Photorefractive optical fuzzy-logic processor based on grating degeneracy

NASA Astrophysics Data System (ADS)

Wu, Weishu; Yang, Changxi; Campbell, Scott; Yeh, Pochi

1995-04-01

A novel optical fuzzy-logic processor using light-induced gratings in photorefractive crystals is proposed and demonstrated. By exploiting grating degeneracy, one can easily implement parallel fuzzy-logic functions in disjunctive normal form.

The cognitive bases for the design of a new class of fuzzy logic controllers: The clearness transformation fuzzy logic controller

NASA Technical Reports Server (NTRS)

Sultan, Labib; Janabi, Talib

1992-01-01

This paper analyses the internal operation of fuzzy logic controllers as referenced to the human cognitive tasks of control and decision making. Two goals are targeted. The first goal focuses on the cognitive interpretation of the mechanisms employed in the current design of fuzzy logic controllers. This analysis helps to create a ground to explore the potential of enhancing the functional intelligence of fuzzy controllers. The second goal is to outline the features of a new class of fuzzy controllers, the Clearness Transformation Fuzzy Logic Controller (CT-FLC), whereby some new concepts are advanced to qualify fuzzy controllers as 'cognitive devices' rather than 'expert system devices'. The operation of the CT-FLC, as a fuzzy pattern processing controller, is explored, simulated, and evaluated.

Commercial applications

NASA Technical Reports Server (NTRS)

Togai, Masaki

1990-01-01

Viewgraphs on commercial applications of fuzzy logic in Japan are presented. Topics covered include: suitable application area of fuzzy theory; characteristics of fuzzy control; fuzzy closed-loop controller; Mitsubishi heavy air conditioner; predictive fuzzy control; the Sendai subway system; automatic transmission; fuzzy logic-based command system for antilock braking system; fuzzy feed-forward controller; and fuzzy auto-tuning system.

Online energy management strategy of fuel cell hybrid electric vehicles based on data fusion approach

NASA Astrophysics Data System (ADS)

Zhou, Daming; Al-Durra, Ahmed; Gao, Fei; Ravey, Alexandre; Matraji, Imad; Godoy Simões, Marcelo

2017-10-01

Energy management strategy plays a key role for Fuel Cell Hybrid Electric Vehicles (FCHEVs), it directly affects the efficiency and performance of energy storages in FCHEVs. For example, by using a suitable energy distribution controller, the fuel cell system can be maintained in a high efficiency region and thus saving hydrogen consumption. In this paper, an energy management strategy for online driving cycles is proposed based on a combination of the parameters from three offline optimized fuzzy logic controllers using data fusion approach. The fuzzy logic controllers are respectively optimized for three typical driving scenarios: highway, suburban and city in offline. To classify patterns of online driving cycles, a Probabilistic Support Vector Machine (PSVM) is used to provide probabilistic classification results. Based on the classification results of the online driving cycle, the parameters of each offline optimized fuzzy logic controllers are then fused using Dempster-Shafer (DS) evidence theory, in order to calculate the final parameters for the online fuzzy logic controller. Three experimental validations using Hardware-In-the-Loop (HIL) platform with different-sized FCHEVs have been performed. Experimental comparison results show that, the proposed PSVM-DS based online controller can achieve a relatively stable operation and a higher efficiency of fuel cell system in real driving cycles.

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.

Fuzzy and neural control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1992-01-01

Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

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.

Modal control of a plate using a fuzzy logic controller

NASA Astrophysics Data System (ADS)

Sharma, Manu; Singh, S. P.; Sachdeva, B. L.

2007-08-01

This paper presents fuzzy logic based independent modal space control (IMSC) and fuzzy logic based modified independent modal space control (MIMSC) of vibration. The rule base of the controller consists of nine rules, which have been derived based upon simple human reasoning. Input to the controller consists of the first two modal displacements and velocities of the structure and the output of the controller is the modal force to be applied by the actuator. Fuzzy logic is used in such a way that the actuator is never called to apply effort which is beyond safe limits and also the operator is saved from calculating control gains. The proposed fuzzy controller is experimentally tested for active vibration control of a cantilevered plate. A piezoelectric patch is used as a sensor to sense vibrations of the plate and another piezoelectric patch is used as an actuator to control vibrations of the plate. For analytical formulation, a finite element method based upon Hamilton's principle is used to model the plate. For experimentation, the first two modes of the plate are observed using a Kalman observer. Real-time experiments are performed to control the first mode, the second mode and both modes simultaneously. Experiments are also performed to control the first mode by IMSC, the second mode by IMSC and both modes simultaneously by MIMSC. It is found that for the same decibel reduction in the first mode, the voltage applied by the fuzzy logic based controller is less than that applied by IMSC. While controlling the second mode by IMSC, a considerable amount of spillover is observed in the first mode and region just after the second mode, whereas while controlling the second mode by fuzzy logic, spillover effects are much smaller. While controlling two modes simultaneously, with a single sensor/actuator pair, appreciable resonance control is observed both with fuzzy logic based MIMSC as well as with direct MIMSC, but there is a considerable amount of spillover in the off-resonance region. This may be due to the sub-optimal location and/or an insufficient number of actuators. So, another smart plate with two piezoelectric actuators and one piezoelectric sensor is considered. Piezoelectric patches are fixed in an area where modal strains are high. With this configuration of the smart plate, experiments are conducted to control the first three modes of the plate and it is found that spillover effects are greatly reduced.

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

Systematic methods for the design of a class of fuzzy logic controllers

NASA Astrophysics Data System (ADS)

Yasin, Saad Yaser

2002-09-01

Fuzzy logic control, a relatively new branch of control, can be used effectively whenever conventional control techniques become inapplicable or impractical. Various attempts have been made to create a generalized fuzzy control system and to formulate an analytically based fuzzy control law. In this study, two methods, the left and right parameterization method and the normalized spline-base membership function method, were utilized for formulating analytical fuzzy control laws in important practical control applications. The first model was used to design an idle speed controller, while the second was used to control an inverted control problem. The results of both showed that a fuzzy logic control system based on the developed models could be used effectively to control highly nonlinear and complex systems. This study also investigated the application of fuzzy control in areas not fully utilizing fuzzy logic control. Three important practical applications pertaining to the automotive industries were studied. The first automotive-related application was the idle speed of spark ignition engines, using two fuzzy control methods: (1) left and right parameterization, and (2) fuzzy clustering techniques and experimental data. The simulation and experimental results showed that a conventional controller-like performance fuzzy controller could be designed based only on experimental data and intuitive knowledge of the system. In the second application, the automotive cruise control problem, a fuzzy control model was developed using parameters adaptive Proportional plus Integral plus Derivative (PID)-type fuzzy logic controller. Results were comparable to those using linearized conventional PID and linear quadratic regulator (LQR) controllers and, in certain cases and conditions, the developed controller outperformed the conventional PID and LQR controllers. The third application involved the air/fuel ratio control problem, using fuzzy clustering techniques, experimental data, and a conversion algorithm, to develop a fuzzy-based control algorithm. Results were similar to those obtained by recently published conventional control based studies. The influence of the fuzzy inference operators and parameters on performance and stability of the fuzzy logic controller was studied Results indicated that, the selections of certain parameters or combinations of parameters, affect greatly the performance and stability of the fuzzy controller. Diagnostic guidelines used to tune or change certain factors or parameters to improve controller performance were developed based on knowledge gained from conventional control methods and knowledge gained from the experimental and the simulation results of this study.

Fuzzy logic electric vehicle regenerative antiskid braking and traction control system

DOEpatents

Cikanek, S.R.

1994-10-25

An regenerative antiskid braking and traction control system using fuzzy logic for an electric or hybrid vehicle having a regenerative braking system operatively connected to an electric traction motor, and a separate hydraulic braking system includes sensors for monitoring present vehicle parameters and a processor, responsive to the sensors, for calculating vehicle parameters defining the vehicle behavior not directly measurable by the sensor and determining if regenerative antiskid braking control, requiring hydraulic braking control, and requiring traction control are required. The processor then employs fuzzy logic based on the determined vehicle state and provides command signals to a motor controller to control operation of the electric traction motor and to the brake controller to control fluid pressure applied at each vehicle wheel to provide the appropriate regenerative braking control, hydraulic braking control, and traction control. 123 figs.

Fuzzy logic electric vehicle regenerative antiskid braking and traction control system

DOEpatents

Cikanek, Susan R.

1994-01-01

An regenerative antiskid braking and traction control system using fuzzy logic for an electric or hybrid vehicle having a regenerative braking system operatively connected to an electric traction motor, and a separate hydraulic braking system includes sensors for monitoring present vehicle parameters and a processor, responsive to the sensors, for calculating vehicle parameters defining the vehicle behavior not directly measurable by the sensor and determining if regenerative antiskid braking control, requiring hydraulic braking control, and requiring traction control are required. The processor then employs fuzzy logic based on the determined vehicle state and provides command signals to a motor controller to control operation of the electric traction motor and to the brake controller to control fluid pressure applied at each vehicle wheel to provide the appropriate regenerative braking control, hydraulic braking control, and traction control.

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

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.

Analysis of atomic force microscopy data for surface characterization using fuzzy logic

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

Al-Mousa, Amjed, E-mail: aalmousa@vt.edu; Niemann, Darrell L.; Niemann, Devin J.

2011-07-15

In this paper we present a methodology to characterize surface nanostructures of thin films. The methodology identifies and isolates nanostructures using Atomic Force Microscopy (AFM) data and extracts quantitative information, such as their size and shape. The fuzzy logic based methodology relies on a Fuzzy Inference Engine (FIE) to classify the data points as being top, bottom, uphill, or downhill. The resulting data sets are then further processed to extract quantitative information about the nanostructures. In the present work we introduce a mechanism which can consistently distinguish crowded surfaces from those with sparsely distributed structures and present an omni-directional searchmore » technique to improve the structural recognition accuracy. In order to demonstrate the effectiveness of our approach we present a case study which uses our approach to quantitatively identify particle sizes of two specimens each with a unique gold nanoparticle size distribution. - Research Highlights: {yields} A Fuzzy logic analysis technique capable of characterizing AFM images of thin films. {yields} The technique is applicable to different surfaces regardless of their densities. {yields} Fuzzy logic technique does not require manual adjustment of the algorithm parameters. {yields} The technique can quantitatively capture differences between surfaces. {yields} This technique yields more realistic structure boundaries compared to other methods.« less

Fuzzy logic feedback control for fed-batch enzymatic hydrolysis of lignocellulosic biomass.

PubMed

Tai, Chao; Voltan, Diego S; Keshwani, Deepak R; Meyer, George E; Kuhar, Pankaj S

2016-06-01

A fuzzy logic feedback control system was developed for process monitoring and feeding control in fed-batch enzymatic hydrolysis of a lignocellulosic biomass, dilute acid-pretreated corn stover. Digested glucose from hydrolysis reaction was assigned as input while doser feeding time and speed of pretreated biomass were responses from fuzzy logic control system. Membership functions for these three variables and rule-base were created based on batch hydrolysis data. The system response was first tested in LabVIEW environment then the performance was evaluated through real-time hydrolysis reaction. The feeding operations were determined timely by fuzzy logic control system and efficient responses were shown to plateau phases during hydrolysis. Feeding of proper amount of cellulose and maintaining solids content was well balanced. Fuzzy logic proved to be a robust and effective online feeding control tool for fed-batch enzymatic hydrolysis.

A genetic algorithms approach for altering the membership functions in fuzzy logic controllers

NASA Technical Reports Server (NTRS)

Shehadeh, Hana; Lea, Robert N.

1992-01-01

Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.

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.

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.

Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor

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

Ondrej Linda; Todd Vollmer; Jason Wright

Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrainedmore » computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.« less

A composite self tuning strategy for fuzzy control of dynamic systems

NASA Technical Reports Server (NTRS)

Shieh, C.-Y.; Nair, Satish S.

1992-01-01

The feature of self learning makes fuzzy logic controllers attractive in control applications. This paper proposes a strategy to tune the fuzzy logic controller on-line by tuning the data base as well as the rule base. The structure of the controller is outlined and preliminary results are presented using simulation studies.

Fuzzy logic-based flight control system design

NASA Astrophysics Data System (ADS)

Nho, Kyungmoon

The application of fuzzy logic to aircraft motion control is studied in this dissertation. The self-tuning fuzzy techniques are developed by changing input scaling factors to obtain a robust fuzzy controller over a wide range of operating conditions and nonlinearities for a nonlinear aircraft model. It is demonstrated that the properly adjusted input scaling factors can meet the required performance and robustness in a fuzzy controller. For a simple demonstration of the easy design and control capability of a fuzzy controller, a proportional-derivative (PD) fuzzy control system is compared to the conventional controller for a simple dynamical system. This thesis also describes the design principles and stability analysis of fuzzy control systems by considering the key features of a fuzzy control system including the fuzzification, rule-base and defuzzification. The wing-rock motion of slender delta wings, a linear aircraft model and the six degree of freedom nonlinear aircraft dynamics are considered to illustrate several self-tuning methods employing change in input scaling factors. Finally, this dissertation is concluded with numerical simulation of glide-slope capture in windshear demonstrating the robustness of the fuzzy logic based flight control system.

Fuzzy Energy Management for a Catenary-Battery-Ultracapacitor based Hybrid Tramway

NASA Astrophysics Data System (ADS)

Jibin, Yang; Jiye, Zhang; Pengyun, Song

2017-05-01

In this paper, an energy management strategy (EMS) based on fuzzy logic control for a catenary-battery-ultracapacitor powered hybrid modern tramway was presented. The fuzzy logic controller for the catenary zone and catenary-less zone was respectively designed by analyzing the structure and working mode of the hybrid system, then an energy management strategy based on double fuzzy logic control was proposed to enhance the fuel economy. The hybrid modern tramway simulation model was developed based on MATLAB/Simulink environment. The simulation results show that the proposed EMS can satisfy the demand of dynamic performance of the tramway and achieve the power distribution reasonably between the each power source.

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

MATLAB Simulation of UPQC for Power Quality Mitigation Using an Ant Colony Based Fuzzy Control Technique

PubMed Central

Kumarasabapathy, N.; Manoharan, P. S.

2015-01-01

This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC) for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both series-active and shunt-active power filters (APFs). The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion. PMID:26504895

Fuzzy pharmacology: theory and applications.

PubMed

Sproule, Beth A; Naranjo, Claudio A; Türksen, I Burhan

2002-09-01

Fuzzy pharmacology is a term coined to represent the application of fuzzy logic and fuzzy set theory to pharmacological problems. Fuzzy logic is the science of reasoning, thinking and inference that recognizes and uses the real world phenomenon that everything is a matter of degree. It is an extension of binary logic that is able to deal with complex systems because it does not require crisp definitions and distinctions for the system components. In pharmacology, fuzzy modeling has been used for the mechanical control of drug delivery in surgical settings, and work has begun evaluating its use in other pharmacokinetic and pharmacodynamic applications. Fuzzy pharmacology is an emerging field that, based on these initial explorations, warrants further investigation.

Applications of fuzzy logic to control and decision making

NASA Technical Reports Server (NTRS)

Lea, Robert N.; Jani, Yashvant

1991-01-01

Long range space missions will require high operational efficiency as well as autonomy to enhance the effectivity of performance. Fuzzy logic technology has been shown to be powerful and robust in interpreting imprecise measurements and generating appropriate control decisions for many space operations. Several applications are underway, studying the fuzzy logic approach to solving control and decision making problems. Fuzzy logic algorithms for relative motion and attitude control have been developed and demonstrated for proximity operations. Based on this experience, motion control algorithms that include obstacle avoidance were developed for a Mars Rover prototype for maneuvering during the sample collection process. A concept of an intelligent sensor system that can identify objects and track them continuously and learn from its environment is under development to support traffic management and proximity operations around the Space Station Freedom. For safe and reliable operation of Lunar/Mars based crew quarters, high speed controllers with ability to combine imprecise measurements from several sensors is required. A fuzzy logic approach that uses high speed fuzzy hardware chips is being studied.

Fuzzy Logic Controlled Solar Module for Driving Three- Phase Induction Motor

NASA Astrophysics Data System (ADS)

Afiqah Zainal, Nurul; Sooi Tat, Chan; Ajisman

2016-02-01

Renewable energy produced by solar module gives advantages for generated three- phase induction motor in remote area. But, solar module's ou tput is uncertain and complex. Fuzzy logic controller is one of controllers that can handle non-linear system and maximum power of solar module. Fuzzy logic controller used for Maximum Power Point Tracking (MPPT) technique to control Pulse-Width Modulation (PWM) for switching power electronics circuit. DC-DC boost converter used to boost up photovoltaic voltage to desired output and supply voltage source inverter which controlled by three-phase PWM generated by microcontroller. IGBT switched Voltage source inverter (VSI) produced alternating current (AC) voltage from direct current (DC) source to control speed of three-phase induction motor from boost converter output. Results showed that, the output power of solar module is optimized and controlled by using fuzzy logic controller. Besides that, the three-phase induction motor can be drive and control using VSI switching by the PWM signal generated by the fuzzy logic controller. This concluded that the non-linear system can be controlled and used in driving three-phase induction motor.

Simulation and experiment of a fuzzy logic based MPPT controller for a small wind turbine system

NASA Astrophysics Data System (ADS)

Petrila, Diana; Muntean, Nicolae

2012-09-01

This paper describes the development of a fuzzy logic based maximum power point tracking (MPPT) strategy for a variable speed wind turbine system (VSWT). For this scope, a fuzzy logic controller (FLC) was described, simulated and tested on a real time "hardware in the loop" wind turbine emulator. Simulation and experimental results show that the controller is able to track the maximum power point for various wind conditions and validate the proposed control strategy.

Refining fuzzy logic controllers with machine learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1994-01-01

In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.

Life insurance risk assessment using a fuzzy logic expert system

NASA Technical Reports Server (NTRS)

Carreno, Luis A.; Steel, Roy A.

1992-01-01

In this paper, we present a knowledge based system that combines fuzzy processing with rule-based processing to form an improved decision aid for evaluating risk for life insurance. This application illustrates the use of FuzzyCLIPS to build a knowledge based decision support system possessing fuzzy components to improve user interactions and KBS performance. The results employing FuzzyCLIPS are compared with the results obtained from the solution of the problem using traditional numerical equations. The design of the fuzzy solution consists of a CLIPS rule-based system for some factors combined with fuzzy logic rules for others. This paper describes the problem, proposes a solution, presents the results, and provides a sample output of the software product.

Fuzzy logic particle tracking velocimetry

NASA Technical Reports Server (NTRS)

Wernet, Mark P.

1993-01-01

Fuzzy logic has proven to be a simple and robust method for process control. Instead of requiring a complex model of the system, a user defined rule base is used to control the process. In this paper the principles of fuzzy logic control are applied to Particle Tracking Velocimetry (PTV). Two frames of digitally recorded, single exposure particle imagery are used as input. The fuzzy processor uses the local particle displacement information to determine the correct particle tracks. Fuzzy PTV is an improvement over traditional PTV techniques which typically require a sequence (greater than 2) of image frames for accurately tracking particles. The fuzzy processor executes in software on a PC without the use of specialized array or fuzzy logic processors. A pair of sample input images with roughly 300 particle images each, results in more than 200 velocity vectors in under 8 seconds of processing time.

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.

Fuzzy, crisp, and human logic in e-commerce marketing data mining

NASA Astrophysics Data System (ADS)

Hearn, Kelda L.; Zhang, Yanqing

2001-03-01

In today's business world there is an abundance of available data and a great need to make good use of it. Many businesses would benefit from examining customer habits and trends and making marketing and product decisions based on that analysis. However, the process of manually examining data and making sound decisions based on that data is time consuming and often impractical. Intelligent systems that can make judgments similar to human judgments are sorely needed. Thus, systems based on fuzzy logic present themselves as an option to be seriously considered. The work described in this paper attempts to make an initial comparison between fuzzy logic and more traditional hard or crisp logic to see which would make a better substitute for human intervention. In this particular case study, customers are classified into categories that indicate how desirable the customer would be as a prospect for marketing. This classification is based on a small set of customer data. The results from these investigations make it clear that fuzzy logic is more able to think for itself and make decisions that more closely match human decision and is therefore significantly closer to human logic than crisp logic.

Knowledge representation in fuzzy logic

NASA Technical Reports Server (NTRS)

Zadeh, Lotfi A.

1989-01-01

The author presents a summary of the basic concepts and techniques underlying the application of fuzzy logic to knowledge representation. He then describes a number of examples relating to its use as a computational system for dealing with uncertainty and imprecision in the context of knowledge, meaning, and inference. It is noted that one of the basic aims of fuzzy logic is to provide a computational framework for knowledge representation and inference in an environment of uncertainty and imprecision. In such environments, fuzzy logic is effective when the solutions need not be precise and/or it is acceptable for a conclusion to have a dispositional rather than categorical validity. The importance of fuzzy logic derives from the fact that there are many real-world applications which fit these conditions, especially in the realm of knowledge-based systems for decision-making and control.

Fuzzy logic based on-line fault detection and classification in transmission line.

PubMed

Adhikari, Shuma; Sinha, Nidul; Dorendrajit, Thingam

2016-01-01

This study presents fuzzy logic based online fault detection and classification of transmission line using Programmable Automation and Control technology based National Instrument Compact Reconfigurable i/o (CRIO) devices. The LabVIEW software combined with CRIO can perform real time data acquisition of transmission line. When fault occurs in the system current waveforms are distorted due to transients and their pattern changes according to the type of fault in the system. The three phase alternating current, zero sequence and positive sequence current data generated by LabVIEW through CRIO-9067 are processed directly for relaying. The result shows that proposed technique is capable of right tripping action and classification of type of fault at high speed therefore can be employed in practical application.

Answer Sets in a Fuzzy Equilibrium Logic

NASA Astrophysics Data System (ADS)

Schockaert, Steven; Janssen, Jeroen; Vermeir, Dirk; de Cock, Martine

Since its introduction, answer set programming has been generalized in many directions, to cater to the needs of real-world applications. As one of the most general “classical” approaches, answer sets of arbitrary propositional theories can be defined as models in the equilibrium logic of Pearce. Fuzzy answer set programming, on the other hand, extends answer set programming with the capability of modeling continuous systems. In this paper, we combine the expressiveness of both approaches, and define answer sets of arbitrary fuzzy propositional theories as models in a fuzzification of equilibrium logic. We show that the resulting notion of answer set is compatible with existing definitions, when the syntactic restrictions of the corresponding approaches are met. We furthermore locate the complexity of the main reasoning tasks at the second level of the polynomial hierarchy. Finally, as an illustration of its modeling power, we show how fuzzy equilibrium logic can be used to find strong Nash equilibria.

