 95% CI: 1.52-2.24), but not with mucinous borderline tumors (HR=1.06; 95% CI: 0.83-1.35). PID is associated with an increased risk of serous borderline tumors. Further research on the potential underlying biological mechanisms and on the identification of the subset of women with PID who are at increased risk of serous borderline tumors is warranted. Copyright © 2016 Elsevier Inc. All rights reserved.

Pelvic Inflammatory Disease and the Risk of Ovarian Cancer and Borderline Ovarian Tumors: A Pooled Analysis of 13 Case-Control Studies

PubMed Central

Rasmussen, Christina B.; Kjaer, Susanne K.; Albieri, Vanna; Bandera, Elisa V.; Doherty, Jennifer A.; Høgdall, Estrid; Webb, Penelope M.; Jordan, Susan J.; Rossing, Mary Anne; Wicklund, Kristine G.; Goodman, Marc T.; Modugno, Francesmary; Moysich, Kirsten B.; Ness, Roberta B.; Edwards, Robert P.; Schildkraut, Joellen M.; Berchuck, Andrew; Olson, Sara H.; Kiemeney, Lambertus A.; Massuger, Leon F. A. G.; Narod, Steven A.; Phelan, Catherine M.; Anton-Culver, Hoda; Ziogas, Argyrios; Wu, Anna H.; Pearce, Celeste L.; Risch, Harvey A.; Jensen, Allan

2017-01-01

Inflammation has been implicated in ovarian carcinogenesis. However, studies investigating the association between pelvic inflammatory disease (PID) and ovarian cancer risk are few and inconsistent. We investigated the association between PID and the risk of epithelial ovarian cancer according to tumor behavior and histotype. We pooled data from 13 case-control studies, conducted between 1989 and 2009, from the Ovarian Cancer Association Consortium (OCAC), including 9,162 women with ovarian cancers, 2,354 women with borderline tumors, and 14,736 control participants. Study-specific odds ratios were estimated and subsequently combined into a pooled odds ratio using a random-effects model. A history of PID was associated with an increased risk of borderline tumors (pooled odds ratio (pOR) = 1.32, 95% confidence interval (CI): 1.10, 1.58). Women with at least 2 episodes of PID had a 2-fold increased risk of borderline tumors (pOR = 2.14, 95% CI: 1.08, 4.24). No association was observed between PID and ovarian cancer risk overall (pOR = 0.99, 95% CI: 0.83, 1.19); however, a statistically nonsignificantly increased risk of low-grade serous tumors (pOR = 1.48, 95% CI: 0.92, 2.38) was noted. In conclusion, PID was associated with an increased risk of borderline ovarian tumors, particularly among women who had had multiple episodes of PID. Although our results indicated a histotype-specific association with PID, the association of PID with ovarian cancer risk is still somewhat uncertain and requires further investigation. PMID:27941069

Development of a Clear Fiber Cherenkov Counter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaneko, N.; Han, S.; Ito, H.

2015-07-01

We have developed a new PID detector consists of clear fibers. PID efficiency was measured with 470 MeV e{sup +} beam. As a result, this detector with thickness of 5 cm has the PID efficiency of 95 %. (authors)

TOXIRAE PRO PID

EPA Science Inventory

The ToxiRAE Pro PID measures total volatile organic compounds (VOCs) using a photoionization detector (PID). This sensor can be programmed to measure concentrations of a specified compound automatically and has a real time reading of VOC concentrations in parts per million (ppm) ...

The dynamic origins of positive health and wellbeing

PubMed Central

Cloninger, C. Robert; Salloum, Ihsan M.; Mezzich, Juan E.

2015-01-01

The causes of wellbeing and illbeing interact with feedback dynamics resulting in the same set of traits giving rise to a variety of health outcomes (multi-finality) and different traits giving rise to the same health outcome (equi-finality). As a result, a full understanding of health and its disorders must be in terms of a complex adaptive system of causes, rather than in terms of categorical diagnoses or sets of symptoms. The three domains of person-centered integrative diagnosis (PID) are considered here as interacting components of a complex adaptive system comprised of health status (functioning/wellness versus disability/disorder), experience of health (self-awareness/fulfillment versus misunderstanding/suffering) and contributors to health (protective versus risk factors). The PID domains thereby allow healthcare and health promotion to be understood in terms of measurable components of a complex adaptive system. Three major concepts of health are examined in detail to identify their dynamic origins: Psychological Maturity, Flourishing and Resilience. In humanistic psychology, psychological maturity (i.e. healthy personality, mental wellbeing) involves the development of high self-directedness, high co-operativeness and high self-transcendence, but self-transcendence is nevertheless devalued in individualistic and materialistic cultures except when people must face adversity and ultimate situations like suffering or the threat of death. Psychological Maturity develops through two complementary processes often labeled as Flourishing and Resilience. Flourishing is the development of one’s potential to live optimally, especially as the result of favorable circumstances, whereas Resilience is positive adaptation to life despite adverse circumstances. As a result of the complex feedback dynamics between the processes of flourishing and resilience, each person is a unique individual who has a variety of paths for achieving positive health and wellbeing open to him or her. Person-centered health promotion and care can thereby be approached as a creative life project that can be conducted with the assistance of healthcare workers who are both therapeutic allies and well-informed experts. PMID:26140189