A Fuzzy Logic Based Controller for the Automated Alignment of a Laser-beam-smoothing Spatial Filter

NASA Technical Reports Server (NTRS)

Krasowski, M. J.; Dickens, D. E.

1992-01-01

A fuzzy logic based controller for a laser-beam-smoothing spatial filter is described. It is demonstrated that a human operator's alignment actions can easily be described by a system of fuzzy rules of inference. The final configuration uses inexpensive, off-the-shelf hardware and allows for a compact, readily implemented embedded control system.

Short Term Load Forecasting with Fuzzy Logic Systems for power system planning and reliability-A Review

NASA Astrophysics Data System (ADS)

Holmukhe, R. M.; Dhumale, Mrs. Sunita; Chaudhari, Mr. P. S.; Kulkarni, Mr. P. P.

2010-10-01

Load forecasting is very essential to the operation of Electricity companies. It enhances the energy efficient and reliable operation of power system. Forecasting of load demand data forms an important component in planning generation schedules in a power system. The purpose of this paper is to identify issues and better method for load foecasting. In this paper we focus on fuzzy logic system based short term load forecasting. It serves as overview of the state of the art in the intelligent techniques employed for load forecasting in power system planning and reliability. Literature review has been conducted and fuzzy logic method has been summarized to highlight advantages and disadvantages of this technique. The proposed technique for implementing fuzzy logic based forecasting is by Identification of the specific day and by using maximum and minimum temperature for that day and finally listing the maximum temperature and peak load for that day. The results show that Load forecasting where there are considerable changes in temperature parameter is better dealt with Fuzzy Logic system method as compared to other short term forecasting techniques.

A Comparison of Neural Networks and Fuzzy Logic Methods for Process Modeling

NASA Technical Reports Server (NTRS)

Cios, Krzysztof J.; Sala, Dorel M.; Berke, Laszlo

1996-01-01

The goal of this work was to analyze the potential of neural networks and fuzzy logic methods to develop approximate response surfaces as process modeling, that is for mapping of input into output. Structural response was chosen as an example. Each of the many methods surveyed are explained and the results are presented. Future research directions are also discussed.

Control Law for Automatic Landing Using Fuzzy-Logic Control

NASA Astrophysics Data System (ADS)

Kato, Akio; Inagaki, Yoshiki

The effectiveness of a fuzzy-logic control law for automatically landing an aircraft that handles both the control to lead an aircraft from horizontal flight at an altitude of 500 meters to flight along the glide-path course near the runway, as well as the control to direct the aircraft to land smoothly on a runway, was investigated. The control law for the automatic landing was designed to match the design goals of directing an aircraft from horizontal flight to flight along a glide-path course quickly and smoothly, and for landing smoothly on a runway. The design of the control law and evaluation of the control performance were performed considering the ground effect at landing. As a result, it was confirmed that the design goals were achieved. Even if the characteristics of the aircraft change greatly, the proposed control law is able to maintain the control performance. Moreover, it was confirmed to be able to land an aircraft safely during air turbulence. The present paper indicates that fuzzy-logic control is an effective and flexible method when applied to the control law for automatic landing, and the design method of the control law using fuzzy-logic control was obtained.

Fuzzy logic controllers: A knowledge-based system perspective

NASA Technical Reports Server (NTRS)

Bonissone, Piero P.

1993-01-01

Over the last few years we have seen an increasing number of applications of Fuzzy Logic Controllers. These applications range from the development of auto-focus cameras, to the control of subway trains, cranes, automobile subsystems (automatic transmissions), domestic appliances, and various consumer electronic products. In summary, we consider a Fuzzy Logic Controller to be a high level language with its local semantics, interpreter, and compiler, which enables us to quickly synthesize non-linear controllers for dynamic systems.

Quantified moving average strategy of crude oil futures market based on fuzzy logic rules and genetic algorithms

NASA Astrophysics Data System (ADS)

Liu, Xiaojia; An, Haizhong; Wang, Lijun; Guan, Qing

2017-09-01

The moving average strategy is a technical indicator that can generate trading signals to assist investment. While the trading signals tell the traders timing to buy or sell, the moving average cannot tell the trading volume, which is a crucial factor for investment. This paper proposes a fuzzy moving average strategy, in which the fuzzy logic rule is used to determine the strength of trading signals, i.e., the trading volume. To compose one fuzzy logic rule, we use four types of moving averages, the length of the moving average period, the fuzzy extent, and the recommend value. Ten fuzzy logic rules form a fuzzy set, which generates a rating level that decides the trading volume. In this process, we apply genetic algorithms to identify an optimal fuzzy logic rule set and utilize crude oil futures prices from the New York Mercantile Exchange (NYMEX) as the experiment data. Each experiment is repeated for 20 times. The results show that firstly the fuzzy moving average strategy can obtain a more stable rate of return than the moving average strategies. Secondly, holding amounts series is highly sensitive to price series. Thirdly, simple moving average methods are more efficient. Lastly, the fuzzy extents of extremely low, high, and very high are more popular. These results are helpful in investment decisions.

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.

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.

Intelligent fuzzy controller for event-driven real time systems

NASA Technical Reports Server (NTRS)

Grantner, Janos; Patyra, Marek; Stachowicz, Marian S.

1992-01-01

Most of the known linguistic models are essentially static, that is, time is not a parameter in describing the behavior of the object's model. In this paper we show a model for synchronous finite state machines based on fuzzy logic. Such finite state machines can be used to build both event-driven, time-varying, rule-based systems and the control unit section of a fuzzy logic computer. The architecture of a pipelined intelligent fuzzy controller is presented, and the linguistic model is represented by an overall fuzzy relation stored in a single rule memory. A VLSI integrated circuit implementation of the fuzzy controller is suggested. At a clock rate of 30 MHz, the controller can perform 3 MFLIPS on multi-dimensional fuzzy data.

Design and implementation of the tree-based fuzzy logic controller.

PubMed

Liu, B D; Huang, C Y

1997-01-01

In this paper, a tree-based approach is proposed to design the fuzzy logic controller. Based on the proposed methodology, the fuzzy logic controller has the following merits: the fuzzy control rule can be extracted automatically from the input-output data of the system and the extraction process can be done in one-pass; owing to the fuzzy tree inference structure, the search spaces of the fuzzy inference process are largely reduced; the operation of the inference process can be simplified as a one-dimensional matrix operation because of the fuzzy tree approach; and the controller has regular and modular properties, so it is easy to be implemented by hardware. Furthermore, the proposed fuzzy tree approach has been applied to design the color reproduction system for verifying the proposed methodology. The color reproduction system is mainly used to obtain a color image through the printer that is identical to the original one. In addition to the software simulation, an FPGA is used to implement the prototype hardware system for real-time application. Experimental results show that the effect of color correction is quite good and that the prototype hardware system can operate correctly under the condition of 30 MHz clock rate.

FUZZY-LOGIC-BASED CONTROLLERS FOR EFFICIENCY OPTIMIZATION OF INVERTER-FED INDUCTION MOTOR DRIVES

EPA Science Inventory

This paper describes a fuzzy-logic-based energy optimizing controller to improve the efficiency of induction motor/drives operating at various load (torque) and speed conditions. Improvement of induction motor efficiency is important not only from the considerations of energy sav...

Fuzzy Logic-Based Audio Pattern Recognition

NASA Astrophysics Data System (ADS)

Malcangi, M.

2008-11-01

Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

Data mining for multiagent rules, strategies, and fuzzy decision tree structure

NASA Astrophysics Data System (ADS)

Smith, James F., III; Rhyne, Robert D., II; Fisher, Kristin

2002-03-01

A fuzzy logic based resource manager (RM) has been developed that automatically allocates electronic attack resources in real-time over many dissimilar platforms. Two different data mining algorithms have been developed to determine rules, strategies, and fuzzy decision tree structure. The first data mining algorithm uses a genetic algorithm as a data mining function and is called from an electronic game. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge. It calls a data mining function, a genetic algorithm, for data mining of the database as required and allows easy evaluation of the information mined in the second step. The criterion for re- optimization is discussed as well as experimental results. Then a second data mining algorithm that uses a genetic program as a data mining function is introduced to automatically discover fuzzy decision tree structures. Finally, a fuzzy decision tree generated through this process is discussed.

Fuzzy-logic based strategy for validation of multiplex methods: example with qualitative GMO assays.

PubMed

Bellocchi, Gianni; Bertholet, Vincent; Hamels, Sandrine; Moens, W; Remacle, José; Van den Eede, Guy

2010-02-01

This paper illustrates the advantages that a fuzzy-based aggregation method could bring into the validation of a multiplex method for GMO detection (DualChip GMO kit, Eppendorf). Guidelines for validation of chemical, bio-chemical, pharmaceutical and genetic methods have been developed and ad hoc validation statistics are available and routinely used, for in-house and inter-laboratory testing, and decision-making. Fuzzy logic allows summarising the information obtained by independent validation statistics into one synthetic indicator of overall method performance. The microarray technology, introduced for simultaneous identification of multiple GMOs, poses specific validation issues (patterns of performance for a variety of GMOs at different concentrations). A fuzzy-based indicator for overall evaluation is illustrated in this paper, and applied to validation data for different genetically modified elements. Remarks were drawn on the analytical results. The fuzzy-logic based rules were shown to be applicable to improve interpretation of results and facilitate overall evaluation of the multiplex method.

Application of Fuzzy-Logic Controller and Neural Networks Controller in Gas Turbine Speed Control and Overheating Control and Surge Control on Transient Performance

NASA Astrophysics Data System (ADS)

Torghabeh, A. A.; Tousi, A. M.

2007-08-01

This paper presents Fuzzy Logic and Neural Networks approach to Gas Turbine Fuel schedules. Modeling of non-linear system using feed forward artificial Neural Networks using data generated by a simulated gas turbine program is introduced. Two artificial Neural Networks are used , depicting the non-linear relationship between gas generator speed and fuel flow, and turbine inlet temperature and fuel flow respectively . Off-line fast simulations are used for engine controller design for turbojet engine based on repeated simulation. The Mamdani and Sugeno models are used to expression the Fuzzy system . The linguistic Fuzzy rules and membership functions are presents and a Fuzzy controller will be proposed to provide an Open-Loop control for the gas turbine engine during acceleration and deceleration . MATLAB Simulink was used to apply the Fuzzy Logic and Neural Networks analysis. Both systems were able to approximate functions characterizing the acceleration and deceleration schedules . Surge and Flame-out avoidance during acceleration and deceleration phases are then checked . Turbine Inlet Temperature also checked and controls by Neural Networks controller. This Fuzzy Logic and Neural Network Controllers output results are validated and evaluated by GSP software . The validation results are used to evaluate the generalization ability of these artificial Neural Networks and Fuzzy Logic controllers.

Visual-area coding technique (VACT): optical parallel implementation of fuzzy logic and its visualization with the digital-halftoning process

NASA Astrophysics Data System (ADS)

Konishi, Tsuyoshi; Tanida, Jun; Ichioka, Yoshiki

1995-06-01

A novel technique, the visual-area coding technique (VACT), for the optical implementation of fuzzy logic with the capability of visualization of the results is presented. This technique is based on the microfont method and is considered to be an instance of digitized analog optical computing. Huge amounts of data can be processed in fuzzy logic with the VACT. In addition, real-time visualization of the processed result can be accomplished.

JPRS Report, Science & Technology, Japan, Key Tech Center Advanced Communications Research

DTIC Science & Technology

1990-02-26

networks. 27 b. Fuzzy access Even when correct information regarding the connection destination is not available, this makes it possible to establish...a connection based on the stored fuzzy information. c. Logical accessing Makes it possible to effect a connection based on the logical name (indivi...understand fuzzy indications from the user. (b) Normality check tests The following tests should be conducted to check the normality of user- defined services

Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks

PubMed Central

Xia, Feng; Zhao, Wenhong; Sun, Youxian; Tian, Yu-Chu

2007-01-01

Wireless sensor/actuator networks (WSANs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSANs. In particular, quality-of-service (QoS) management in WSANs remains an important issue yet to be investigated. As an attempt in this direction, this paper develops a fuzzy logic control based QoS management (FLC-QM) scheme for WSANs with constrained resources and in dynamic and unpredictable environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSANs. It utilizes a fuzzy logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the actuator. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved. The FLC-QM has the advantages of generality, scalability, and simplicity. Simulation results show that the FLC-QM can provide WSANs with QoS support. PMID:28903288

Professional Learning: A Fuzzy Logic-Based Modelling Approach

ERIC Educational Resources Information Center

Gravani, M. N.; Hadjileontiadou, S. J.; Nikolaidou, G. N.; Hadjileontiadis, L. J.

2007-01-01

Studies have suggested that professional learning is influenced by two key parameters, i.e., climate and planning, and their associated variables (mutual respect, collaboration, mutual trust, supportiveness, openness). In this paper, we applied analysis of the relationships between the proposed quantitative, fuzzy logic-based model and a series of…

Incorporation of negative rules and evolution of a fuzzy controller for yeast fermentation process.

PubMed

Birle, Stephan; Hussein, Mohamed Ahmed; Becker, Thomas

2016-08-01

The control of bioprocesses can be very challenging due to the fact that these kinds of processes are highly affected by various sources of uncertainty like the intrinsic behavior of the used microorganisms. Due to the reason that these kinds of process uncertainties are not directly measureable in most cases, the overall control is either done manually because of the experience of the operator or intelligent expert systems are applied, e.g., on the basis of fuzzy logic theory. In the latter case, however, the control concept is mainly represented by using merely positive rules, e.g., "If A then do B". As this is not straightforward with respect to the semantics of the human decision-making process that also includes negative experience in form of constraints or prohibitions, the incorporation of negative rules for process control based on fuzzy logic is emphasized. In this work, an approach of fuzzy logic control of the yeast propagation process based on a combination of positive and negative rules is presented. The process is guided along a reference trajectory for yeast cell concentration by alternating the process temperature. The incorporation of negative rules leads to a much more stable and accurate control of the process as the root mean squared error of reference trajectory and system response could be reduced by an average of 62.8 % compared to the controller using only positive rules.

Optical Generation of Fuzzy-Based Rules

NASA Astrophysics Data System (ADS)

Gur, Eran; Mendlovic, David; Zalevsky, Zeev

2002-08-01

In the last third of the 20th century, fuzzy logic has risen from a mathematical concept to an applicable approach in soft computing. Today, fuzzy logic is used in control systems for various applications, such as washing machines, train-brake systems, automobile automatic gear, and so forth. The approach of optical implementation of fuzzy inferencing was given by the authors in previous papers, giving an extra emphasis to applications with two dominant inputs. In this paper the authors introduce a real-time optical rule generator for the dual-input fuzzy-inference engine. The paper briefly goes over the dual-input optical implementation of fuzzy-logic inferencing. Then, the concept of constructing a set of rules from given data is discussed. Next, the authors show ways to implement this procedure optically. The discussion is accompanied by an example that illustrates the transformation from raw data into fuzzy set rules.

A survey of fuzzy logic monitoring and control utilisation in medicine.

PubMed

Mahfouf, M; Abbod, M F; Linkens, D A

2001-01-01

Intelligent systems have appeared in many technical areas, such as consumer electronics, robotics and industrial control systems. Many of these intelligent systems are based on fuzzy control strategies which describe complex systems mathematical models in terms of linguistic rules. Since the 1980s new techniques have appeared from which fuzzy logic has been applied extensively in medical systems. The justification for such intelligent systems driven solutions is that biological systems are so complex that the development of computerised systems within such environments is not always a straightforward exercise. In practice, a precise model may not exist for biological systems or it may be too difficult to model. In most cases fuzzy logic is considered to be an ideal tool as human minds work from approximate data, extract meaningful information and produce crisp solutions. This paper surveys the utilisation of fuzzy logic control and monitoring in medical sciences with an analysis of its possible future penetration.

Single board system for fuzzy inference

NASA Technical Reports Server (NTRS)

Symon, James R.; Watanabe, Hiroyuki

1991-01-01

The very large scale integration (VLSI) implementation of a fuzzy logic inference mechanism allows the use of rule-based control and decision making in demanding real-time applications. Researchers designed a full custom VLSI inference engine. The chip was fabricated using CMOS technology. The chip consists of 688,000 transistors of which 476,000 are used for RAM memory. The fuzzy logic inference engine board system incorporates the custom designed integrated circuit into a standard VMEbus environment. The Fuzzy Logic system uses Transistor-Transistor Logic (TTL) parts to provide the interface between the Fuzzy chip and a standard, double height VMEbus backplane, allowing the chip to perform application process control through the VMEbus host. High level C language functions hide details of the hardware system interface from the applications level programmer. The first version of the board was installed on a robot at Oak Ridge National Laboratory in January of 1990.

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.

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.

Combining fuzzy mathematics with fuzzy logic to solve business management problems

NASA Astrophysics Data System (ADS)

Vrba, Joseph A.

1993-12-01

Fuzzy logic technology has been applied to control problems with great success. Because of this, many observers fell that fuzzy logic is applicable only in the control arena. However, business management problems almost never deal with crisp values. Fuzzy systems technology--a combination of fuzzy logic, fuzzy mathematics and a graphical user interface--is a natural fit for developing software to assist in typical business activities such as planning, modeling and estimating. This presentation discusses how fuzzy logic systems can be extended through the application of fuzzy mathematics and the use of a graphical user interface to make the information contained in fuzzy numbers accessible to business managers. As demonstrated through examples from actual deployed systems, this fuzzy systems technology has been employed successfully to provide solutions to the complex real-world problems found in the business environment.

DC motor speed control using fuzzy logic controller

NASA Astrophysics Data System (ADS)

Ismail, N. L.; Zakaria, K. A.; Nazar, N. S. Moh; Syaripuddin, M.; Mokhtar, A. S. N.; Thanakodi, S.

2018-02-01

The automatic control has played a vital role in the advance of engineering and science. Nowadays in industries, the control of direct current (DC) motor is a common practice thus the implementation of DC motor controller speed is important. The main purpose of motor speed control is to keep the rotation of the motor at the present speed and to drive a system at the demand speed. The main purpose of this project is to control speed of DC Series Wound Motor using Fuzzy Logic Controller (FLC). The expectation of this project is the Fuzzy Logic Controller will get the best performance compared to dc motor without controller in terms of settling time (Ts), rise time (Tr), peak time (Tp) and percent overshoot (%OS).

Fuzzy logic techniques for rendezvous and docking of two geostationary satellites

NASA Technical Reports Server (NTRS)

Ortega, Guillermo

1995-01-01

Large assemblings in space require the ability to manage rendezvous and docking operations. In future these techniques will be required for the gradual build up of big telecommunication platforms in the geostationary orbit. The paper discusses the use of fuzzy logic to model and implement a control system for the docking/berthing of two satellites in geostationary orbit. The system mounted in a chaser vehicle determines the actual state of both satellites and generates torques to execute maneuvers to establish the structural latching. The paper describes the proximity operations to collocate the two satellites in the same orbital window, the fuzzy guidance and navigation of the chaser approaching the target and the final Fuzzy berthing. The fuzzy logic system represents a knowledge based controller that realizes the close loop operations autonomously replacing the conventional control algorithms. The goal is to produce smooth control actions in the proximity of the target and during the docking to avoid disturbance torques in the final assembly orbit. The knowledge of the fuzzy controller consists of a data base of rules and the definitions of the fuzzy sets. The knowledge of an experienced spacecraft controller is captured into a set of rules forming the Rules Data Base.

Experiments on neural network architectures for fuzzy logic

NASA Technical Reports Server (NTRS)

Keller, James M.

1991-01-01

The use of fuzzy logic to model and manage uncertainty in a rule-based system places high computational demands on an inference engine. In an earlier paper, the authors introduced a trainable neural network structure for fuzzy logic. These networks can learn and extrapolate complex relationships between possibility distributions for the antecedents and consequents in the rules. Here, the power of these networks is further explored. The insensitivity of the output to noisy input distributions (which are likely if the clauses are generated from real data) is demonstrated as well as the ability of the networks to internalize multiple conjunctive clause and disjunctive clause rules. Since different rules with the same variables can be encoded in a single network, this approach to fuzzy logic inference provides a natural mechanism for rule conflict resolution.

Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic.

PubMed

Hou, Shi-Wang; Feng, Shunxiao; Wang, Hui

2016-01-01

Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating.

Taming Data to Make Decisions: Using a Spatial Fuzzy Logic Decision Support Framework to Inform Conservation and Land Use Planning

NASA Astrophysics Data System (ADS)

Sheehan, T.; Baker, B.; Degagne, R. S.

2015-12-01

With the abundance of data sources, analytical methods, and computer models, land managers are faced with the overwhelming task of making sense of a profusion of data of wildly different types. Luckily, fuzzy logic provides a method to work with different types of data using language-based propositions such as "the landscape is undisturbed," and a simple set of logic constructs. Just as many surveys allow different levels of agreement with a proposition, fuzzy logic allows values reflecting different levels of truth for a proposition. Truth levels fall within a continuum ranging from Fully True to Fully False. Hence a fuzzy logic model produces continuous results. The Environmental Evaluation Modeling System (EEMS) is a platform-independent, tree-based, fuzzy logic modeling framework. An EEMS model provides a transparent definition of an evaluation model and is commonly developed as a collaborative effort among managers, scientists, and GIS experts. Managers specify a set of evaluative propositions used to characterize the landscape. Scientists, working with managers, formulate functions that convert raw data values into truth values for the propositions and produce a logic tree to combine results into a single metric used to guide decisions. Managers, scientists, and GIS experts then work together to implement and iteratively tune the logic model and produce final results. We present examples of two successful EEMS projects that provided managers with map-based results suitable for guiding decisions: sensitivity and climate change exposure in Utah and the Colorado Plateau modeled for the Bureau of Land Management; and terrestrial ecological intactness in the Mojave and Sonoran region of southern California modeled for the Desert Renewable Energy Conservation Plan.

A Simplified Version of the Fuzzy Decision Method and its Comparison with the Paraconsistent Decision Method

NASA Astrophysics Data System (ADS)

de Carvalho, Fábio Romeu; Abe, Jair Minoro

2010-11-01

Two recent non-classical logics have been used to make decision: fuzzy logic and paraconsistent annotated evidential logic Et. In this paper we present a simplified version of the fuzzy decision method and its comparison with the paraconsistent one. Paraconsistent annotated evidential logic Et, introduced by Da Costa, Vago and Subrahmanian (1991), is capable of handling uncertain and contradictory data without becoming trivial. It has been used in many applications such as information technology, robotics, artificial intelligence, production engineering, decision making etc. Intuitively, one Et logic formula is type p(a, b), in which a and b belong to [0, 1] (real interval) and represent respectively the degree of favorable evidence (or degree of belief) and the degree of contrary evidence (or degree of disbelief) found in p. The set of all pairs (a; b), called annotations, when plotted, form the Cartesian Unitary Square (CUS). This set, containing a similar order relation of real number, comprises a network, called lattice of the annotations. Fuzzy logic was introduced by Zadeh (1965). It tries to systematize the knowledge study, searching mainly to study the fuzzy knowledge (you don't know what it means) and distinguish it from the imprecise one (you know what it means, but you don't know its exact value). This logic is similar to paraconsistent annotated one, since it attributes a numeric value (only one, not two values) to each proposition (then we can say that it is an one-valued logic). This number translates the intensity (the degree) with which the preposition is true. Let's X a set and A, a subset of X, identified by the function f(x). For each element x∈X, you have y = f(x)∈[0, 1]. The number y is called degree of pertinence of x in A. Decision making theories based on these logics have shown to be powerful in many aspects regarding more traditional methods, like the one based on Statistics. In this paper we present a first study for a simplified version of decision making theory based on Fuzzy Logic (SVMFD) and a comparison with the Paraconsistent Decision Method (PDM) based on Paraconsistent Annotated Evidential Logic Eτ, already presented and summarized in this paper. An example showing the two methods is presented in the paper, as well as a comparison between them.

Fuzzy logic and neural networks in artificial intelligence and pattern recognition

NASA Astrophysics Data System (ADS)

Sanchez, Elie

1991-10-01

With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

Learning fuzzy logic control system

NASA Technical Reports Server (NTRS)

Lung, Leung Kam

1994-01-01

The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the D.C. motor. Furthermore, the LFLC has better performance in rise time, settling time and steady state error than to the conventional PI controller. This abstract accurately represents the content of the candidate's thesis. I recommend its publication.

Risk analysis with a fuzzy-logic approach of a complex installation

NASA Astrophysics Data System (ADS)

Peikert, Tim; Garbe, Heyno; Potthast, Stefan

2016-09-01

This paper introduces a procedural method based on fuzzy logic to analyze systematic the risk of an electronic system in an intentional electromagnetic environment (IEME). The method analyzes the susceptibility of a complex electronic installation with respect to intentional electromagnetic interference (IEMI). It combines the advantages of well-known techniques as fault tree analysis (FTA), electromagnetic topology (EMT) and Bayesian networks (BN) and extends the techniques with an approach to handle uncertainty. This approach uses fuzzy sets, membership functions and fuzzy logic to handle the uncertainty with probability functions and linguistic terms. The linguistic terms add to the risk analysis the knowledge from experts of the investigated system or environment.

Fuzzy Logic and Education: Teaching the Basics of Fuzzy Logic through an Example (By Way of Cycling)

ERIC Educational Resources Information Center

Sobrino, Alejandro

2013-01-01

Fuzzy logic dates back to 1965 and it is related not only to current areas of knowledge, such as Control Theory and Computer Science, but also to traditional ones, such as Philosophy and Linguistics. Like any logic, fuzzy logic is concerned with argumentation, but unlike other modalities, which focus on the crisp reasoning of Mathematics, it deals…

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

A Fuzzy Description Logic with Automatic Object Membership Measurement

NASA Astrophysics Data System (ADS)

Cai, Yi; Leung, Ho-Fung

In this paper, we propose a fuzzy description logic named f om -DL by combining the classical view in cognitive psychology and fuzzy set theory. A formal mechanism used to determine object memberships automatically in concepts is also proposed, which is lacked in previous work fuzzy description logics. In this mechanism, object membership is based on the defining properties of concept definition and properties in object description. Moreover, while previous works cannot express the qualitative measurements of an object possessing a property, we introduce two kinds of properties named N-property and L-property, which are quantitative measurements and qualitative measurements of an object possessing a property respectively. The subsumption and implication of concepts and properties are also explored in our work. We believe that it is useful to the Semantic Web community for reasoning the fuzzy membership of objects for concepts in fuzzy ontologies.

Fuzzy logic application for modeling man-in-the-loop space shuttle proximity operations. M.S. Thesis - MIT

NASA Technical Reports Server (NTRS)

Brown, Robert B.

1994-01-01

A software pilot model for Space Shuttle proximity operations is developed, utilizing fuzzy logic. The model is designed to emulate a human pilot during the terminal phase of a Space Shuttle approach to the Space Station. The model uses the same sensory information available to a human pilot and is based upon existing piloting rules and techniques determined from analysis of human pilot performance. Such a model is needed to generate numerous rendezvous simulations to various Space Station assembly stages for analysis of current NASA procedures and plume impingement loads on the Space Station. The advantages of a fuzzy logic pilot model are demonstrated by comparing its performance with NASA's man-in-the-loop simulations and with a similar model based upon traditional Boolean logic. The fuzzy model is shown to respond well from a number of initial conditions, with results typical of an average human. In addition, the ability to model different individual piloting techniques and new piloting rules is demonstrated.

A comparison of fuzzy logic and cluster renewal approaches for heat transfer modeling in a 1296 t/h CFB boiler with low level of flue gas recirculation

NASA Astrophysics Data System (ADS)

Błaszczuk, Artur; Krzywański, Jarosław

2017-03-01

The interrelation between fuzzy logic and cluster renewal approaches for heat transfer modeling in a circulating fluidized bed (CFB) has been established based on a local furnace data. The furnace data have been measured in a 1296 t/h CFB boiler with low level of flue gas recirculation. In the present study, the bed temperature and suspension density were treated as experimental variables along the furnace height. The measured bed temperature and suspension density were varied in the range of 1131-1156 K and 1.93-6.32 kg/m3, respectively. Using the heat transfer coefficient for commercial CFB combustor, two empirical heat transfer correlation were developed in terms of important operating parameters including bed temperature and also suspension density. The fuzzy logic results were found to be in good agreement with the corresponding experimental heat transfer data obtained based on cluster renewal approach. The predicted bed-to-wall heat transfer coefficient covered a range of 109-241 W/(m2K) and 111-240 W/(m2K), for fuzzy logic and cluster renewal approach respectively. The divergence in calculated heat flux recovery along the furnace height between fuzzy logic and cluster renewal approach did not exceeded ±2%.

Comparison of conventional rule based flow control with control processes based on fuzzy logic in a combined sewer system.

PubMed

Klepiszewski, K; Schmitt, T G

2002-01-01

While conventional rule based, real time flow control of sewer systems is in common use, control systems based on fuzzy logic have been used only rarely, but successfully. The intention of this study is to compare a conventional rule based control of a combined sewer system with a fuzzy logic control by using hydrodynamic simulation. The objective of both control strategies is to reduce the combined sewer overflow volume by an optimization of the utilized storage capacities of four combined sewer overflow tanks. The control systems affect the outflow of four combined sewer overflow tanks depending on the water levels inside the structures. Both systems use an identical rule base. The developed control systems are tested and optimized for a single storm event which affects heterogeneously hydraulic load conditions and local discharge. Finally the efficiencies of the two different control systems are compared for two more storm events. The results indicate that the conventional rule based control and the fuzzy control similarly reach the objective of the control strategy. In spite of the higher expense to design the fuzzy control system its use provides no advantages in this case.

Robust Fault Detection Using Robust Z1 Estimation and Fuzzy Logic

NASA Technical Reports Server (NTRS)

Curry, Tramone; Collins, Emmanuel G., Jr.; Selekwa, Majura; Guo, Ten-Huei (Technical Monitor)

2001-01-01

This research considers the application of robust Z(sub 1), estimation in conjunction with fuzzy logic to robust fault detection for an aircraft fight control system. It begins with the development of robust Z(sub 1) estimators based on multiplier theory and then develops a fixed threshold approach to fault detection (FD). It then considers the use of fuzzy logic for robust residual evaluation and FD. Due to modeling errors and unmeasurable disturbances, it is difficult to distinguish between the effects of an actual fault and those caused by uncertainty and disturbance. Hence, it is the aim of a robust FD system to be sensitive to faults while remaining insensitive to uncertainty and disturbances. While fixed thresholds only allow a decision on whether a fault has or has not occurred, it is more valuable to have the residual evaluation lead to a conclusion related to the degree of, or probability of, a fault. Fuzzy logic is a viable means of determining the degree of a fault and allows the introduction of human observations that may not be incorporated in the rigorous threshold theory. Hence, fuzzy logic can provide a more reliable and informative fault detection process. Using an aircraft flight control system, the results of FD using robust Z(sub 1) estimation with a fixed threshold are demonstrated. FD that combines robust Z(sub 1) estimation and fuzzy logic is also demonstrated. It is seen that combining the robust estimator with fuzzy logic proves to be advantageous in increasing the sensitivity to smaller faults while remaining insensitive to uncertainty and disturbances.

Dynamic Fuzzy Logic-Based Quality of Interaction within Blended-Learning: The Rare and Contemporary Dance Cases

ERIC Educational Resources Information Center

Dias, Sofia B.; Diniz, José A.; Hadjileontiadis, Leontios J.

2014-01-01

The combination of the process of pedagogical planning within the Blended (b-) learning environment with the users' quality of interaction ("QoI") with the Learning Management System (LMS) is explored here. The required "QoI" (both for professors and students) is estimated by adopting a fuzzy logic-based modeling approach,…

Fuzzy logic control for camera tracking system

NASA Technical Reports Server (NTRS)

Lea, Robert N.; Fritz, R. H.; Giarratano, J.; Jani, Yashvant

1992-01-01

A concept utilizing fuzzy theory has been developed for a camera tracking system to provide support for proximity operations and traffic management around the Space Station Freedom. Fuzzy sets and fuzzy logic based reasoning are used in a control system which utilizes images from a camera and generates required pan and tilt commands to track and maintain a moving target in the camera's field of view. This control system can be implemented on a fuzzy chip to provide an intelligent sensor for autonomous operations. Capabilities of the control system can be expanded to include approach, handover to other sensors, caution and warning messages.

Fuzzy Versions of Epistemic and Deontic Logic

NASA Technical Reports Server (NTRS)

Gounder, Ramasamy S.; Esterline, Albert C.

1998-01-01

Epistemic and deontic logics are modal logics, respectively, of knowledge and of the normative concepts of obligation, permission, and prohibition. Epistemic logic is useful in formalizing systems of communicating processes and knowledge and belief in AI (Artificial Intelligence). Deontic logic is useful in computer science wherever we must distinguish between actual and ideal behavior, as in fault tolerance and database integrity constraints. We here discuss fuzzy versions of these logics. In the crisp versions, various axioms correspond to various properties of the structures used in defining the semantics of the logics. Thus, any axiomatic theory will be characterized not only by its axioms but also by the set of properties holding of the corresponding semantic structures. Fuzzy logic does not proceed with axiomatic systems, but fuzzy versions of the semantic properties exist and can be shown to correspond to some of the axioms for the crisp systems in special ways that support dependency networks among assertions in a modal domain. This in turn allows one to implement truth maintenance systems. For the technical development of epistemic logic, and for that of deontic logic. To our knowledge, we are the first to address fuzzy epistemic and fuzzy deontic logic explicitly and to consider the different systems and semantic properties available. We give the syntax and semantics of epistemic logic and discuss the correspondence between axioms of epistemic logic and properties of semantic structures. The same topics are covered for deontic logic. Fuzzy epistemic and fuzzy deontic logic discusses the relationship between axioms and semantic properties for these logics. Our results can be exploited in truth maintenance systems.

Development of Real Time Implementation of 5/5 Rule based Fuzzy Logic Controller Shunt Active Power Filter for Power Quality Improvement

NASA Astrophysics Data System (ADS)

Puhan, Pratap Sekhar; Ray, Pravat Kumar; Panda, Gayadhar

2016-12-01

This paper presents the effectiveness of 5/5 Fuzzy rule implementation in Fuzzy Logic Controller conjunction with indirect control technique to enhance the power quality in single phase system, An indirect current controller in conjunction with Fuzzy Logic Controller is applied to the proposed shunt active power filter to estimate the peak reference current and capacitor voltage. Current Controller based pulse width modulation (CCPWM) is used to generate the switching signals of voltage source inverter. Various simulation results are presented to verify the good behaviour of the Shunt active Power Filter (SAPF) with proposed two levels Hysteresis Current Controller (HCC). For verification of Shunt Active Power Filter in real time, the proposed control algorithm has been implemented in laboratory developed setup in dSPACE platform.

An Energy Saving Green Plug Device for Nonlinear Loads

NASA Astrophysics Data System (ADS)

Bloul, Albe; Sharaf, Adel; El-Hawary, Mohamed

2018-03-01

The paper presents a low cost a FACTS Based flexible fuzzy logic based modulated/switched tuned arm filter and Green Plug compensation (SFC-GP) scheme for single-phase nonlinear loads ensuring both voltage stabilization and efficient energy utilization. The new Green Plug-Switched filter compensator SFC modulated LC-Filter PWM Switched Capacitive Compensation Devices is controlled using a fuzzy logic regulator to enhance power quality, improve power factor at the source and reduce switching transients and inrush current conditions as well harmonic contents in source current. The FACTS based SFC-GP Device is a member of family of Green Plug/Filters/Compensation Schemes used for efficient energy utilization, power quality enhancement and voltage/inrush current/soft starting control using a dynamic error driven fuzzy logic controller (FLC). The device with fuzzy logic controller is validated using the Matlab / Simulink Software Environment for enhanced power quality (PQ), improved power factor and reduced inrush currents. This is achieved using modulated PWM Switching of the Filter-Capacitive compensation scheme to cope with dynamic type nonlinear and inrush cyclical loads..

Family of new operations equivalency of neuro-fuzzy logic: optoelectronic realization and applications

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Yatskovsky, Victor I.; Ogorodnik, K. V.; Lischenko, Sergey

2002-07-01

The perspective of neural networks equivalental models (EM) base on vector-matrix procedure with basic operations of continuous and neuro-fuzzy logic (equivalence, absolute difference) are shown. Capacity on base EMs exceeded the amount of neurons in 2.5 times. This is larger than others neural networks paradigms. Amount neurons of this neural networks on base EMs may be 10 - 20 thousands. The base operations in EMs are normalized equivalency operations. The family of new operations equivalency and non-equivalency of neuro-fuzzy logic's, which we have elaborated on the based of such generalized operations of fuzzy-logic's as fuzzy negation, t-norm and s-norm are shown. Generalized rules of construction of new functions (operations) equivalency which uses relations of t-norm and s-norm to fuzzy negation are proposed. Among these elements the following should be underlined: (1) the element which fulfills the operation of limited difference; (2) the element which algebraic product (intensifier with controlled coefficient of transmission or multiplier of analog signals); (3) the element which fulfills a sample summarizing (uniting) of signals (including the one during normalizing). Synthesized structures which realize on the basic of these elements the whole spectrum of required operations: t-norm, s-norm and new operations equivalency are shown. These realization on the basic of new multifunctional optoelectronical BISPIN- devices (MOEBD) represent the circuit with constant and pulse optical input signals. They are modeling the operation of limited difference. These circuits realize frequency- dynamic neuron models and neural networks. Experimental results of these MOEBD and equivalency circuits, which fulfill the limited difference operation are discussed. For effective realization of neural networks on the basic of EMs as it is shown in report, picture elements are required as main nodes to implement element operations equivalence ('non-equivalence') of neuro-fuzzy logic's.

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.

Fluorescence intensity positivity classification of Hep-2 cells images using fuzzy logic

NASA Astrophysics Data System (ADS)

Sazali, Dayang Farzana Abang; Janier, Josefina Barnachea; May, Zazilah Bt.

2014-10-01

Indirect Immunofluorescence (IIF) is a good standard used for antinuclear autoantibody (ANA) test using Hep-2 cells to determine specific diseases. Different classifier algorithm methods have been proposed in previous works however, there still no valid set as a standard to classify the fluorescence intensity. This paper presents the use of fuzzy logic to classify the fluorescence intensity and to determine the positivity of the Hep-2 cell serum samples. The fuzzy algorithm involves the image pre-processing by filtering the noises and smoothen the image, converting the red, green and blue (RGB) color space of images to luminosity layer, chromaticity layer "a" and "b" (LAB) color space where the mean value of the lightness and chromaticity layer "a" was extracted and classified by using fuzzy logic algorithm based on the standard score ranges of antinuclear autoantibody (ANA) fluorescence intensity. Using 100 data sets of positive and intermediate fluorescence intensity for testing the performance measurements, the fuzzy logic obtained an accuracy of intermediate and positive class as 85% and 87% respectively.

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.

Molecular processors: from qubits to fuzzy logic.

PubMed

Gentili, Pier Luigi

2011-03-14

Single molecules or their assemblies are information processing devices. Herein it is demonstrated how it is possible to process different types of logic through molecules. As long as decoherent effects are maintained far away from a pure quantum mechanical system, quantum logic can be processed. If the collapse of superimposed or entangled wavefunctions is unavoidable, molecules can still be used to process either crisp (binary or multi-valued) or fuzzy logic. The way for implementing fuzzy inference engines is declared and it is supported by the examples of molecular fuzzy logic systems devised so far. Fuzzy logic is drawing attention in the field of artificial intelligence, because it models human reasoning quite well. This ability may be due to some structural analogies between a fuzzy logic system and the human nervous system. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

Fuzzy logic applications to expert systems and control

NASA Technical Reports Server (NTRS)

Lea, Robert N.; Jani, Yashvant

1991-01-01

A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings.

Detection of Failure in Asynchronous Motor Using Soft Computing Method

NASA Astrophysics Data System (ADS)

Vinoth Kumar, K.; Sony, Kevin; Achenkunju John, Alan; Kuriakose, Anto; John, Ano P.

2018-04-01

This paper investigates the stator short winding failure of asynchronous motor also their effects on motor current spectrums. A fuzzy logic approach i.e., model based technique possibly will help to detect the asynchronous motor failure. Actually, fuzzy logic similar to humanoid intelligent methods besides expected linguistic empowering inferences through vague statistics. The dynamic model is technologically advanced for asynchronous motor by means of fuzzy logic classifier towards investigate the stator inter turn failure in addition open phase failure. A hardware implementation was carried out with LabVIEW for the online-monitoring of faults.

Interference Path Loss Prediction in A319/320 Airplanes Using Modulated Fuzzy Logic and Neural Networks

NASA Technical Reports Server (NTRS)

Jafri, Madiha J.; Ely, Jay J.; Vahala, Linda L.

2007-01-01

In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.

A fuzzy logic approach to modeling a vehicle crash test

NASA Astrophysics Data System (ADS)

Pawlus, Witold; Karimi, Hamid Reza; Robbersmyr, Kjell G.

2013-03-01

This paper presents an application of fuzzy approach to vehicle crash modeling. A typical vehicle to pole collision is described and kinematics of a car involved in this type of crash event is thoroughly characterized. The basics of fuzzy set theory and modeling principles based on fuzzy logic approach are presented. In particular, exceptional attention is paid to explain the methodology of creation of a fuzzy model of a vehicle collision. Furthermore, the simulation results are presented and compared to the original vehicle's kinematics. It is concluded which factors have influence on the accuracy of the fuzzy model's output and how they can be adjusted to improve the model's fidelity.

FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM

EPA Science Inventory

The paper describes a variable-speed wind generation system where fuzzy logic principles are used to optimize efficiency and enhance performance control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which either pump...

FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM

EPA Science Inventory

The report gives results of a demonstration of the successful application of fuzzy logic to enhance the performance and control of a variable-speed wind generation system. A squirrel cage induction generator feeds the power to either a double-sided pulse-width modulation converte...

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.

Fuzzy Logic Approaches to Multi-Objective Decision-Making in Aerospace Applications

NASA Technical Reports Server (NTRS)

Hardy, Terry L.

1994-01-01

Fuzzy logic allows for the quantitative representation of multi-objective decision-making problems which have vague or fuzzy objectives and parameters. As such, fuzzy logic approaches are well-suited to situations where alternatives must be assessed by using criteria that are subjective and of unequal importance. This paper presents an overview of fuzzy logic and provides sample applications from the aerospace industry. Applications include an evaluation of vendor proposals, an analysis of future space vehicle options, and the selection of a future space propulsion system. On the basis of the results provided in this study, fuzzy logic provides a unique perspective on the decision-making process, allowing the evaluator to assess the degree to which each option meets the evaluation criteria. Future decision-making should take full advantage of fuzzy logic methods to complement existing approaches in the selection of alternatives.

Integration of Genetic Algorithms and Fuzzy Logic for Urban Growth Modeling

NASA Astrophysics Data System (ADS)

Foroutan, E.; Delavar, M. R.; Araabi, B. N.

2012-07-01

Urban growth phenomenon as a spatio-temporal continuous process is subject to spatial uncertainty. This inherent uncertainty cannot be fully addressed by the conventional methods based on the Boolean algebra. Fuzzy logic can be employed to overcome this limitation. Fuzzy logic preserves the continuity of dynamic urban growth spatially by choosing fuzzy membership functions, fuzzy rules and the fuzzification-defuzzification process. Fuzzy membership functions and fuzzy rule sets as the heart of fuzzy logic are rather subjective and dependent on the expert. However, due to lack of a definite method for determining the membership function parameters, certain optimization is needed to tune the parameters and improve the performance of the model. This paper integrates genetic algorithms and fuzzy logic as a genetic fuzzy system (GFS) for modeling dynamic urban growth. The proposed approach is applied for modeling urban growth in Tehran Metropolitan Area in Iran. Historical land use/cover data of Tehran Metropolitan Area extracted from the 1988 and 1999 Landsat ETM+ images are employed in order to simulate the urban growth. The extracted land use classes of the year 1988 include urban areas, street, vegetation areas, slope and elevation used as urban growth physical driving forces. Relative Operating Characteristic (ROC) curve as an fitness function has been used to evaluate the performance of the GFS algorithm. The optimum membership function parameter is applied for generating a suitability map for the urban growth. Comparing the suitability map and real land use map of 1999 gives the threshold value for the best suitability map which can simulate the land use map of 1999. The simulation outcomes in terms of kappa of 89.13% and overall map accuracy of 95.58% demonstrated the efficiency and reliability of the proposed model.

Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control

NASA Astrophysics Data System (ADS)

Othman, Ahmed M.; El-arini, Mahdi M. M.; Ghitas, Ahmed; Fathy, Ahmed

2012-12-01

In the recent years, the solar energy becomes one of the most important alternative sources of electric energy, so it is important to improve the efficiency and reliability of the photovoltaic (PV) systems. Maximum power point tracking (MPPT) plays an important role in photovoltaic power systems because it maximize the power output from a PV system for a given set of conditions, and therefore maximize their array efficiency. This paper presents a maximum power point tracker (MPPT) using Fuzzy Logic theory for a PV system. The work is focused on the well known Perturb and Observe (P&O) algorithm and is compared to a designed fuzzy logic controller (FLC). The simulation work dealing with MPPT controller; a DC/DC Ćuk converter feeding a load is achieved. The results showed that the proposed Fuzzy Logic MPPT in the PV system is valid.

Fuzzy logic in autonomous orbital operations

NASA Technical Reports Server (NTRS)

Lea, Robert N.; Jani, Yashvant

1991-01-01

Fuzzy logic can be used advantageously in autonomous orbital operations that require the capability of handling imprecise measurements from sensors. Several applications are underway to investigate fuzzy logic approaches and develop guidance and control algorithms for autonomous orbital operations. Translational as well as rotational control of a spacecraft have been demonstrated using space shuttle simulations. An approach to a camera tracking system has been developed to support proximity operations and traffic management around the Space Station Freedom. Pattern recognition and object identification algorithms currently under development will become part of this camera system at an appropriate level in the future. A concept to control environment and life support systems for large Lunar based crew quarters is also under development. Investigations in the area of reinforcement learning, utilizing neural networks, combined with a fuzzy logic controller, are planned as a joint project with the Ames Research Center.

Risk Assessment in Underground Coalmines Using Fuzzy Logic in the Presence of Uncertainty

NASA Astrophysics Data System (ADS)

Tripathy, Debi Prasad; Ala, Charan Kumar

2018-04-01

Fatal accidents are occurring every year as regular events in Indian coal mining industry. To increase the safety conditions, it has become a prerequisite to performing a risk assessment of various operations in mines. However, due to uncertain accident data, it is hard to conduct a risk assessment in mines. The object of this study is to present a method to assess safety risks in underground coalmines. The assessment of safety risks is based on the fuzzy reasoning approach. Mamdani fuzzy logic model is developed in the fuzzy logic toolbox of MATLAB. A case study is used to demonstrate the applicability of the developed model. The summary of risk evaluation in case study mine indicated that mine fire has the highest risk level among all the hazard factors. This study could help the mine management to prepare safety measures based on the risk rankings obtained.

Fuzzy feature selection based on interval type-2 fuzzy sets

NASA Astrophysics Data System (ADS)

Cherif, Sahar; Baklouti, Nesrine; Alimi, Adel; Snasel, Vaclav

2017-03-01

When dealing with real world data; noise, complexity, dimensionality, uncertainty and irrelevance can lead to low performance and insignificant judgment. Fuzzy logic is a powerful tool for controlling conflicting attributes which can have similar effects and close meanings. In this paper, an interval type-2 fuzzy feature selection is presented as a new approach for removing irrelevant features and reducing complexity. We demonstrate how can Feature Selection be joined with Interval Type-2 Fuzzy Logic for keeping significant features and hence reducing time complexity. The proposed method is compared with some other approaches. The results show that the number of attributes is proportionally small.

Fuzzy self-learning control for magnetic servo system

NASA Technical Reports Server (NTRS)

Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

1994-01-01

It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

Segmentation method of eye region based on fuzzy logic system for classifying open and closed eyes

NASA Astrophysics Data System (ADS)

Kim, Ki Wan; Lee, Won Oh; Kim, Yeong Gon; Hong, Hyung Gil; Lee, Eui Chul; Park, Kang Ryoung

2015-03-01

The classification of eye openness and closure has been researched in various fields, e.g., driver drowsiness detection, physiological status analysis, and eye fatigue measurement. For a classification with high accuracy, accurate segmentation of the eye region is required. Most previous research used the segmentation method by image binarization on the basis that the eyeball is darker than skin, but the performance of this approach is frequently affected by thick eyelashes or shadows around the eye. Thus, we propose a fuzzy-based method for classifying eye openness and closure. First, the proposed method uses I and K color information from the HSI and CMYK color spaces, respectively, for eye segmentation. Second, the eye region is binarized using the fuzzy logic system based on I and K inputs, which is less affected by eyelashes and shadows around the eye. The combined image of I and K pixels is obtained through the fuzzy logic system. Third, in order to reflect the effect by all the inference values on calculating the output score of the fuzzy system, we use the revised weighted average method, where all the rectangular regions by all the inference values are considered for calculating the output score. Fourth, the classification of eye openness or closure is successfully made by the proposed fuzzy-based method with eye images of low resolution which are captured in the environment of people watching TV at a distance. By using the fuzzy logic system, our method does not require the additional procedure of training irrespective of the chosen database. Experimental results with two databases of eye images show that our method is superior to previous approaches.

Fuzzy logic control of stand-alone photovoltaic system with battery storage

NASA Astrophysics Data System (ADS)

Lalouni, S.; Rekioua, D.; Rekioua, T.; Matagne, E.

Photovoltaic energy has nowadays an increased importance in electrical power applications, since it is considered as an essentially inexhaustible and broadly available energy resource. However, the output power provided via the photovoltaic conversion process depends on solar irradiation and temperature. Therefore, to maximize the efficiency of the photovoltaic energy system, it is necessary to track the maximum power point of the PV array. The present paper proposes a maximum power point tracker (MPPT) method, based on fuzzy logic controller (FLC), applied to a stand-alone photovoltaic system. It uses a sampling measure of the PV array power and voltage then determines an optimal increment required to have the optimal operating voltage which permits maximum power tracking. This method carries high accuracy around the optimum point when compared to the conventional one. The stand-alone photovoltaic system used in this paper includes two bi-directional DC/DC converters and a lead-acid battery bank to overcome the scare periods. One converter works as an MPP tracker, while the other regulates the batteries state of charge and compensates the power deficit to provide a continuous delivery of energy to the load. The Obtained simulation results show the effectiveness of the proposed fuzzy logic controller.

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.

Challenges And Results of the Applications of Fuzzy Logic in the Classification of Rich Galaxy Clusters

NASA Astrophysics Data System (ADS)

Girola Schneider, R.

2017-07-01

The fuzzy logic is a branch of the artificial intelligence founded on the concept that everything is a matter of degree. It intends to create mathematical approximations on the resolution of certain types of problems. In addition, it aims to produce exact results obtained from imprecise data, for which it is particularly useful for electronic and computer applications. This enables it to handle vague or unspecific information when certain parts of a system are unknown or ambiguous and, therefore, they cannot be measured in a reliable manner. Also, when the variation of a variable can produce an alteration on the others The main focus of this paper is to prove the importance of these techniques formulated from a theoretical analysis on its application on ambiguous situations in the field of the rich clusters of galaxies. The purpose is to show its applicability in the several classification systems proposed for the rich clusters, which are based on criteria such as the level of richness of the cluster, the distribution of the brightest galaxies, whether there are signs of type-cD galaxies or not or the existence of sub-clusters. Fuzzy logic enables the researcher to work with "imprecise" information implementing fuzzy sets and combining rules to define actions. The control systems based on fuzzy logic join input variables that are defined in terms of fuzzy sets through rule groups that produce one or several output values of the system under study. From this context, the application of the fuzzy logic's techniques approximates the solution of the mathematical models in abstractions about the rich galaxy cluster classification of physical properties in order to solve the obscurities that must be confronted by an investigation group in order to make a decision.

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.

Fuzzy-Arden-Syntax-based, Vendor-agnostic, Scalable Clinical Decision Support and Monitoring Platform.

PubMed

Adlassnig, Klaus-Peter; Fehre, Karsten; Rappelsberger, Andrea

2015-01-01

This study's objective is to develop and use a scalable genuine technology platform for clinical decision support based on Arden Syntax, which was extended by fuzzy set theory and fuzzy logic. Arden Syntax is a widely recognized formal language for representing clinical and scientific knowledge in an executable format, and is maintained by Health Level Seven (HL7) International and approved by the American National Standards Institute (ANSI). Fuzzy set theory and logic permit the representation of knowledge and automated reasoning under linguistic and propositional uncertainty. These forms of uncertainty are a common feature of patients' medical data, the body of medical knowledge, and deductive clinical reasoning.

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.

Control of motion stability of the line tracer robot using fuzzy logic and kalman filter

NASA Astrophysics Data System (ADS)

Novelan, M. S.; Tulus; Zamzami, E. M.

2018-03-01

Setting of motion and balance line tracer robot two wheels is actually a combination of a two-wheeled robot balance concept and the concept of line follower robot. The main objective of this research is to maintain the robot in an upright and can move to follow the line of the Wizard while maintaining balance. In this study the motion balance system on line tracer robot by considering the presence of a noise, so that it takes the estimator is used to mengestimasi the line tracer robot motion. The estimation is done by the method of Kalman Filter and the combination of Fuzzy logic-Fuzzy Kalman Filter called Kalman Filter, as well as optimal smooting. Based on the results of the study, the value of the output of the fuzzy results obtained from the sensor input value has been filtered before entering the calculation of the fuzzy. The results of the output of the fuzzy logic hasn’t been able to control dc motors are well balanced at the moment to be able to run. The results of the fuzzy logic by using membership function of triangular membership function or yet can control with good dc motor movement in order to be balanced

A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater.

PubMed

Turkdogan-Aydinol, F Ilter; Yetilmezsoy, Kaan

2010-10-15

A MIMO (multiple inputs and multiple outputs) fuzzy-logic-based model was developed to predict biogas and methane production rates in a pilot-scale 90-L mesophilic up-flow anaerobic sludge blanket (UASB) reactor treating molasses wastewater. Five input variables such as volumetric organic loading rate (OLR), volumetric total chemical oxygen demand (TCOD) removal rate (R(V)), influent alkalinity, influent pH and effluent pH were fuzzified by the use of an artificial intelligence-based approach. Trapezoidal membership functions with eight levels were conducted for the fuzzy subsets, and a Mamdani-type fuzzy inference system was used to implement a total of 134 rules in the IF-THEN format. The product (prod) and the centre of gravity (COG, centroid) methods were employed as the inference operator and defuzzification methods, respectively. Fuzzy-logic predicted results were compared with the outputs of two exponential non-linear regression models derived in this study. The UASB reactor showed a remarkable performance on the treatment of molasses wastewater, with an average TCOD removal efficiency of 93 (+/-3)% and an average volumetric TCOD removal rate of 6.87 (+/-3.93) kg TCOD(removed)/m(3)-day, respectively. Findings of this study clearly indicated that, compared to non-linear regression models, the proposed MIMO fuzzy-logic-based model produced smaller deviations and exhibited a superior predictive performance on forecasting of both biogas and methane production rates with satisfactory determination coefficients over 0.98. 2010 Elsevier B.V. All rights reserved.

Supervised Learning in CINets

DTIC Science & Technology

2011-07-01

supervised learning process is compared to that of Artificial Neural Network ( ANNs ), fuzzy logic rule set, and Bayesian network approaches...of both fuzzy logic systems and Artificial Neural Networks ( ANNs ). Like fuzzy logic systems, the CINet technique allows the use of human- intuitive...fuzzy rule systems [3] CINets also maintain features common to both fuzzy systems and ANNs . The technique can be be shown to possess the property

Automated cloud classification with a fuzzy logic expert system

NASA Technical Reports Server (NTRS)

Tovinkere, Vasanth; Baum, Bryan A.

1993-01-01

An unresolved problem in current cloud retrieval algorithms concerns the analysis of scenes containing overlapping cloud layers. Cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget. Most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. One promising method uses fuzzy logic to determine whether mixed cloud and/or surface types exist within a group of pixels, such as cirrus, land, and water, or cirrus and stratus. When two or more class types are present, fuzzy logic uses membership values to assign the group of pixels partially to the different class types. The strength of fuzzy logic lies in its ability to work with patterns that may include more than one class, facilitating greater information extraction from satellite radiometric data. The development of the fuzzy logic rule-based expert system involves training the fuzzy classifier with spectral and textural features calculated from accurately labeled 32x32 regions of Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data. The spectral data consists of AVHRR channels 1 (0.55-0.68 mu m), 2 (0.725-1.1 mu m), 3 (3.55-3.93 mu m), 4 (10.5-11.5 mu m), and 5 (11.5-12.5 mu m), which include visible, near-infrared, and infrared window regions. The textural features are based on the gray level difference vector (GLDV) method. A sophisticated new interactive visual image Classification System (IVICS) is used to label samples chosen from scenes collected during the FIRE IFO II. The training samples are chosen from predefined classes, chosen to be ocean, land, unbroken stratiform, broken stratiform, and cirrus. The November 28, 1991 NOAA overpasses contain complex multilevel cloud situations ideal for training and validating the fuzzy logic expert system.

Figure Control of Lightweight Optical Structures

NASA Technical Reports Server (NTRS)

Main, John A.; Song, Haiping

2005-01-01

The goal of this paper is to demonstrate the use of fuzzy logic controllers in modifying the figure of a piezoceramic bimorph mirror. Non-contact electron actuation technology is used to actively control a bimorph mirror comprised two PZT-5H wafers by varying the electron flux and electron voltages. Due to electron blooming generated by the electron flux, it is difficult to develop an accurate control model for the bimorph mirror through theoretical analysis alone. The non-contact shape control system with electron flux blooming can be approximately described with a heuristic model based on experimental data. Two fuzzy logic feedback controllers are developed to control the shape of the bimorph mirror according to heuristic fuzzy inference rules generated from previous experimental results. Validation of the proposed fuzzy logic controllers is also discussed.

Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model

PubMed Central

Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun

2014-01-01

Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586

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

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.

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.

Multi-objective decision-making under uncertainty: Fuzzy logic methods

NASA Technical Reports Server (NTRS)

Hardy, Terry L.

1995-01-01

Fuzzy logic allows for quantitative representation of vague or fuzzy objectives, and therefore is well-suited for multi-objective decision-making. This paper presents methods employing fuzzy logic concepts to assist in the decision-making process. In addition, this paper describes software developed at NASA Lewis Research Center for assisting in the decision-making process. Two diverse examples are used to illustrate the use of fuzzy logic in choosing an alternative among many options and objectives. One example is the selection of a lunar lander ascent propulsion system, and the other example is the selection of an aeration system for improving the water quality of the Cuyahoga River in Cleveland, Ohio. The fuzzy logic techniques provided here are powerful tools which complement existing approaches, and therefore should be considered in future decision-making activities.

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.

Fuzzy Logic-based expert system for evaluating cake quality of freeze-dried formulations.

PubMed

Trnka, Hjalte; Wu, Jian X; Van De Weert, Marco; Grohganz, Holger; Rantanen, Jukka

2013-12-01

Freeze-drying of peptide and protein-based pharmaceuticals is an increasingly important field of research. The diverse nature of these compounds, limited understanding of excipient functionality, and difficult-to-analyze quality attributes together with the increasing importance of the biosimilarity concept complicate the development phase of safe and cost-effective drug products. To streamline the development phase and to make high-throughput formulation screening possible, efficient solutions for analyzing critical quality attributes such as cake quality with minimal material consumption are needed. The aim of this study was to develop a fuzzy logic system based on image analysis (IA) for analyzing cake quality. Freeze-dried samples with different visual quality attributes were prepared in well plates. Imaging solutions together with image analytical routines were developed for extracting critical visual features such as the degree of cake collapse, glassiness, and color uniformity. On the basis of the IA outputs, a fuzzy logic system for analysis of these freeze-dried cakes was constructed. After this development phase, the system was tested with a new screening well plate. The developed fuzzy logic-based system was found to give comparable quality scores with visual evaluation, making high-throughput classification of cake quality possible. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association.

A Fuzzy Logic Prompting Mechanism Based on Pattern Recognition and Accumulated Activity Effective Index Using a Smartphone Embedded Sensor.

PubMed

Liu, Chung-Tse; Chan, Chia-Tai

2016-08-19

Sufficient physical activity can reduce many adverse conditions and contribute to a healthy life. Nevertheless, inactivity is prevalent on an international scale. Improving physical activity is an essential concern for public health. Reminders that help people change their health behaviors are widely applied in health care services. However, timed-based reminders deliver periodic prompts suffer from flexibility and dependency issues which may decrease prompt effectiveness. We propose a fuzzy logic prompting mechanism, Accumulated Activity Effective Index Reminder (AAEIReminder), based on pattern recognition and activity effective analysis to manage physical activity. AAEIReminder recognizes activity levels using a smartphone-embedded sensor for pattern recognition and analyzing the amount of physical activity in activity effective analysis. AAEIReminder can infer activity situations such as the amount of physical activity and days spent exercising through fuzzy logic, and decides whether a prompt should be delivered to a user. This prompting system was implemented in smartphones and was used in a short-term real-world trial by seventeenth participants for validation. The results demonstrated that the AAEIReminder is feasible. The fuzzy logic prompting mechanism can deliver prompts automatically based on pattern recognition and activity effective analysis. AAEIReminder provides flexibility which may increase the prompts' efficiency.

A Simple and Effective Remedial Learning System with a Fuzzy Expert System

ERIC Educational Resources Information Center

Lin, C.-C.; Guo, K.-H.; Lin, Y.-C.

2016-01-01

This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate…

A reinforcement learning-based architecture for fuzzy logic control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1992-01-01

This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

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 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

Towards Resilient Critical Infrastructures: Application of Type-2 Fuzzy Logic in Embedded Network Security Cyber Sensor

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

Ondrej Linda; Todd Vollmer; Jim Alves-Foss

2011-08-01

Resiliency and cyber security of modern critical infrastructures is becoming increasingly important with the growing number of threats in the cyber-environment. This paper proposes an extension to a previously developed fuzzy logic based anomaly detection network security cyber sensor via incorporating Type-2 Fuzzy Logic (T2 FL). In general, fuzzy logic provides a framework for system modeling in linguistic form capable of coping with imprecise and vague meanings of words. T2 FL is an extension of Type-1 FL which proved to be successful in modeling and minimizing the effects of various kinds of dynamic uncertainties. In this paper, T2 FL providesmore » a basis for robust anomaly detection and cyber security state awareness. In addition, the proposed algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental cyber-security test-bed.« less

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 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.

A Sliding Mode Controller Using Nonlinear Sliding Surface Improved With Fuzzy Logic: Application to the Coupled Tanks System

NASA Astrophysics Data System (ADS)

Boubakir, A.; Boudjema, F.; Boubakir, C.

2008-06-01

This paper proposes an approach of hybrid control that is based on the concept of combining fuzzy logic and the methodology of sliding mode control (SMC). In the present works, a first-order nonlinear sliding surface is presented, on which the developed control law is based. Mathematical proof for the stability and convergence of the system is presented. In order to reduce the chattering in sliding mode control, a fixed boundary layer around the switch surface is used. Within the boundary layer, since the fuzzy logic control is applied, the chattering phenomenon, which is inherent in a sliding mode control, is avoided by smoothing the switch signal. Outside the boundary, the sliding mode control is applied to driving the system states into the boundary layer. Experimental studies carried out on a coupled Tanks system indicate that the proposed fuzzy sliding mode control (FSMC) is a good candidate for control applications.

Adaptive fuzzy logic controller with direct action type structures for InnoSAT attitude control system

NASA Astrophysics Data System (ADS)

Bakri, F. A.; Mashor, M. Y.; Sharun, S. M.; Bibi Sarpinah, S. N.; Abu Bakar, Z.

2016-10-01

This study proposes an adaptive fuzzy controller for attitude control system (ACS) of Innovative Satellite (InnoSAT) based on direct action type structure. In order to study new methods used in satellite attitude control, this paper presents three structures of controllers: Fuzzy PI, Fuzzy PD and conventional Fuzzy PID. The objective of this work is to compare the time response and tracking performance among the three different structures of controllers. The parameters of controller were tuned on-line by adjustment mechanism, which was an approach similar to a PID error that could minimize errors between actual and model reference output. This paper also presents a Model References Adaptive Control (MRAC) as a control scheme to control time varying systems where the performance specifications were given in terms of the reference model. All the controllers were tested using InnoSAT system under some operating conditions such as disturbance, varying gain, measurement noise and time delay. In conclusion, among all considered DA-type structures, AFPID controller was observed as the best structure since it outperformed other controllers in most conditions.

A fuzzy logic controller for an autonomous mobile robot

NASA Technical Reports Server (NTRS)

Yen, John; Pfluger, Nathan

1993-01-01

The ability of a mobile robot system to plan and move intelligently in a dynamic system is needed if robots are to be useful in areas other than controlled environments. An example of a use for this system is to control an autonomous mobile robot in a space station, or other isolated area where it is hard or impossible for human life to exist for long periods of time (e.g., Mars). The system would allow the robot to be programmed to carry out the duties normally accomplished by a human being. Some of the duties that could be accomplished include operating instruments, transporting objects, and maintenance of the environment. The main focus of our early work has been on developing a fuzzy controller that takes a path and adapts it to a given environment. The robot only uses information gathered from the sensors, but retains the ability to avoid dynamically placed obstacles near and along the path. Our fuzzy logic controller is based on the following algorithm: (1) determine the desired direction of travel; (2) determine the allowed direction of travel; and (3) combine the desired and allowed directions in order to determine a direciton that is both desired and allowed. The desired direction of travel is determined by projecting ahead to a point along the path that is closer to the goal. This gives a local direction of travel for the robot and helps to avoid obstacles.

Fuzzy Logic based Handoff Latency Reduction Mechanism in Layer 2 of Heterogeneous Mobile IPv6 Networks

NASA Astrophysics Data System (ADS)

Anwar, Farhat; Masud, Mosharrof H.; Latif, Suhaimi A.

2013-12-01

Mobile IPv6 (MIPv6) is one of the pioneer standards that support mobility in IPv6 environment. It has been designed to support different types of technologies for providing seamless communications in next generation network. However, MIPv6 and subsequent standards have some limitations due to its handoff latency. In this paper, a fuzzy logic based mechanism is proposed to reduce the handoff latency of MIPv6 for Layer 2 (L2) by scanning the Access Points (APs) while the Mobile Node (MN) is moving among different APs. Handoff latency occurs when the MN switches from one AP to another in L2. Heterogeneous network is considered in this research in order to reduce the delays in L2. Received Signal Strength Indicator (RSSI) and velocity of the MN are considered as the input of fuzzy logic technique. This technique helps the MN to measure optimum signal quality from APs for the speedy mobile node based on fuzzy logic input rules and makes a list of interfaces. A suitable interface from the list of available interfaces can be selected like WiFi, WiMAX or GSM. Simulation results show 55% handoff latency reduction and 50% packet loss improvement in L2 compared to standard to MIPv6.

Investigating the role of Fuzzy as confirmatory tool for service quality assessment (Case study: Comparison of Fuzzy SERVQUAL and SERVQUAL in hotel service evaluation)

NASA Astrophysics Data System (ADS)

Wahyudi, R. D.

2017-11-01

The problem was because of some indicators qualitatively assessed had been discussed in engineering field. Whereas, qualitative assessment was presently used in certain occasion including in engineering field, for instance, the assessment of service satisfaction. Probably, understanding of satisfaction definition caused bias if customers had their own definition of satisfactory level of service. Therefore, the use of fuzzy logic in SERVQUAL as service satisfaction measurement tool would probably be useful. This paper aimed to investigate the role of fuzzy in SERVQUAL by comparing result measurement of SERVQUAL and fuzzy SERVQUAL for study case of hotel service evaluation. Based on data processing, initial result showed that there was no significant different between them. Thus, either implementation of fuzzy SERVQUAL in different case or study about the role of fuzzy logic in SERVQUAL would be interesting further discussed topic.

A few categories of electromagnetic field problems treated through Fuzzy Logic

NASA Astrophysics Data System (ADS)

Lolea, M. S.; Dzitac, S.

2018-01-01

The paper deals with the problems of fuzzy logic applied in the field of electromagnetism. In the first part, there are presented some theoretical aspects regarding the characteristics and the application of the fuzzy logic in the general case. Are presented then, some categories of electromagnetic field problems treated by fuzzy logic. The accent is on the effects of exposure to the electromagnetic field on the human body. For this approach is dedicated a paragraph at the end of the paper. There is an application on how to treat by fuzzy logic the effects of electric field exposure. For this purpose, the fuzzy toolbox existing in the Matlab software and the results of some electric field strength measurements into a power substation are used. The results of the study and its conclusions are analyzed and exposed at the end of the paper.

Doubly fed induction generator wind turbines with fuzzy controller: a survey.

PubMed

Sathiyanarayanan, J S; Kumar, A Senthil

2014-01-01

Wind energy is one of the extraordinary sources of renewable energy due to its clean character and free availability. With the increasing wind power penetration, the wind farms are directly influencing the power systems. The majority of wind farms are using variable speed wind turbines equipped with doubly fed induction generators (DFIG) due to their advantages over other wind turbine generators (WTGs). Therefore, the analysis of wind power dynamics with the DFIG wind turbines has become a very important research issue, especially during transient faults. This paper presents fuzzy logic control of doubly fed induction generator (DFIG) wind turbine in a sample power system. Fuzzy logic controller is applied to rotor side converter for active power control and voltage regulation of wind turbine.

Application of the removal of pollutants from textile industry wastewater in constructed wetlands using fuzzy logic.

PubMed

Dogdu, Gamze; Yalcuk, Arda; Postalcioglu, Seda

2017-02-01

There are more than a hundred textile industries in Turkey that discharge large quantities of dye-rich wastewater, resulting in water pollution. Such effluents must be treated to meet discharge limits imposed by the Water Framework Directive in Turkey. Industrial treatment facilities must be required to monitor operations, keep them cost-effective, prevent operational faults, discharge-limit infringements, and water pollution. This paper proposes the treatment of actual textile wastewater by vertical flow constructed wetland (VFCW) systems operation and monitoring effluent wastewater quality using fuzzy logic with a graphical user interface. The treatment performance of VFCW is investigated in terms of chemical oxygen demand and ammonium nitrogen (NH4-N) content, color, and pH parameters during a 75-day period of operation. A computer program was developed with a fuzzy logic system (a decision- making tool) to graphically present (via a status analysis chart) the quality of treated textile effluent in relation to the Turkish Water Pollution Control Regulation. Fuzzy logic is used in the evaluation of data obtained from the VFCW systems and for notification of critical states exceeding the discharge limits. This creates a warning chart that reports any errors encountered in a reactor during the collection of any sample to the concerned party.

Programmer's guide to the fuzzy logic ramp metering algorithm : software design, integration, testing, and evaluation

DOT National Transportation Integrated Search

2000-02-01

A Fuzzy Logic Ramp Metering Algorithm was implemented on 126 ramps in the greater Seattle area. This report documents the implementation of the Fuzzy Logic Ramp Metering Algorithm at the Northwest District of the Washington State Department of Transp...

Hybrid neural network and fuzzy logic approaches for rendezvous and capture in space

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Castellano, Timothy

1991-01-01

The nonlinear behavior of many practical systems and unavailability of quantitative data regarding the input-output relations makes the analytical modeling of these systems very difficult. On the other hand, approximate reasoning-based controllers which do not require analytical models have demonstrated a number of successful applications such as the subway system in the city of Sendai. These applications have mainly concentrated on emulating the performance of a skilled human operator in the form of linguistic rules. However, the process of learning and tuning the control rules to achieve the desired performance remains a difficult task. Fuzzy Logic Control is based on fuzzy set theory. A fuzzy set is an extension of a crisp set. Crisp sets only allow full membership or no membership at all, whereas fuzzy sets allow partial membership. In other words, an element may partially belong to a set.

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.

Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

ERIC Educational Resources Information Center

Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

2016-01-01

In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

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.

The comparison of manual and LabVIEW-based fuzzy control on mechanical ventilation.

PubMed

Guler, Hasan; Ata, Fikret

2014-09-01

The aim of this article is to develop a knowledge-based therapy for management of rats with respiratory distress. A mechanical ventilator was designed to achieve this aim. The designed ventilator is called an intelligent mechanical ventilator since fuzzy logic was used to control the pneumatic equipment according to the rat's status. LabVIEW software was used to control all equipments in the ventilator prototype and to monitor respiratory variables in the experiment. The designed ventilator can be controlled both manually and by fuzzy logic. Eight female Wistar-Albino rats were used to test the designed ventilator and to show the effectiveness of fuzzy control over manual control on pressure control ventilation mode. The anesthetized rats were first ventilated for 20 min manually. After that time, they were ventilated for 20 min by fuzzy logic. Student's t-test for p < 0.05 was applied to the measured minimum, maximum and mean peak inspiration pressures to analyze the obtained results. The results show that there is no statistical difference in the rat's lung parameters before and after the experiments. It can be said that the designed ventilator and developed knowledge-based therapy support artificial respiration of living things successfully. © IMechE 2014.

Challenges And Results of the Applications of Fuzzy Logic in the Classification of Rich Galaxy Clusters

NASA Astrophysics Data System (ADS)

Santiago Girola Schneider, Rafael

2015-08-01

The fuzzy logic is a branch of the artificial intelligence founded on the concept that 'everything is a matter of degree.' It intends to create mathematical approximations on the resolution of certain types of problems. In addition, it aims to produce exact results obtained from imprecise data, for which it is particularly useful for electronic and computer applications. This enables it to handle vague or unspecific information when certain parts of a system are unknown or ambiguous and, therefore, they cannot be measured in a reliable manner. Also, when the variation of a variable can produce an alteration on the others.The main focus of this paper is to prove the importance of these techniques formulated from a theoretical analysis on its application on ambiguous situations in the field of the rich clusters of galaxies. The purpose is to show its applicability in the several classification systems proposed for the rich clusters, which are based on criteria such as the level of richness of the cluster, the distribution of the brightest galaxies, whether there are signs of type-cD galaxies or not or the existence of sub-clusters.Fuzzy logic enables the researcher to work with “imprecise” information implementing fuzzy sets and combining rules to define actions. The control systems based on fuzzy logic join input variables that are defined in terms of fuzzy sets through rule groups that produce one or several output values of the system under study. From this context, the application of the fuzzy logic’s techniques approximates the solution of the mathematical models in abstractions about the rich galaxy cluster classification of physical properties in order to solve the obscurities that must be confronted by an investigation group in order to make a decision.

Development of fuzzy air quality index using soft computing approach.

PubMed

Mandal, T; Gorai, A K; Pathak, G

2012-10-01

Proper assessment of air quality status in an atmosphere based on limited observations is an essential task for meeting the goals of environmental management. A number of classification methods are available for estimating the changing status of air quality. However, a discrepancy frequently arises from the quality criteria of air employed and vagueness or fuzziness embedded in the decision making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies like air quality index when describing integrated air quality conditions with respect to various pollutants parameters and time of exposure. In recent years, the fuzzy logic-based methods have demonstrated to be appropriated to address uncertainty and subjectivity in environmental issues. In the present study, a methodology based on fuzzy inference systems (FIS) to assess air quality is proposed. This paper presents a comparative study to assess status of air quality using fuzzy logic technique and that of conventional technique. The findings clearly indicate that the FIS may successfully harmonize inherent discrepancies and interpret complex conditions.

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.

Construction of a fuzzy and Boolean logic gates based on DNA.

PubMed

Zadegan, Reza M; Jepsen, Mette D E; Hildebrandt, Lasse L; Birkedal, Victoria; Kjems, Jørgen

2015-04-17

Logic gates are devices that can perform logical operations by transforming a set of inputs into a predictable single detectable output. The hybridization properties, structure, and function of nucleic acids can be used to make DNA-based logic gates. These devices are important modules in molecular computing and biosensing. The ideal logic gate system should provide a wide selection of logical operations, and be integrable in multiple copies into more complex structures. Here we show the successful construction of a small DNA-based logic gate complex that produces fluorescent outputs corresponding to the operation of the six Boolean logic gates AND, NAND, OR, NOR, XOR, and XNOR. The logic gate complex is shown to work also when implemented in a three-dimensional DNA origami box structure, where it controlled the position of the lid in a closed or open position. Implementation of multiple microRNA sensitive DNA locks on one DNA origami box structure enabled fuzzy logical operation that allows biosensing of complex molecular signals. Integrating logic gates with DNA origami systems opens a vast avenue to applications in the fields of nanomedicine for diagnostics and therapeutics. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Optimization of fertirrigation efficiency in strawberry crops by application of fuzzy logic techniques.

PubMed

de la Torre, M L; Grande, J A; Aroba, J; Andujar, J M

2005-11-01

A high level of price support has favoured intensive agriculture and an increasing use of fertilisers and pesticides. This has resulted in the pollution of water and soils and damage to certain eco-systems. The target relationship that must be established between agriculture and environment can be called "sustainable agriculture". In this work we aim at relating strawberry total yield with nitrate concentration in water at different soil depths. To achieve this objective, we have used the Predictive Fuzzy Rules Generator (PreFuRGe) tool, based on fuzzy logic and data mining, by means of which the dose that allows a balance between yield and environmental damage minimization can be determined. This determination is quite simple and is done directly from the obtained charts. This technique can be used in other types of crops permitting one to determine in a precise way at which depth the appropriate dose of nitrate fertilizer must be correctly applied, on the one hand providing the maximum yield but, on the other hand, with the minimum loss of nitrates that leachate through the saturated zone polluting aquifers.

Implementation of logic functions and computations by chemical kinetics

NASA Astrophysics Data System (ADS)

Hjelmfelt, A.; Ross, J.

We review our work on the computational functions of the kinetics of chemical networks. We examine spatially homogeneous networks which are based on prototypical reactions occurring in living cells and show the construction of logic gates and sequential and parallel networks. This work motivates the study of an important biochemical pathway, glycolysis, and we demonstrate that the switch that controls the flux in the direction of glycolysis or gluconeogenesis may be described as a fuzzy AND operator. We also study a spatially inhomogeneous network which shares features of theoretical and biological neural networks.

Through-wall image enhancement using fuzzy and QR decomposition.

PubMed

Riaz, Muhammad Mohsin; Ghafoor, Abdul

2014-01-01

QR decomposition and fuzzy logic based scheme is proposed for through-wall image enhancement. QR decomposition is less complex compared to singular value decomposition. Fuzzy inference engine assigns weights to different overlapping subspaces. Quantitative measures and visual inspection are used to analyze existing and proposed techniques.

The Absolute Stability Analysis in Fuzzy Control Systems with Parametric Uncertainties and Reference Inputs

NASA Astrophysics Data System (ADS)

Wu, Bing-Fei; Ma, Li-Shan; Perng, Jau-Woei

This study analyzes the absolute stability in P and PD type fuzzy logic control systems with both certain and uncertain linear plants. Stability analysis includes the reference input, actuator gain and interval plant parameters. For certain linear plants, the stability (i.e. the stable equilibriums of error) in P and PD types is analyzed with the Popov or linearization methods under various reference inputs and actuator gains. The steady state errors of fuzzy control systems are also addressed in the parameter plane. The parametric robust Popov criterion for parametric absolute stability based on Lur'e systems is also applied to the stability analysis of P type fuzzy control systems with uncertain plants. The PD type fuzzy logic controller in our approach is a single-input fuzzy logic controller and is transformed into the P type for analysis. In our work, the absolute stability analysis of fuzzy control systems is given with respect to a non-zero reference input and an uncertain linear plant with the parametric robust Popov criterion unlike previous works. Moreover, a fuzzy current controlled RC circuit is designed with PSPICE models. Both numerical and PSPICE simulations are provided to verify the analytical results. Furthermore, the oscillation mechanism in fuzzy control systems is specified with various equilibrium points of view in the simulation example. Finally, the comparisons are also given to show the effectiveness of the analysis method.

Syllogistic reasoning in fuzzy logic and its application to usuality and reasoning with dispositions

NASA Technical Reports Server (NTRS)

Zadeh, L. A.

1985-01-01

A fuzzy syllogism in fuzzy logic is defined to be an inference schema in which the major premise, the minor premise and the conclusion are propositions containing fuzzy quantifiers. A basic fuzzy syllogism in fuzzy logic is the intersection/product syllogism. Several other basic syllogisms are developed that may be employed as rules of combination of evidence in expert systems. Among these is the consequent conjunction syllogism. Furthermore, it is shown that syllogistic reasoning in fuzzy logic provides a basis for reasoning with dispositions; that is, with propositions that are preponderantly but not necessarily always true. It is also shown that the concept of dispositionality is closely related to the notion of usuality and serves as a basis for what might be called a theory of usuality - a theory which may eventually provide a computational framework for commonsense reasoning.

Fuzzy mobile-robot positioning in intelligent spaces using wireless sensor networks.

PubMed

Herrero, David; Martínez, Humberto

2011-01-01

This work presents the development and experimental evaluation of a method based on fuzzy logic to locate mobile robots in an Intelligent Space using wireless sensor networks (WSNs). The problem consists of locating a mobile node using only inter-node range measurements, which are estimated by radio frequency signal strength attenuation. The sensor model of these measurements is very noisy and unreliable. The proposed method makes use of fuzzy logic for modeling and dealing with such uncertain information. Besides, the proposed approach is compared with a probabilistic technique showing that the fuzzy approach is able to handle highly uncertain situations that are difficult to manage by well-known localization methods.

Evaluation of a fuzzy logic ramp metering algorithm : a comparative study among three ramp metering algorithms used in the greater Seattle area

DOT National Transportation Integrated Search

2000-02-01

A Fuzzy Logic Ramp Metering Algorithm was implemented on 126 ramps in the greater Seattle area. Two multiple-ramp study sites were evaluted by comparing the fuzzy logic controller (FLC) to the other two ramp metering algorithms in operation at those ...

FUZZY LOGIC CONTROL OF ELECTRIC MOTORS AND MOTOR DRIVES: FEASIBILITY STUDY

EPA Science Inventory

The report gives results of a study (part 1) of fuzzy logic motor control (FLMC). The study included: 1) reviews of existing applications of fuzzy logic, of motor operation, and of motor control; 2) a description of motor control schemes that can utilize FLMC; 3) selection of a m...

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

NASA Astrophysics Data System (ADS)

Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno

2017-03-01

This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

Investigation of the Flutter Suppression by Fuzzy Logic Control for Hypersonic Wing

NASA Astrophysics Data System (ADS)

Li, Dongxu; Luo, Qing; Xu, Rui

This paper presents a fundamental study of flutter characteristics and control performance of an aeroelastic system based on a two-dimensional double wedge wing in the hypersonic regime. Dynamic equations were established based on the modified third order nonlinear piston theory and some nonlinear structural effects are also included. A set of important parameters are observed. And then aeroelastic control law is designed to suppress the amplitude of the LCOs for the system in the sub/supercritical speed range by applying fuzzy logic control on the input of the deflection of the flap. The overall effects of the parameters on the aeroelastic system were outlined. Nonlinear aeroelastic responses in the open- and closed-loop system are obtained through numerical methods. The simulations show fuzzy logic control methods are effective in suppressing flutter and provide a smart approach for this complicated system.

Fuzzy logic based expert system for the treatment of mobile tooth.

PubMed

Mago, Vijay Kumar; Mago, Anjali; Sharma, Poonam; Mago, Jagmohan

2011-01-01

The aim of this research work is to design an expert system to assist dentist in treating the mobile tooth. There is lack of consistency among dentists in choosing the treatment plan. Moreover, there is no expert system currently available to verify and support such decision making in dentistry. A Fuzzy Logic based expert system has been designed to accept imprecise and vague values of dental sign-symptoms related to mobile tooth and the system suggests treatment plan(s). The comparison of predictions made by the system with those of the dentist is conducted. Chi-square Test of homogeneity is conducted and it is found that the system is capable of predicting accurate results. With this system, dentist feels more confident while planning the treatment of mobile tooth as he can verify his decision with the expert system. The authors also argue that Fuzzy Logic provides an appropriate mechanism to handle imprecise values of dental domain.

Fuzzy Logic Module of Convolutional Neural Network for Handwritten Digits Recognition

NASA Astrophysics Data System (ADS)

Popko, E. A.; Weinstein, I. A.

2016-08-01

Optical character recognition is one of the important issues in the field of pattern recognition. This paper presents a method for recognizing handwritten digits based on the modeling of convolutional neural network. The integrated fuzzy logic module based on a structural approach was developed. Used system architecture adjusted the output of the neural network to improve quality of symbol identification. It was shown that proposed algorithm was flexible and high recognition rate of 99.23% was achieved.

Use of Fuzzy rainfall-runoff predictions for claypan watersheds with conservation buffers in Northeast Missouri

USDA-ARS?s Scientific Manuscript database

Despite increased interest in watershed scale model simulations, literature lacks application of long-term data in fuzzy logic simulations and comparing outputs with physically based models such as APEX (Agricultural Policy Environmental eXtender). The objective of this study was to develop a fuzzy...

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.

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.

A Logical Framework for Service Migration Based Survivability

DTIC Science & Technology

2016-06-24

platforms; Service Migration Strategy Fuzzy Inference System Knowledge Base Fuzzy rules representing domain expert knowledge about implications of...service migration strategy. Our approach uses expert knowledge as linguistic reasoning rules and takes service programs damage assessment, service...programs complexity, and available network capability as input. The fuzzy inference system includes four components as shown in Figure 5: (1) a knowledge

Multi-objective decision-making under uncertainty: Fuzzy logic methods

NASA Technical Reports Server (NTRS)

Hardy, Terry L.

1994-01-01

Selecting the best option among alternatives is often a difficult process. This process becomes even more difficult when the evaluation criteria are vague or qualitative, and when the objectives vary in importance and scope. Fuzzy logic allows for quantitative representation of vague or fuzzy objectives, and therefore is well-suited for multi-objective decision-making. This paper presents methods employing fuzzy logic concepts to assist in the decision-making process. In addition, this paper describes software developed at NASA Lewis Research Center for assisting in the decision-making process. Two diverse examples are used to illustrate the use of fuzzy logic in choosing an alternative among many options and objectives. One example is the selection of a lunar lander ascent propulsion system, and the other example is the selection of an aeration system for improving the water quality of the Cuyahoga River in Cleveland, Ohio. The fuzzy logic techniques provided here are powerful tools which complement existing approaches, and therefore should be considered in future decision-making activities.

Fuzzy control of a fluidized bed dryer

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

Taprantzis, A.V.; Siettos, C.I.; Bafas, G.V.

1997-05-01

Fluidized bed dryers are utilized in almost every area of drying applications and therefore improved control strategies are always of great interest. The nonlinear character of the process, exhibited in the mathematical model and the open loop analysis, implies that a fuzzy logic controller is appropriate because, in contrast with conventional control schemes, fuzzy control inherently compensates for process nonlinearities and exhibits more robust behavior. In this study, a fuzzy logic controller is proposed; its design is based on a heuristic approach and its performance is compared against a conventional PI controller for a variety of responses. It is shownmore » that the fuzzy controller exhibits a remarkable dynamic behavior, equivalent if not better than the PI controller, for a wide range of disturbances. In addition, the proposed fuzzy controller seems to be less sensitive to the nonlinearities of the process, achieves energy savings and enables MIMO control.« less

Computer vision for general purpose visual inspection: a fuzzy logic approach

NASA Astrophysics Data System (ADS)

Chen, Y. H.

In automatic visual industrial inspection, computer vision systems have been widely used. Such systems are often application specific, and therefore require domain knowledge in order to have a successful implementation. Since visual inspection can be viewed as a decision making process, it is argued that the integration of fuzzy logic analysis and computer vision systems provides a practical approach to general purpose visual inspection applications. This paper describes the development of an integrated fuzzy-rule-based automatic visual inspection system. Domain knowledge about a particular application is represented as a set of fuzzy rules. From the status of predefined fuzzy variables, the set of fuzzy rules are defuzzified to give the inspection results. A practical application where IC marks (often in the forms of English characters and a company logo) inspection is demonstrated, which shows a more consistent result as compared to a conventional thresholding method.

Evolutionary fuzzy modeling human diagnostic decisions.

PubMed

Peña-Reyes, Carlos Andrés

2004-05-01

Fuzzy CoCo is a methodology, combining fuzzy logic and evolutionary computation, for constructing systems able to accurately predict the outcome of a human decision-making process, while providing an understandable explanation of the underlying reasoning. Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (accuracy) and linguistic representation (interpretability). However, fuzzy modeling--meaning the construction of fuzzy systems--is an arduous task, demanding the identification of many parameters. To solve it, we use evolutionary computation techniques (specifically cooperative coevolution), which are widely used to search for adequate solutions in complex spaces. We have successfully applied the algorithm to model the decision processes involved in two breast cancer diagnostic problems, the WBCD problem and the Catalonia mammography interpretation problem, obtaining systems both of high performance and high interpretability. For the Catalonia problem, an evolved system was embedded within a Web-based tool-called COBRA-for aiding radiologists in mammography interpretation.

CONCAM's Fuzzy-Logic All-Sky Star Recognition Algorithm

NASA Astrophysics Data System (ADS)

Shamir, L.; Nemiroff, R. J.

2004-05-01

One of the purposes of the global Night Sky Live (NSL) network of fisheye CONtinuous CAMeras (CONCAMs) is to monitor and archive the entire bright night sky, track stellar variability, and search for transients. The high quality of raw CONCAM data allows automation of stellar object recognition, although distortions of the fisheye lens and frequent slight shifts in CONCAM orientations can make even this seemingly simple task formidable. To meet this challenge, a fuzzy logic based algorithm has been developed that transforms (x,y) image coordinates in the CCD frame into fuzzy right ascension and declination coordinates for use in matching with star catalogs. Using a training set of reference stars, the algorithm statically builds the fuzzy logic model. At runtime, the algorithm searches for peaks, and then applies the fuzzy logic model to perform the coordinate transformation before choosing the optimal star catalog match. The present fuzzy-logic algorithm works much better than our first generation, straightforward coordinate transformation formula. Following this essential step, algorithms dealing with the higher level data products can then provide a stream of photometry for a few hundred stellar objects visible in the night sky. Accurate photometry further enables the computation of all-sky maps of skyglow and opacity, as well as a search for uncataloged transients. All information is stored in XML-like tagged ASCII files that are instantly copied to the public domain and available at http://NightSkyLive.net. Currently, the NSL software detects stars and creates all-sky image files from eight different locations around the globe every 3 minutes and 56 seconds.

Doubly Fed Induction Generator Wind Turbines with Fuzzy Controller: A Survey

PubMed Central

Sathiyanarayanan, J. S.; Senthil Kumar, A.

2014-01-01

Wind energy is one of the extraordinary sources of renewable energy due to its clean character and free availability. With the increasing wind power penetration, the wind farms are directly influencing the power systems. The majority of wind farms are using variable speed wind turbines equipped with doubly fed induction generators (DFIG) due to their advantages over other wind turbine generators (WTGs). Therefore, the analysis of wind power dynamics with the DFIG wind turbines has become a very important research issue, especially during transient faults. This paper presents fuzzy logic control of doubly fed induction generator (DFIG) wind turbine in a sample power system. Fuzzy logic controller is applied to rotor side converter for active power control and voltage regulation of wind turbine. PMID:25028677

Fuzzy Logic for Incidence Geometry

PubMed Central

2016-01-01

The paper presents a mathematical framework for approximate geometric reasoning with extended objects in the context of Geography, in which all entities and their relationships are described by human language. These entities could be labelled by commonly used names of landmarks, water areas, and so forth. Unlike single points that are given in Cartesian coordinates, these geographic entities are extended in space and often loosely defined, but people easily perform spatial reasoning with extended geographic objects “as if they were points.” Unfortunately, up to date, geographic information systems (GIS) miss the capability of geometric reasoning with extended objects. The aim of the paper is to present a mathematical apparatus for approximate geometric reasoning with extended objects that is usable in GIS. In the paper we discuss the fuzzy logic (Aliev and Tserkovny, 2011) as a reasoning system for geometry of extended objects, as well as a basis for fuzzification of the axioms of incidence geometry. The same fuzzy logic was used for fuzzification of Euclid's first postulate. Fuzzy equivalence relation “extended lines sameness” is introduced. For its approximation we also utilize a fuzzy conditional inference, which is based on proposed fuzzy “degree of indiscernibility” and “discernibility measure” of extended points. PMID:27689133

Fuzzy logic and causal reasoning with an 'n' of 1 for diagnosis and treatment of the stroke patient.

PubMed

Helgason, Cathy M; Jobe, Thomas H

2004-03-01

The current scientific model for clinical decision-making is founded on binary or Aristotelian logic, classical set theory and probability-based statistics. Evidence-based medicine has been established as the basis for clinical recommendations. There is a problem with this scientific model when the physician must diagnose and treat the individual patient. The problem is a paradox, which is that the scientific model of evidence-based medicine is based upon a hypothesis aimed at the group and therefore, any conclusions cannot be extrapolated but to a degree to the individual patient. This extrapolation is dependent upon the expertise of the physician. A fuzzy logic multivalued-based scientific model allows this expertise to be numerically represented and solves the clinical paradox of evidence-based medicine.

Multi-layered reasoning by means of conceptual fuzzy sets

NASA Technical Reports Server (NTRS)

Takagi, Tomohiro; Imura, Atsushi; Ushida, Hirohide; Yamaguchi, Toru

1993-01-01

The real world consists of a very large number of instances of events and continuous numeric values. On the other hand, people represent and process their knowledge in terms of abstracted concepts derived from generalization of these instances and numeric values. Logic based paradigms for knowledge representation use symbolic processing both for concept representation and inference. Their underlying assumption is that a concept can be defined precisely. However, as this assumption hardly holds for natural concepts, it follows that symbolic processing cannot deal with such concepts. Thus symbolic processing has essential problems from a practical point of view of applications in the real world. In contrast, fuzzy set theory can be viewed as a stronger and more practical notation than formal, logic based theories because it supports both symbolic processing and numeric processing, connecting the logic based world and the real world. In this paper, we propose multi-layered reasoning by using conceptual fuzzy sets (CFS). The general characteristics of CFS are discussed along with upper layer supervision and context dependent processing.

A Novel Strain-Based Method to Estimate Tire Conditions Using Fuzzy Logic for Intelligent Tires.

PubMed

Garcia-Pozuelo, Daniel; Olatunbosun, Oluremi; Yunta, Jorge; Yang, Xiaoguang; Diaz, Vicente

2017-02-10

The so-called intelligent tires are one of the most promising research fields for automotive engineers. These tires are equipped with sensors which provide information about vehicle dynamics. Up to now, the commercial intelligent tires only provide information about inflation pressure and their contribution to stability control systems is currently very limited. Nowadays one of the major problems for intelligent tire development is how to embed feasible and low cost sensors to obtain reliable information such as inflation pressure, vertical load or rolling speed. These parameters provide key information for vehicle dynamics characterization. In this paper, we propose a novel algorithm based on fuzzy logic to estimate the mentioned parameters by means of a single strain-based system. Experimental tests have been carried out in order to prove the suitability and durability of the proposed on-board strain sensor system, as well as its low cost advantages, and the accuracy of the obtained estimations by means of fuzzy logic.

A new robust control scheme using second order sliding mode and fuzzy logic of a DFIM supplied by two five-level SVPWM inverters

NASA Astrophysics Data System (ADS)

Boudjema, Zinelaabidine; Taleb, Rachid; Bounadja, Elhadj

2017-02-01

Traditional filed oriented control strategy including proportional-integral (PI) regulator for the speed drive of the doubly fed induction motor (DFIM) have some drawbacks such as parameter tuning complications, mediocre dynamic performances and reduced robustness. Therefore, based on the analysis of the mathematical model of a DFIM supplied by two five-level SVPWM inverters, this paper proposes a new robust control scheme based on super twisting sliding mode and fuzzy logic. The conventional sliding mode control (SMC) has vast chattering effect on the electromagnetic torque developed by the DFIM. In order to resolve this problem, a second order sliding mode technique based on super twisting algorithm and fuzzy logic functions is employed. The validity of the employed approach was tested by using Matlab/Simulink software. Interesting simulation results were obtained and remarkable advantages of the proposed control scheme were exposed including simple design of the control system, reduced chattering as well as the other advantages.

A Novel Strain-Based Method to Estimate Tire Conditions Using Fuzzy Logic for Intelligent Tires

PubMed Central

Garcia-Pozuelo, Daniel; Olatunbosun, Oluremi; Yunta, Jorge; Yang, Xiaoguang; Diaz, Vicente

2017-01-01

The so-called intelligent tires are one of the most promising research fields for automotive engineers. These tires are equipped with sensors which provide information about vehicle dynamics. Up to now, the commercial intelligent tires only provide information about inflation pressure and their contribution to stability control systems is currently very limited. Nowadays one of the major problems for intelligent tire development is how to embed feasible and low cost sensors to obtain reliable information such as inflation pressure, vertical load or rolling speed. These parameters provide key information for vehicle dynamics characterization. In this paper, we propose a novel algorithm based on fuzzy logic to estimate the mentioned parameters by means of a single strain-based system. Experimental tests have been carried out in order to prove the suitability and durability of the proposed on-board strain sensor system, as well as its low cost advantages, and the accuracy of the obtained estimations by means of fuzzy logic. PMID:28208631

Fuzzy logic control of an AGV

NASA Astrophysics Data System (ADS)

Kelkar, Nikhal; Samu, Tayib; Hall, Ernest L.

1997-09-01

Automated guided vehicles (AGVs) have many potential applications in manufacturing, medicine, space and defense. The purpose of this paper is to describe exploratory research on the design of a modular autonomous mobile robot controller. The controller incorporates a fuzzy logic approach for steering and speed control, a neuro-fuzzy approach for ultrasound sensing (not discussed in this paper) and an overall expert system. The advantages of a modular system are related to portability and transportability, i.e. any vehicle can become autonomous with minimal modifications. A mobile robot test-bed has been constructed using a golf cart base. This cart has full speed control with guidance provided by a vision system and obstacle avoidance using ultrasonic sensors. The speed and steering fuzzy logic controller is supervised by a 486 computer through a multi-axis motion controller. The obstacle avoidance system is based on a micro-controller interfaced with six ultrasonic transducers. This micro- controller independently handles all timing and distance calculations and sends a steering angle correction back to the computer via the serial line. This design yields a portable independent system in which high speed computer communication is not necessary. Vision guidance is accomplished with a CCD camera with a zoom lens. The data is collected by a vision tracking device that transmits the X, Y coordinates of the lane marker to the control computer. Simulation and testing of these systems yielded promising results. This design, in its modularity, creates a portable autonomous fuzzy logic controller applicable to any mobile vehicle with only minor adaptations.

Dc microgrid stabilization through fuzzy control of interleaved, heterogeneous storage elements

NASA Astrophysics Data System (ADS)

Smith, Robert David

As microgrid power systems gain prevalence and renewable energy comprises greater and greater portions of distributed generation, energy storage becomes important to offset the higher variance of renewable energy sources and maximize their usefulness. One of the emerging techniques is to utilize a combination of lead-acid batteries and ultracapacitors to provide both short and long-term stabilization to microgrid systems. The different energy and power characteristics of batteries and ultracapacitors imply that they ought to be utilized in different ways. Traditional linear controls can use these energy storage systems to stabilize a power grid, but cannot effect more complex interactions. This research explores a fuzzy logic approach to microgrid stabilization. The ability of a fuzzy logic controller to regulate a dc bus in the presence of source and load fluctuations, in a manner comparable to traditional linear control systems, is explored and demonstrated. Furthermore, the expanded capabilities (such as storage balancing, self-protection, and battery optimization) of a fuzzy logic system over a traditional linear control system are shown. System simulation results are presented and validated through hardware-based experiments. These experiments confirm the capabilities of the fuzzy logic control system to regulate bus voltage, balance storage elements, optimize battery usage, and effect self-protection.

Fuzzy logic

NASA Technical Reports Server (NTRS)

Zadeh, Lofti A.

1988-01-01

The author presents a condensed exposition of some basic ideas underlying fuzzy logic and describes some representative applications. The discussion covers basic principles; meaning representation and inference; basic rules of inference; and the linguistic variable and its application to fuzzy control.

A new approach of active compliance control via fuzzy logic control for multifingered robot hand

NASA Astrophysics Data System (ADS)

Jamil, M. F. A.; Jalani, J.; Ahmad, A.

2016-07-01

Safety is a vital issue in Human-Robot Interaction (HRI). In order to guarantee safety in HRI, a model reference impedance control can be a very useful approach introducing a compliant control. In particular, this paper establishes a fuzzy logic compliance control (i.e. active compliance control) to reduce impact and forces during physical interaction between humans/objects and robots. Exploiting a virtual mass-spring-damper system allows us to determine a desired compliant level by understanding the behavior of the model reference impedance control. The performance of fuzzy logic compliant control is tested in simulation for a robotic hand known as the RED Hand. The results show that the fuzzy logic is a feasible control approach, particularly to control position and to provide compliant control. In addition, the fuzzy logic control allows us to simplify the controller design process (i.e. avoid complex computation) when dealing with nonlinearities and uncertainties.

An Efficient Interval Type-2 Fuzzy CMAC for Chaos Time-Series Prediction and Synchronization.

PubMed

Lee, Ching-Hung; Chang, Feng-Yu; Lin, Chih-Min

2014-03-01

This paper aims to propose a more efficient control algorithm for chaos time-series prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) is proposed. In some special cases, this T2FCMAC can be reduced to an interval type-2 fuzzy neural network, a fuzzy neural network, and a fuzzy cerebellar model articulation controller (CMAC). So, this T2FCMAC is a more generalized network with better learning ability, thus, it is used for the chaos time-series prediction and synchronization. Moreover, this T2FCMAC realizes the un-normalized interval type-2 fuzzy logic system based on the structure of the CMAC. It can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC. Unlike most of the interval type-2 fuzzy system, the type-reduction of T2FCMAC is bypassed due to the property of un-normalized interval type-2 fuzzy logic system. This causes T2FCMAC to have lower computational complexity and is more practical. For chaos time-series prediction and synchronization applications, the training architectures with corresponding convergence analyses and optimal learning rates based on Lyapunov stability approach are introduced. Finally, two illustrated examples are presented to demonstrate the performance of the proposed T2FCMAC.

Design and implementation of a fuzzy logic-based state-of-charge meter for Li-ion batteries used in portable defibrillators

NASA Astrophysics Data System (ADS)

Singh, Pritpal; Vinjamuri, Ramana; Wang, Xiquan; Reisner, David

A fuzzy logic-based state-of-charge meter is being developed for Li-ion batteries for potential use in portable defibrillators. ac impedance and voltage recovery measurements have been made which are used as the input parameters for the fuzzy logic model. The load profile for the Li-ion battery packs comprises a continuous 1.4 A constant current discharge periodically interrupted by 10 A pulses. As the battery is cycled the available capacity diminishes and so the number of 10 A pulses that may be delivered decreases. Measurements are being made on a total of three battery packs at three different temperatures (0, 20 and 40 °C) and as expected the number of pulses deliverable by the battery pack diminishes as temperature is decreased. For example, at room temperature the battery pack was initially able to deliver 42 pulses early in the cycle life whereas at 0 °C the battery-pack is only able to initially deliver 12 pulses. The voltage recovery profile upon removal of the 10 A load has been used both in the time domain and frequency domain to develop fuzzy logic models to estimate the number of remaining pulses that the battery-pack can deliver. Accurate models are being developed to estimate the number of pulses that the battery pack can deliver at various stages of its cycle life and at the different temperatures. With sufficient data collected for the battery packs at room temperature accurate fuzzy logic models have been developed for estimation of state-of-charge and implemented in the Motorola MC 68HC12 microcontroller.

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.

Determining a human cardiac pacemaker using fuzzy logic

NASA Astrophysics Data System (ADS)

Varnavsky, A. N.; Antonenco, A. V.

2017-01-01

The paper presents a possibility of estimating a human cardiac pacemaker using combined application of nonlinear integral transformation and fuzzy logic, which allows carrying out the analysis in the real-time mode. The system of fuzzy logical conclusion is proposed, membership functions and rules of fuzzy products are defined. It was shown that the ratio of the value of a truth degree of the winning rule condition to the value of a truth degree of any other rule condition is at least 3.

An Application of Fuzzy Logic Control to a Classical Military Tracking Problem

DTIC Science & Technology

1994-05-19

34, Fuzzy Sets and Systems, vol.4., 1980, pp.13-30. Berenji , Hamid R . and Pratap Khedkar. "Learning and Tuning Fuzzy Logic Controllers Through...A TRIDENT SCHOLAR PROJECT REPORT" NO. 222 "An Application of Fuzzy Logic Control to a Classical Military Tracking Problem" DTIC •S r F UNITED STATES...Zq qAvail andlor ____________________I__________ Dist SpecialDate USNA- 1531-2 REPORT DOCUMENTATION PAGE r •,,,op APmw OMB no. 0704.0188 ¢iQiiati~m.f

Method and system of Jones-matrix mapping of blood plasma films with "fuzzy" analysis in differentiation of breast pathology changes

NASA Astrophysics Data System (ADS)

Zabolotna, Natalia I.; Radchenko, Kostiantyn O.; Karas, Oleksandr V.

2018-01-01

A fibroadenoma diagnosing of breast using statistical analysis (determination and analysis of statistical moments of the 1st-4th order) of the obtained polarization images of Jones matrix imaginary elements of the optically thin (attenuation coefficient τ <= 0,1 ) blood plasma films with further intellectual differentiation based on the method of "fuzzy" logic and discriminant analysis were proposed. The accuracy of the intellectual differentiation of blood plasma samples to the "norm" and "fibroadenoma" of breast was 82.7% by the method of linear discriminant analysis, and by the "fuzzy" logic method is 95.3%. The obtained results allow to confirm the potentially high level of reliability of the method of differentiation by "fuzzy" analysis.

Stock and option portfolio using fuzzy logic approach

NASA Astrophysics Data System (ADS)

Sumarti, Novriana; Wahyudi, Nanang

2014-03-01

Fuzzy Logic in decision-making process has been widely implemented in various problems in industries. It is the theory of imprecision and uncertainty that was not based on probability theory. Fuzzy Logic adds values of degree between absolute true and absolute false. It starts with and builds on a set of human language rules supplied by the user. The fuzzy systems convert these rules to their mathematical equivalents. This could simplify the job of the system designer and the computer, and results in much more accurate representations of the way systems behave in the real world. In this paper we examine the decision making process of stock and option trading by the usage of MACD (Moving Average Convergence Divergence) technical analysis and Option Pricing with Fuzzy Logic approach. MACD technical analysis is for the prediction of the trends of underlying stock prices, such as bearish (going downward), bullish (going upward), and sideways. By using Fuzzy C-Means technique and Mamdani Fuzzy Inference System, we define the decision output where the value of MACD is high then decision is "Strong Sell", and the value of MACD is Low then the decision is "Strong Buy". We also implement the fuzzification of the Black-Scholes option-pricing formula. The stock and options methods are implemented on a portfolio of one stock and its options. Even though the values of input data, such as interest rates, stock price and its volatility, cannot be obtain accurately, these fuzzy methods can give a belief degree of the calculated the Black-Scholes formula so we can make the decision on option trading. The results show the good capability of the methods in the prediction of stock price trends. The performance of the simulated portfolio for a particular period of time also shows good return.

Security risk assessment: applying the concepts of fuzzy logic.

PubMed

Bajpai, Shailendra; Sachdeva, Anish; Gupta, J P

2010-01-15

Chemical process industries (CPI) handling hazardous chemicals in bulk can be attractive targets for deliberate adversarial actions by terrorists, criminals and disgruntled employees. It is therefore imperative to have comprehensive security risk management programme including effective security risk assessment techniques. In an earlier work, it has been shown that security risk assessment can be done by conducting threat and vulnerability analysis or by developing Security Risk Factor Table (SRFT). HAZOP type vulnerability assessment sheets can be developed that are scenario based. In SRFT model, important security risk bearing factors such as location, ownership, visibility, inventory, etc., have been used. In this paper, the earlier developed SRFT model has been modified using the concepts of fuzzy logic. In the modified SRFT model, two linguistic fuzzy scales (three-point and four-point) are devised based on trapezoidal fuzzy numbers. Human subjectivity of different experts associated with previous SRFT model is tackled by mapping their scores to the newly devised fuzzy scale. Finally, the fuzzy score thus obtained is defuzzyfied to get the results. A test case of a refinery is used to explain the method and compared with the earlier work.

A fuzzy-logic based decision-making approach for identification of groundwater quality based on groundwater quality indices.

PubMed

Vadiati, M; Asghari-Moghaddam, A; Nakhaei, M; Adamowski, J; Akbarzadeh, A H

2016-12-15

Due to inherent uncertainties in measurement and analysis, groundwater quality assessment is a difficult task. Artificial intelligence techniques, specifically fuzzy inference systems, have proven useful in evaluating groundwater quality in uncertain and complex hydrogeological systems. In the present study, a Mamdani fuzzy-logic-based decision-making approach was developed to assess groundwater quality based on relevant indices. In an effort to develop a set of new hybrid fuzzy indices for groundwater quality assessment, a Mamdani fuzzy inference model was developed with widely-accepted groundwater quality indices: the Groundwater Quality Index (GQI), the Water Quality Index (WQI), and the Ground Water Quality Index (GWQI). In an effort to present generalized hybrid fuzzy indices a significant effort was made to employ well-known groundwater quality index acceptability ranges as fuzzy model output ranges rather than employing expert knowledge in the fuzzification of output parameters. The proposed approach was evaluated for its ability to assess the drinking water quality of 49 samples collected seasonally from groundwater resources in Iran's Sarab Plain during 2013-2014. Input membership functions were defined as "desirable", "acceptable" and "unacceptable" based on expert knowledge and the standard and permissible limits prescribed by the World Health Organization. Output data were categorized into multiple categories based on the GQI (5 categories), WQI (5 categories), and GWQI (3 categories). Given the potential of fuzzy models to minimize uncertainties, hybrid fuzzy-based indices produce significantly more accurate assessments of groundwater quality than traditional indices. The developed models' accuracy was assessed and a comparison of the performance indices demonstrated the Fuzzy Groundwater Quality Index model to be more accurate than both the Fuzzy Water Quality Index and Fuzzy Ground Water Quality Index models. This suggests that the new hybrid fuzzy indices developed in this research are reliable and flexible when used in groundwater quality assessment for drinking purposes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Simulation of the Predictive Control Algorithm for Container Crane Operation using Matlab Fuzzy Logic Tool Box

NASA Technical Reports Server (NTRS)

Richardson, Albert O.

1997-01-01

This research has investigated the use of fuzzy logic, via the Matlab Fuzzy Logic Tool Box, to design optimized controller systems. The engineering system for which the controller was designed and simulate was the container crane. The fuzzy logic algorithm that was investigated was the 'predictive control' algorithm. The plant dynamics of the container crane is representative of many important systems including robotic arm movements. The container crane that was investigated had a trolley motor and hoist motor. Total distance to be traveled by the trolley was 15 meters. The obstruction height was 5 meters. Crane height was 17.8 meters. Trolley mass was 7500 kilograms. Load mass was 6450 kilograms. Maximum trolley and rope velocities were 1.25 meters per sec. and 0.3 meters per sec., respectively. The fuzzy logic approach allowed the inclusion, in the controller model, of performance indices that are more effectively defined in linguistic terms. These include 'safety' and 'cargo swaying'. Two fuzzy inference systems were implemented using the Matlab simulation package, namely the Mamdani system (which relates fuzzy input variables to fuzzy output variables), and the Sugeno system (which relates fuzzy input variables to crisp output variable). It is found that the Sugeno FIS is better suited to including aspects of those plant dynamics whose mathematical relationships can be determined.

Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems

PubMed Central

2017-01-01

Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI) for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI) using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data. PMID:28806754

Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems.

PubMed

Almaraashi, Majid

2017-01-01

Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI) for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI) using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data.

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.

A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling.

PubMed

Hajri, S; Liouane, N; Hammadi, S; Borne, P

2000-01-01

Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational scheme, heuristic rules for creating the initial population, and a new methodology for mixing and computing genetic operator probabilities.

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.

Improving the anesthetic process by a fuzzy rule based medical decision system.

PubMed

Mendez, Juan Albino; Leon, Ana; Marrero, Ayoze; Gonzalez-Cava, Jose M; Reboso, Jose Antonio; Estevez, Jose Ignacio; Gomez-Gonzalez, José F

2018-01-01

The main objective of this research is the design and implementation of a new fuzzy logic tool for automatic drug delivery in patients undergoing general anesthesia. The aim is to adjust the drug dose to the real patient needs using heuristic knowledge provided by clinicians. A two-level computer decision system is proposed. The idea is to release the clinician from routine tasks so that he can focus on other variables of the patient. The controller uses the Bispectral Index (BIS) to assess the hypnotic state of the patient. Fuzzy controller was included in a closed-loop system to reach the BIS target and reject disturbances. BIS was measured using a BIS VISTA monitor, a device capable of calculating the hypnosis level of the patient through EEG information. An infusion pump with propofol 1% is used to supply the drug to the patient. The inputs to the fuzzy inference system are BIS error and BIS rate. The output is infusion rate increment. The mapping of the input information and the appropriate output is given by a rule-base based on knowledge of clinicians. To evaluate the performance of the fuzzy closed-loop system proposed, an observational study was carried out. Eighty one patients scheduled for ambulatory surgery were randomly distributed in 2 groups: one group using a fuzzy logic based closed-loop system (FCL) to automate the administration of propofol (42 cases); the second group using manual delivering of the drug (39 cases). In both groups, the BIS target was 50. The FCL, designed with intuitive logic rules based on the clinician experience, performed satisfactorily and outperformed the manual administration in patients in terms of accuracy through the maintenance stage. Copyright © 2018 Elsevier B.V. All rights reserved.

A new systematic and quantitative approach to characterization of surface nanostructures using fuzzy logic

NASA Astrophysics Data System (ADS)

Al-Mousa, Amjed A.

Thin films are essential constituents of modern electronic devices and have a multitude of applications in such devices. The impact of the surface morphology of thin films on the device characteristics where these films are used has generated substantial attention to advanced film characterization techniques. In this work, we present a new approach to characterize surface nanostructures of thin films by focusing on isolating nanostructures and extracting quantitative information, such as the shape and size of the structures. This methodology is applicable to any Scanning Probe Microscopy (SPM) data, such as Atomic Force Microscopy (AFM) data which we are presenting here. The methodology starts by compensating the AFM data for some specific classes of measurement artifacts. After that, the methodology employs two distinct techniques. The first, which we call the overlay technique, proceeds by systematically processing the raster data that constitute the scanning probe image in both vertical and horizontal directions. It then proceeds by classifying points in each direction separately. Finally, the results from both the horizontal and the vertical subsets are overlaid, where a final decision on each surface point is made. The second technique, based on fuzzy logic, relies on a Fuzzy Inference Engine (FIE) to classify the surface points. Once classified, these points are clustered into surface structures. The latter technique also includes a mechanism which can consistently distinguish crowded surfaces from those with sparsely distributed structures and then tune the fuzzy technique system uniquely for that surface. Both techniques have been applied to characterize organic semiconductor thin films of pentacene on different substrates. Also, we present a case study to demonstrate the effectiveness of our methodology to identify quantitatively particle sizes of two specimens of gold nanoparticles of different nominal dimensions dispersed on a mica surface. A comparison with other techniques like: thresholding, watershed and edge detection is presented next. Finally, we present a systematic study of the fuzzy logic technique by experimenting with synthetic data. These results are discussed and compared along with the challenges of the two techniques.

Environmental impact assessment procedure: A new approach based on fuzzy logic

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

Peche, Roberto, E-mail: roberto.peche@ehu.e; Rodriguez, Esther, E-mail: esther.rodriguez@ehu.e

2009-09-15

The information related to the different environmental impacts produced by the execution of activities and projects is often limited, described by semantic variables and, affected by a high degree of inaccuracy and uncertainty, thereby making fuzzy logic a suitable tool with which to express and treat this information. The present study proposes a new approach based on fuzzy logic to carry out the environmental impact assessment (EIA) of these activities and projects. Firstly, a set of impact properties is stated and two nondimensional parameters - ranging from 0 to 100 -are assigned, (p{sub i}) to assess the value of themore » property and (v{sub i}) to assess its contribution to each environmental impact. Next, the impact properties are described by means of fuzzy numbers p{sub i}{sup -} using generalised confidence intervals. Then, a procedure based on fuzzy arithmetic is developed to define the assessment functions v-bar = f(p-bar) - conventional mathematical functions, which incorporate the knowledge of these impact properties and give the fuzzy values v{sub i}{sup -} corresponding to each p{sub i}{sup -}. Subsequently, the fuzzy value of each environmental impact V-bar is estimated by aggregation of the values v{sub i}{sup -}, in order to obtain the total positive and negative environmental impacts V{sup +-} and V{sup --} and, later - from them - the total environmental impact of the activity or project TV{sup -}. Finally, the defuzzyfication of TV{sup -} leads to a punctual impact estimator TV{sup (1)} - a conventional EI estimation - and its corresponding uncertainty interval estimator left brace(delta{sub l}(TV{sup -}),delta{sub r}(TV{sup -})right brace, which represent the total value of the environmental impact caused by the execution of the considered activity or project.« less

Reconfigurable fuzzy cell

NASA Technical Reports Server (NTRS)

Salazar, George A. (Inventor)

1993-01-01

This invention relates to a reconfigurable fuzzy cell comprising a digital control programmable gain operation amplifier, an analog-to-digital converter, an electrically erasable PROM, and 8-bit counter and comparator, and supporting logic configured to achieve in real-time fuzzy systems high throughput, grade-of-membership or membership-value conversion of multi-input sensor data. The invention provides a flexible multiplexing-capable configuration, implemented entirely in hardware, for effectuating S-, Z-, and PI-membership functions or combinations thereof, based upon fuzzy logic level-set theory. A membership value table storing 'knowledge data' for each of S-, Z-, and PI-functions is contained within a nonvolatile memory for storing bits of membership and parametric information in a plurality of address spaces. Based upon parametric and control signals, analog sensor data is digitized and converted into grade-of-membership data. In situ learn and recognition modes of operation are also provided.

Investigating the ability of shunt hybrid power filter based on SRF method under non-ideal supply voltage

NASA Astrophysics Data System (ADS)

Omran, Mohamed A.; Mohd, Izzeldin I.; Almelian, Mohamad M.; Ullah Sheikh, Usman; Bofares, Mustafa E. A. A.

2018-04-01

This study presents the capacity of a self-tuning filter based on the synchronous reference frame method with a fuzzy logic controller for the improvement of the efficiency of harmonic suppression of a shunt hybrid active power filter in an unbalanced distorted and un-distorted voltage supply conditions. The simulation results indicated that the filter with a fuzzy logic controller had a good filtering performance in steady and transient states, irrespective of whether the voltage supply is distorted or unbalanced.

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.

High-efficiency induction motor drives using type-2 fuzzy logic

NASA Astrophysics Data System (ADS)

Khemis, A.; Benlaloui, I.; Drid, S.; Chrifi-Alaoui, L.; Khamari, D.; Menacer, A.

2018-03-01

In this work we propose to develop an algorithm for improving the efficiency of an induction motor using type-2 fuzzy logic. Vector control is used to control this motor due to the high performances of this strategy. The type-2 fuzzy logic regulators are developed to obtain the optimal rotor flux for each torque load by minimizing the copper losses. We have compared the performances of our fuzzy type-2 algorithm with the type-1 fuzzy one proposed in the literature. The proposed algorithm is tested with success on the dSPACE DS1104 system even if there is parameters variance.

Energy Management of An Extended Hybrid Renewable Energy System For Isolated Sites Using A Fuzzy Logic Controller

NASA Astrophysics Data System (ADS)

Faquir, Sanaa; Yahyaouy, Ali; Tairi, Hamid; Sabor, Jalal

2018-05-01

This paper presents the implementation of a fuzzy logic controller to manage the flow of energy in an extended hybrid renewable energy system employed to satisfy the load for a wide isolated site at the city of Essaouira in Morocco. To achieve Efficient energy management, the system is combining two important renewable energies: solar and wind. Lithium Ion batteries were also used as storage devices to store the excess of energy provided by the renewable sources or to supply the system with the required energy when the energy delivered by the input sources is not enough to satisfy the load demand. To manage the energy in the system, a controller based on fuzzy logic was implemented. Real data taken from previous research and meteorological sites was used to test the controller.

Characterization of Soil Moisture Level for Rice and Maize Crops using GSM Shield and Arduino Microcontroller

NASA Astrophysics Data System (ADS)

Gines, G. A.; Bea, J. G.; Palaoag, T. D.

2018-03-01

Soil serves a medium for plants growth. One factor that affects soil moisture is drought. Drought has been a major cause of agricultural disaster. Agricultural drought is said to occur when soil moisture is insufficient to meet crop water requirements, resulting in yield losses. In this research, it aimed to characterize soil moisture level for Rice and Maize Crops using Arduino and applying fuzzy logic. System architecture for soil moisture sensor and water pump were the basis in developing the equipment. The data gathered was characterized by applying fuzzy logic. Based on the results, applying fuzzy logic in validating the characterization of soil moisture level for Rice and Maize crops is accurate as attested by the experts. This will help the farmers in monitoring the soil moisture level of the Rice and Maize crops.

Use of Fuzzy Logic Systems for Assessment of Primary Faults

NASA Astrophysics Data System (ADS)

Petrović, Ivica; Jozsa, Lajos; Baus, Zoran

2015-09-01

In electric power systems, grid elements are often subjected to very complex and demanding disturbances or dangerous operating conditions. Determining initial fault or cause of those states is a difficult task. When fault occurs, often it is an imperative to disconnect affected grid element from the grid. This paper contains an overview of possibilities for using fuzzy logic in an assessment of primary faults in the transmission grid. The tool for this task is SCADA system, which is based on information of currents, voltages, events of protection devices and status of circuit breakers in the grid. The function model described with the membership function and fuzzy logic systems will be presented in the paper. For input data, diagnostics system uses information of protection devices tripping, states of circuit breakers and measurements of currents and voltages before and after faults.

Fuzzy logic controller to improve powerline communication

NASA Astrophysics Data System (ADS)

Tirrito, Salvatore

2015-12-01

The Power Line Communications (PLC) technology allows the use of the power grid in order to ensure the exchange of data information among devices. This work proposes an approach, based on Fuzzy Logic, that dynamically manages the amplitude of the signal, with which each node transmits, by processing the master-slave link quality measured and the master-slave distance. The main objective of this is to reduce both the impact of communication interferences induced and power consumption.

Fuzzy logic inference-based Pavement Friction Management and real-time slippery warning systems: A proof of concept study.

PubMed

Najafi, Shahriar; Flintsch, Gerardo W; Khaleghian, Seyedmeysam

2016-05-01

Minimizing roadway crashes and fatalities is one of the primary objectives of highway engineers, and can be achieved in part through appropriate maintenance practices. Maintaining an appropriate level of friction is a crucial maintenance practice, due to the effect it has on roadway safety. This paper presents a fuzzy logic inference system that predicts the rate of vehicle crashes based on traffic level, speed limit, and surface friction. Mamdani and Sugeno fuzzy controllers were used to develop the model. The application of the proposed fuzzy control system in a real-time slippery road warning system is demonstrated as a proof of concept. The results of this study provide a decision support model for highway agencies to monitor their network's friction and make appropriate judgments to correct deficiencies based on crash risk. Furthermore, this model can be implemented in the connected vehicle environment to warn drivers of potentially slippery locations. Published by Elsevier Ltd.

Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

PubMed

Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

2016-03-01

In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

[New horizons in medicine. The application of "fuzzy logic" in clinical and experimental medicine].

PubMed

Guarini, G

1994-06-01

In medicine, the study of physiological and physiopathological problems is generally programmed by elaborating models which respond to the principals of formal logic. This gives the advantage of favouring the transformation of the formal model into a mathematical model of reference which responds to the principles of the set theories. All this is in the utopian wish to obtain as a result of each research, a net answer whether positive or negative, according to the Aristotelian principal of tertium non datur. Taking this into consideration, the A. briefly traces the principles of modal logic and, in particular, those of fuzzy logic, proposing that the latter substitute the actual definition of "logic with more truth values", with that perhaps more pertinent of "logic of conditioned possibilities". After a brief synthesis on the state of the art on the application of fuzzy logic, the A. reports an example of graphic expression of fuzzy logic by demonstrating how the basic glycemic data (expressed by the vectors magnitude) revealed in a sample of healthy individuals, constituted on the whole an unbroken continuous stream of set partials. The A. calls attention to fuzzy logic as a useful instrument to elaborate in a new way the analysis of scenario qualified to acquire the necessary information to single out the critical points which characterize the potential development of any biological phenomenon.

A Grey Fuzzy Logic Approach for Cotton Fibre Selection

NASA Astrophysics Data System (ADS)

Chakraborty, Shankar; Das, Partha Protim; Kumar, Vidyapati

2017-06-01

It is a well known fact that the quality of ring spun yarn predominantly depends on various physical properties of cotton fibre. Any variation in these fibre properties may affect the strength and unevenness of the final yarn. Thus, so as to achieve the desired yarn quality and characteristics, it becomes imperative for the spinning industry personnel to identify the most suitable cotton fibre from a set of feasible alternatives in presence of several conflicting properties/attributes. This cotton fibre selection process can be modelled as a Multi-Criteria Decision Making (MCDM) problem. In this paper, a grey fuzzy logic-based approach is proposed for selection of the most apposite cotton fibre from 17 alternatives evaluated based on six important fibre properties. It is observed that the preference order of the top-ranked cotton fibres derived using the grey fuzzy logic approach closely matches with that attained by the past researchers which proves the application potentiality of this method in solving varying MCDM problems in textile industries.

Fuzzy Logic Decoupled Lateral Control for General Aviation Airplanes

NASA Technical Reports Server (NTRS)

Duerksen, Noel

1997-01-01

It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control different airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control aileron or roll spoiler position. This controller was used to control bank angle for both a piston powered single engine aileron equipped airplane simulation and a business jet simulation which used spoilers for primary roll control. Overspeed, stall and overbank protection were incorporated in the form of expert systems supervisors and weighted fuzzy rules. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic lateral controller could be successfully used on two general aviation aircraft types that have very different characteristics. These controllers worked for both airplanes over their entire flight envelopes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle ]ever travel, etc.). This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.

Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models.

PubMed

Nadiri, Ata Allah; Gharekhani, Maryam; Khatibi, Rahman; Moghaddam, Asghar Asghari

2017-03-01

Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling results and baselines, if any. Therefore, this research presents a supervised committee fuzzy logic (SCFL) method, which uses artificial neural networks to overarch and combine a selection of FL models. The indices are expressed by the widely used DRASTIC framework, which include geological, hydrological, and hydrogeological parameters often subject to uncertainty. DRASTIC indices represent collectively intrinsic (or natural) vulnerability and give a sense of contaminants, such as nitrate-N, percolating to aquifers from the surface. The study area is an aquifer in Ardabil plain, the province of Ardabil, northwest Iran. Improvements on vulnerability indices are achieved by FL techniques, which comprise Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL). As the correlation between estimated DRASTIC vulnerability index values and nitrate-N values is as low as 0.4, it is improved significantly by FL models (SFL, MFL, and LFL), which perform in similar ways but have differences. Their synergy is exploited by SCFL and uses the FL modeling results "conditioned" by nitrate-N values to raise their correlation to higher than 0.9.

An experimental comparison of fuzzy logic and analytic hierarchy process for medical decision support systems.

PubMed

Uzoka, Faith-Michael Emeka; Obot, Okure; Barker, Ken; Osuji, J

2011-07-01

The task of medical diagnosis is a complex one, considering the level vagueness and uncertainty management, especially when the disease has multiple symptoms. A number of researchers have utilized the fuzzy-analytic hierarchy process (fuzzy-AHP) methodology in handling imprecise data in medical diagnosis and therapy. The fuzzy logic is able to handle vagueness and unstructuredness in decision making, while the AHP has the ability to carry out pairwise comparison of decision elements in order to determine their importance in the decision process. This study attempts to do a case comparison of the fuzzy and AHP methods in the development of medical diagnosis system, which involves basic symptoms elicitation and analysis. The results of the study indicate a non-statistically significant relative superiority of the fuzzy technology over the AHP technology. Data collected from 30 malaria patients were used to diagnose using AHP and fuzzy logic independent of one another. The results were compared and found to covary strongly. It was also discovered from the results of fuzzy logic diagnosis covary a little bit more strongly to the conventional diagnosis results than that of AHP. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

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

Navigating a Mobile Robot Across Terrain Using Fuzzy Logic

NASA Technical Reports Server (NTRS)

Seraji, Homayoun; Howard, Ayanna; Bon, Bruce

2003-01-01

A strategy for autonomous navigation of a robotic vehicle across hazardous terrain involves the use of a measure of traversability of terrain within a fuzzy-logic conceptual framework. This navigation strategy requires no a priori information about the environment. Fuzzy logic was selected as a basic element of this strategy because it provides a formal methodology for representing and implementing a human driver s heuristic knowledge and operational experience. Within a fuzzy-logic framework, the attributes of human reasoning and decision- making can be formulated by simple IF (antecedent), THEN (consequent) rules coupled with easily understandable and natural linguistic representations. The linguistic values in the rule antecedents convey the imprecision associated with measurements taken by sensors onboard a mobile robot, while the linguistic values in the rule consequents represent the vagueness inherent in the reasoning processes to generate the control actions. The operational strategies of the human expert driver can be transferred, via fuzzy logic, to a robot-navigation strategy in the form of a set of simple conditional statements composed of linguistic variables. These linguistic variables are defined by fuzzy sets in accordance with user-defined membership functions. The main advantages of a fuzzy navigation strategy lie in the ability to extract heuristic rules from human experience and to obviate the need for an analytical model of the robot navigation process.

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

Pin, F.G.; Bender, S.R.

Most fuzzy logic-based reasoning schemes developed for robot control are fully reactive, i.e., the reasoning modules consist of fuzzy rule bases that represent direct mappings from the stimuli provided by the perception systems to the responses implemented by the motion controllers. Due to their totally reactive nature, such reasoning systems can encounter problems such as infinite loops and limit cycles. In this paper, we proposed an approach to remedy these problems by adding a memory and memory-related behaviors to basic reactive systems. Three major types of memory behaviors are addressed: memory creation, memory management, and memory utilization. These are firstmore » presented, and examples of their implementation for the recognition of limit cycles during the navigation of an autonomous robot in a priori unknown environments are then discussed.« less

Using LDR as Sensing Element for an External Fuzzy Controller Applied in Photovoltaic Pumping Systems with Variable-Speed Drives.

PubMed

Maranhão, Geraldo Neves De A; Brito, Alaan Ubaiara; Leal, Anderson Marques; Fonseca, Jéssica Kelly Silva; Macêdo, Wilson Negrão

2015-09-22

In the present paper, a fuzzy controller applied to a Variable-Speed Drive (VSD) for use in Photovoltaic Pumping Systems (PVPS) is proposed. The fuzzy logic system (FLS) used is embedded in a microcontroller and corresponds to a proportional-derivative controller. A Light-Dependent Resistor (LDR) is used to measure, approximately, the irradiance incident on the PV array. Experimental tests are executed using an Arduino board. The experimental results show that the fuzzy controller is capable of operating the system continuously throughout the day and controlling the direct current (DC) voltage level in the VSD with a good performance.

Structure identification in fuzzy inference using reinforcement learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1993-01-01

In our previous work on the GARIC architecture, we have shown that the system can start with surface structure of the knowledge base (i.e., the linguistic expression of the rules) and learn the deep structure (i.e., the fuzzy membership functions of the labels used in the rules) by using reinforcement learning. Assuming the surface structure, GARIC refines the fuzzy membership functions used in the consequents of the rules using a gradient descent procedure. This hybrid fuzzy logic and reinforcement learning approach can learn to balance a cart-pole system and to backup a truck to its docking location after a few trials. In this paper, we discuss how to do structure identification using reinforcement learning in fuzzy inference systems. This involves identifying both surface as well as deep structure of the knowledge base. The term set of fuzzy linguistic labels used in describing the values of each control variable must be derived. In this process, splitting a label refers to creating new labels which are more granular than the original label and merging two labels creates a more general label. Splitting and merging of labels directly transform the structure of the action selection network used in GARIC by increasing or decreasing the number of hidden layer nodes.

Application of Fuzzy Logic to Matrix FMECA

NASA Astrophysics Data System (ADS)

Shankar, N. Ravi; Prabhu, B. S.

2001-04-01

A methodology combining the benefits of Fuzzy Logic and Matrix FMEA is presented in this paper. The presented methodology extends the risk prioritization beyond the conventional Risk Priority Number (RPN) method. Fuzzy logic is used to calculate the criticality rank. Also the matrix approach is improved further to develop a pictorial representation retaining all relevant qualitative and quantitative information of several FMEA elements relationships. The methodology presented is demonstrated by application to an illustrative example.

Fuzzy logic and image processing techniques for the interpretation of seismic data

NASA Astrophysics Data System (ADS)

Orozco-del-Castillo, M. G.; Ortiz-Alemán, C.; Urrutia-Fucugauchi, J.; Rodríguez-Castellanos, A.

2011-06-01

Since interpretation of seismic data is usually a tedious and repetitive task, the ability to do so automatically or semi-automatically has become an important objective of recent research. We believe that the vagueness and uncertainty in the interpretation process makes fuzzy logic an appropriate tool to deal with seismic data. In this work we developed a semi-automated fuzzy inference system to detect the internal architecture of a mass transport complex (MTC) in seismic images. We propose that the observed characteristics of a MTC can be expressed as fuzzy if-then rules consisting of linguistic values associated with fuzzy membership functions. The constructions of the fuzzy inference system and various image processing techniques are presented. We conclude that this is a well-suited problem for fuzzy logic since the application of the proposed methodology yields a semi-automatically interpreted MTC which closely resembles the MTC from expert manual interpretation.

Rocket engine system reliability analyses using probabilistic and fuzzy logic techniques

NASA Technical Reports Server (NTRS)

Hardy, Terry L.; Rapp, Douglas C.

1994-01-01

The reliability of rocket engine systems was analyzed by using probabilistic and fuzzy logic techniques. Fault trees were developed for integrated modular engine (IME) and discrete engine systems, and then were used with the two techniques to quantify reliability. The IRRAS (Integrated Reliability and Risk Analysis System) computer code, developed for the U.S. Nuclear Regulatory Commission, was used for the probabilistic analyses, and FUZZYFTA (Fuzzy Fault Tree Analysis), a code developed at NASA Lewis Research Center, was used for the fuzzy logic analyses. Although both techniques provided estimates of the reliability of the IME and discrete systems, probabilistic techniques emphasized uncertainty resulting from randomness in the system whereas fuzzy logic techniques emphasized uncertainty resulting from vagueness in the system. Because uncertainty can have both random and vague components, both techniques were found to be useful tools in the analysis of rocket engine system reliability.

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.

Fuzzy logic based sensor performance evaluation of vehicle mounted metal detector systems

NASA Astrophysics Data System (ADS)

Abeynayake, Canicious; Tran, Minh D.

2015-05-01

Vehicle Mounted Metal Detector (VMMD) systems are widely used for detection of threat objects in humanitarian demining and military route clearance scenarios. Due to the diverse nature of such operational conditions, operational use of VMMD without a proper understanding of its capability boundaries may lead to heavy causalities. Multi-criteria fitness evaluations are crucial for determining capability boundaries of any sensor-based demining equipment. Evaluation of sensor based military equipment is a multi-disciplinary topic combining the efforts of researchers, operators, managers and commanders having different professional backgrounds and knowledge profiles. Information acquired through field tests usually involves uncertainty, vagueness and imprecision due to variations in test and evaluation conditions during a single test or series of tests. This report presents a fuzzy logic based methodology for experimental data analysis and performance evaluation of VMMD. This data evaluation methodology has been developed to evaluate sensor performance by consolidating expert knowledge with experimental data. A case study is presented by implementing the proposed data analysis framework in a VMMD evaluation scenario. The results of this analysis confirm accuracy, practicability and reliability of the fuzzy logic based sensor performance evaluation framework.

ELIPS: Toward a Sensor Fusion Processor on a Chip

NASA Technical Reports Server (NTRS)

Daud, Taher; Stoica, Adrian; Tyson, Thomas; Li, Wei-te; Fabunmi, James

1998-01-01

The paper presents the concept and initial tests from the hardware implementation of a low-power, high-speed reconfigurable sensor fusion processor. The Extended Logic Intelligent Processing System (ELIPS) processor is developed to seamlessly combine rule-based systems, fuzzy logic, and neural networks to achieve parallel fusion of sensor in compact low power VLSI. The first demonstration of the ELIPS concept targets interceptor functionality; other applications, mainly in robotics and autonomous systems are considered for the future. The main assumption behind ELIPS is that fuzzy, rule-based and neural forms of computation can serve as the main primitives of an "intelligent" processor. Thus, in the same way classic processors are designed to optimize the hardware implementation of a set of fundamental operations, ELIPS is developed as an efficient implementation of computational intelligence primitives, and relies on a set of fuzzy set, fuzzy inference and neural modules, built in programmable analog hardware. The hardware programmability allows the processor to reconfigure into different machines, taking the most efficient hardware implementation during each phase of information processing. Following software demonstrations on several interceptor data, three important ELIPS building blocks (a fuzzy set preprocessor, a rule-based fuzzy system and a neural network) have been fabricated in analog VLSI hardware and demonstrated microsecond-processing times.

Fuzzy-logic based Q-Learning interference management algorithms in two-tier networks

NASA Astrophysics Data System (ADS)

Xu, Qiang; Xu, Zezhong; Li, Li; Zheng, Yan

2017-10-01

Unloading from macrocell network and enhancing coverage can be realized by deploying femtocells in the indoor scenario. However, the system performance of the two-tier network could be impaired by the co-tier and cross-tier interference. In this paper, a distributed resource allocation scheme is studied when each femtocell base station is self-governed and the resource cannot be assigned centrally through the gateway. A novel Q-Learning interference management scheme is proposed, that is divided into cooperative and independent part. In the cooperative algorithm, the interference information is exchanged between the cell-edge users which are classified by the fuzzy logic in the same cell. Meanwhile, we allocate the orthogonal subchannels to the high-rate cell-edge users to disperse the interference power when the data rate requirement is satisfied. The resource is assigned directly according to the minimum power principle in the independent algorithm. Simulation results are provided to demonstrate the significant performance improvements in terms of the average data rate, interference power and energy efficiency over the cutting-edge resource allocation algorithms.

Automated Robot Movement in the Mapped Area Using Fuzzy Logic for Wheel Chair Application

NASA Astrophysics Data System (ADS)

Siregar, B.; Efendi, S.; Ramadhana, H.; Andayani, U.; Fahmi, F.

2018-03-01

The difficulties of the disabled to move make them unable to live independently. People with disabilities need supporting device to move from place to place. For that, we proposed a solution that can help people with disabilities to move from one room to another automatically. This study aims to create a wheelchair prototype in the form of a wheeled robot as a means to learn the automatic mobilization. The fuzzy logic algorithm was used to determine motion direction based on initial position, ultrasonic sensors reading in avoiding obstacles, infrared sensors reading as a black line reader for the wheeled robot to move smooth and smartphone as a mobile controller. As a result, smartphones with the Android operating system can control the robot using Bluetooth. Here Bluetooth technology can be used to control the robot from a maximum distance of 15 meters. The proposed algorithm was able to work stable for automatic motion determination based on initial position, and also able to modernize the wheelchair movement from one room to another automatically.

Analysis of maizena drying system using temperature control based fuzzy logic method

NASA Astrophysics Data System (ADS)

Arief, Ulfah Mediaty; Nugroho, Fajar; Purbawanto, Sugeng; Setyaningsih, Dyah Nurani; Suryono

2018-03-01

Corn is one of the rice subtitution food that has good potential. Corn can be processed to be a maizena, and it can be used to make type of food that has been made from maizena, viz. Brownies cake, egg roll, and other cookies. Generally, maizena obtained by drying process carried out 2-3 days under the sun. However, drying process not possible during the rainy season. This drying process can be done using an automatic drying tool. This study was to analyze the design result and manufacture of maizena drying system with temperature control based fuzzylogic method. The result show that temperature of drying system with set point 40°C - 60°C work in suitable condition. The level of water content in 15% (BSN) and temperatureat 50°C included in good drying process. Time required to reach the set point of temperature in 50°C is 7.05 minutes. Drying time for 500 gr samples with temperature 50°C and power capacity 127.6 watt was 1 hour. Based on the result, drying process using temperature control based fuzzy logic method can improve energy efficiency than the conventional method of drying using a direct sunlight source with a temperature that cannot be directly controlled by human being causing the quality of drying result of flour is erratic.

An analytical fuzzy-based approach to ?-gain optimal control of input-affine nonlinear systems using Newton-type algorithm

NASA Astrophysics Data System (ADS)

Milic, Vladimir; Kasac, Josip; Novakovic, Branko

2015-10-01

This paper is concerned with ?-gain optimisation of input-affine nonlinear systems controlled by analytic fuzzy logic system. Unlike the conventional fuzzy-based strategies, the non-conventional analytic fuzzy control method does not require an explicit fuzzy rule base. As the first contribution of this paper, we prove, by using the Stone-Weierstrass theorem, that the proposed fuzzy system without rule base is universal approximator. The second contribution of this paper is an algorithm for solving a finite-horizon minimax problem for ?-gain optimisation. The proposed algorithm consists of recursive chain rule for first- and second-order derivatives, Newton's method, multi-step Adams method and automatic differentiation. Finally, the results of this paper are evaluated on a second-order nonlinear system.

Prediction system of hydroponic plant growth and development using algorithm Fuzzy Mamdani method

NASA Astrophysics Data System (ADS)

Sudana, I. Made; Purnawirawan, Okta; Arief, Ulfa Mediaty

2017-03-01

Hydroponics is a method of farming without soil. One of the Hydroponic plants is Watercress (Nasturtium Officinale). The development and growth process of hydroponic Watercress was influenced by levels of nutrients, acidity and temperature. The independent variables can be used as input variable system to predict the value level of plants growth and development. The prediction system is using Fuzzy Algorithm Mamdani method. This system was built to implement the function of Fuzzy Inference System (Fuzzy Inference System/FIS) as a part of the Fuzzy Logic Toolbox (FLT) by using MATLAB R2007b. FIS is a computing system that works on the principle of fuzzy reasoning which is similar to humans' reasoning. Basically FIS consists of four units which are fuzzification unit, fuzzy logic reasoning unit, base knowledge unit and defuzzification unit. In addition to know the effect of independent variables on the plants growth and development that can be visualized with the function diagram of FIS output surface that is shaped three-dimensional, and statistical tests based on the data from the prediction system using multiple linear regression method, which includes multiple linear regression analysis, T test, F test, the coefficient of determination and donations predictor that are calculated using SPSS (Statistical Product and Service Solutions) software applications.

WARP: Weight Associative Rule Processor. A dedicated VLSI fuzzy logic megacell

NASA Technical Reports Server (NTRS)

Pagni, A.; Poluzzi, R.; Rizzotto, G. G.

1992-01-01

During the last five years Fuzzy Logic has gained enormous popularity in the academic and industrial worlds. The success of this new methodology has led the microelectronics industry to create a new class of machines, called Fuzzy Machines, to overcome the limitations of traditional computing systems when utilized as Fuzzy Systems. This paper gives an overview of the methods by which Fuzzy Logic data structures are represented in the machines (each with its own advantages and inefficiencies). Next, the paper introduces WARP (Weight Associative Rule Processor) which is a dedicated VLSI megacell allowing the realization of a fuzzy controller suitable for a wide range of applications. WARP represents an innovative approach to VLSI Fuzzy controllers by utilizing different types of data structures for characterizing the membership functions during the various stages of the Fuzzy processing. WARP dedicated architecture has been designed in order to achieve high performance by exploiting the computational advantages offered by the different data representations.

Development of a solar-powered electric bicycle in bike sharing transportation system

NASA Astrophysics Data System (ADS)

Adhisuwignjo, S.; Siradjuddin, I.; Rifa'i, M.; Putri, R. I.

2017-06-01

The increasing mobility has directly led to deteriorating traffic conditions, extra fuel consumption, increasing automobile exhaust emissions, air pollution and lowering quality of life. Apart from being clean, cheap and equitable mode of transport for short-distance journeys, cycling can potentially offer solutions to the problem of urban mobility. Many cities have tried promoting cycling particularly through the implementation of bike-sharing. Apparently the fourth generation bikesharing system has been promoted utilizing electric bicycles which considered as a clean technology implementation. Utilization of solar power is probably the development keys in the fourth generation bikesharing system and will become the standard in bikesharing system in the future. Electric bikes use batteries as a source of energy, thus they require a battery charger system which powered from the solar cells energy. This research aims to design and implement electric bicycle battery charging system with solar energy sources using fuzzy logic algorithm. It is necessary to develop an electric bicycle battery charging system with solar energy sources using fuzzy logic algorithm. The study was conducted by means of experimental method which includes the design, manufacture and testing controller systems. The designed fuzzy algorithm have been planted in EEPROM microcontroller ATmega8535. The charging current was set at 1.2 Amperes and the full charged battery voltage was observed to be 40 Volts. The results showed a fuzzy logic controller was able to maintain the charging current of 1.2 Ampere with an error rate of less than 5% around the set point. The process of charging electric bike lead acid batteries from empty to fully charged was 5 hours. In conclusion, the development of solar-powered electric bicycle controlled using fuzzy logic controller can keep the battery charging current in solar-powered electric bicycle to remain stable. This shows that the fuzzy algorithm can be used as a controller in the process of charging for a solar electric bicycle.

Co-evolutionary data mining for fuzzy rules: automatic fitness function creation phase space, and experiments

NASA Astrophysics Data System (ADS)

Smith, James F., III; Blank, Joseph A.

2003-03-01

An approach is being explored that involves embedding a fuzzy logic based resource manager in an electronic game environment. Game agents can function under their own autonomous logic or human control. This approach automates the data mining problem. The game automatically creates a cleansed database reflecting the domain expert's knowledge, it calls a data mining function, a genetic algorithm, for data mining of the data base as required and allows easy evaluation of the information extracted. The co-evolutionary fitness functions, chromosomes and stopping criteria for ending the game are discussed. Genetic algorithm and genetic program based data mining procedures are discussed that automatically discover new fuzzy rules and strategies. The strategy tree concept and its relationship to co-evolutionary data mining are examined as well as the associated phase space representation of fuzzy concepts. The overlap of fuzzy concepts in phase space reduces the effective strategies available to adversaries. Co-evolutionary data mining alters the geometric properties of the overlap region known as the admissible region of phase space significantly enhancing the performance of the resource manager. Procedures for validation of the information data mined are discussed and significant experimental results provided.

A fuzzy logic approach to modeling the underground economy in Taiwan

NASA Astrophysics Data System (ADS)

Yu, Tiffany Hui-Kuang; Wang, David Han-Min; Chen, Su-Jane

2006-04-01

The size of the ‘underground economy’ (UE) is valuable information in the formulation of macroeconomic and fiscal policy. This study applies fuzzy set theory and fuzzy logic to model Taiwan's UE over the period from 1960 to 2003. Two major factors affecting the size of the UE, the effective tax rate and the degree of government regulation, are used. The size of Taiwan's UE is scaled and compared with those of other models. Although our approach yields different estimates, similar patterns and leading are exhibited throughout the period. The advantage of applying fuzzy logic is twofold. First, it can avoid the complex calculations in conventional econometric models. Second, fuzzy rules with linguistic terms are easy for human to understand.

Fuzzy Current-Mode Control and Stability Analysis

NASA Technical Reports Server (NTRS)

Kopasakis, George

2000-01-01

In this paper a current-mode control (CMC) methodology is developed for a buck converter by using a fuzzy logic controller. Conventional CMC methodologies are based on lead-lag compensation with voltage and inductor current feedback. In this paper the converter lead-lag compensation will be substituted with a fuzzy controller. A small-signal model of the fuzzy controller will also be developed in order to examine the stability properties of this buck converter control system. The paper develops an analytical approach, introducing fuzzy control into the area of CMC.

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-based assessment of groundwater intrinsic vulnerability of a volcanic aquifer in the Chilean Andean Valley.

PubMed

Duhalde, Denisse J; Arumí, José L; Oyarzún, Ricardo A; Rivera, Diego A

2018-06-11

A fuzzy logic approach has been proposed to face the uncertainty caused by sparse data in the assessment of the intrinsic vulnerability of a groundwater system with parametric methods in Las Trancas Valley, Andean Mountain, south-central Chile, a popular touristic place in Chile, but lacking of a centralized drinking and sewage water public systems; this situation is a potentially source of groundwater pollution. Based on DRASTIC, GOD, and EKv and the expert knowledge of the study area, the Mamdani fuzzy approach was generated and the spatial data were processed by ArcGIS. The groundwater system exhibited areas with high, medium, and low intrinsic vulnerability indices. The fuzzy approach results were compared with traditional methods results, which, in general, have shown a good spatial agreement even though significant changes were also identified in the spatial distribution of the indices. The Mamdani logic approach has shown to be a useful and practical tool to assess the intrinsic vulnerability of an aquifer under sparse data conditions.

Teaching Machines to Think Fuzzy

ERIC Educational Resources Information Center

Technology Teacher, 2004

2004-01-01

Fuzzy logic programs for computers make them more human. Computers can then think through messy situations and make smart decisions. It makes computers able to control things the way people do. Fuzzy logic has been used to control subway trains, elevators, washing machines, microwave ovens, and cars. Pretty much all the human has to do is push one…

Quality Improvement of Liver Ultrasound Images Using Fuzzy Techniques.

PubMed

Bayani, Azadeh; Langarizadeh, Mostafa; Radmard, Amir Reza; Nejad, Ahmadreza Farzaneh

2016-12-01

Liver ultrasound images are so common and are applied so often to diagnose diffuse liver diseases like fatty liver. However, the low quality of such images makes it difficult to analyze them and diagnose diseases. The purpose of this study, therefore, is to improve the contrast and quality of liver ultrasound images. In this study, a number of image contrast enhancement algorithms which are based on fuzzy logic were applied to liver ultrasound images - in which the view of kidney is observable - using Matlab2013b to improve the image contrast and quality which has a fuzzy definition; just like image contrast improvement algorithms using a fuzzy intensification operator, contrast improvement algorithms applying fuzzy image histogram hyperbolization, and contrast improvement algorithms by fuzzy IF-THEN rules. With the measurement of Mean Squared Error and Peak Signal to Noise Ratio obtained from different images, fuzzy methods provided better results, and their implementation - compared with histogram equalization method - led both to the improvement of contrast and visual quality of images and to the improvement of liver segmentation algorithms results in images. Comparison of the four algorithms revealed the power of fuzzy logic in improving image contrast compared with traditional image processing algorithms. Moreover, contrast improvement algorithm based on a fuzzy intensification operator was selected as the strongest algorithm considering the measured indicators. This method can also be used in future studies on other ultrasound images for quality improvement and other image processing and analysis applications.

Quality Improvement of Liver Ultrasound Images Using Fuzzy Techniques

PubMed Central

Bayani, Azadeh; Langarizadeh, Mostafa; Radmard, Amir Reza; Nejad, Ahmadreza Farzaneh

2016-01-01

Background: Liver ultrasound images are so common and are applied so often to diagnose diffuse liver diseases like fatty liver. However, the low quality of such images makes it difficult to analyze them and diagnose diseases. The purpose of this study, therefore, is to improve the contrast and quality of liver ultrasound images. Methods: In this study, a number of image contrast enhancement algorithms which are based on fuzzy logic were applied to liver ultrasound images - in which the view of kidney is observable - using Matlab2013b to improve the image contrast and quality which has a fuzzy definition; just like image contrast improvement algorithms using a fuzzy intensification operator, contrast improvement algorithms applying fuzzy image histogram hyperbolization, and contrast improvement algorithms by fuzzy IF-THEN rules. Results: With the measurement of Mean Squared Error and Peak Signal to Noise Ratio obtained from different images, fuzzy methods provided better results, and their implementation - compared with histogram equalization method - led both to the improvement of contrast and visual quality of images and to the improvement of liver segmentation algorithms results in images. Conclusion: Comparison of the four algorithms revealed the power of fuzzy logic in improving image contrast compared with traditional image processing algorithms. Moreover, contrast improvement algorithm based on a fuzzy intensification operator was selected as the strongest algorithm considering the measured indicators. This method can also be used in future studies on other ultrasound images for quality improvement and other image processing and analysis applications. PMID:28077898

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 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.

Fault tolerant control based on interval type-2 fuzzy sliding mode controller for coaxial trirotor aircraft.

PubMed

Zeghlache, Samir; Kara, Kamel; Saigaa, Djamel

2015-11-01

In this paper, a robust controller for a Six Degrees of Freedom (6 DOF) coaxial trirotor helicopter control is proposed in presence of defects in the system. A control strategy based on the coupling of the interval type-2 fuzzy logic control and sliding mode control technique are used to design a controller. The main purpose of this work is to eliminate the chattering phenomenon and guaranteeing the stability and the robustness of the system. In order to achieve this goal, interval type-2 fuzzy logic control has been used to generate the discontinuous control signal. The simulation results have shown that the proposed control strategy can greatly alleviate the chattering effect, and perform good reference tracking in presence of defects in the system. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Knowledge discovery through games and game theory

NASA Astrophysics Data System (ADS)

Smith, James F., III; Rhyne, Robert D.

2001-03-01

A fuzzy logic based expert system has been developed that automatically allocates electronic attack (EA) resources in real-time over many dissimilar platforms. The platforms can be very general, e.g., ships, planes, robots, land based facilities, etc. Potential foes the platforms deal with can also be general. The initial version of the algorithm was optimized using a genetic algorithm employing fitness functions constructed based on expertise. A new approach is being explored that involves embedding the resource manager in a electronic game environment. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge, it calls a data mining function, a genetic algorithm, for data mining of the database as required. The game allows easy evaluation of the information mined in the second step. The measure of effectiveness (MOE) for re-optimization is discussed. The mined information is extremely valuable as shown through demanding scenarios.

Fuzzy Hybrid Deliberative/Reactive Paradigm (FHDRP)

NASA Technical Reports Server (NTRS)

Sarmadi, Hengameth

2004-01-01

This work aims to introduce a new concept for incorporating fuzzy sets in hybrid deliberative/reactive paradigm. After a brief review on basic issues of hybrid paradigm the definition of agent-based fuzzy hybrid paradigm, which enables the agents to proceed and extract their behavior through quantitative numerical and qualitative knowledge and to impose their decision making procedure via fuzzy rule bank, is discussed. Next an example performs a more applied platform for the developed approach and finally an overview of the corresponding agents architecture enhances agents logical framework.

Fuzzy expert systems using CLIPS

NASA Technical Reports Server (NTRS)

Le, Thach C.

1994-01-01

This paper describes a CLIPS-based fuzzy expert system development environment called FCLIPS and illustrates its application to the simulated cart-pole balancing problem. FCLIPS is a straightforward extension of CLIPS without any alteration to the CLIPS internal structures. It makes use of the object-oriented and module features in CLIPS version 6.0 for the implementation of fuzzy logic concepts. Systems of varying degrees of mixed Boolean and fuzzy rules can be implemented in CLIPS. Design and implementation issues of FCLIPS will also be discussed.

A Priority Fuzzy Logic Extension of the XQuery Language

NASA Astrophysics Data System (ADS)

Škrbić, Srdjan; Wettayaprasit, Wiphada; Saeueng, Pannipa

2011-09-01

In recent years there have been significant research findings in flexible XML querying techniques using fuzzy set theory. Many types of fuzzy extensions to XML data model and XML query languages have been proposed. In this paper, we introduce priority fuzzy logic extensions to XQuery language. Describing these extensions we introduce a new query language. Moreover, we describe a way to implement an interpreter for this language using an existing XML native database.

Using LDR as Sensing Element for an External Fuzzy Controller Applied in Photovoltaic Pumping Systems with Variable-Speed Drives

PubMed Central

Maranhão, Geraldo Neves De A.; Brito, Alaan Ubaiara; Leal, Anderson Marques; Fonseca, Jéssica Kelly Silva; Macêdo, Wilson Negrão

2015-01-01

In the present paper, a fuzzy controller applied to a Variable-Speed Drive (VSD) for use in Photovoltaic Pumping Systems (PVPS) is proposed. The fuzzy logic system (FLS) used is embedded in a microcontroller and corresponds to a proportional-derivative controller. A Light-Dependent Resistor (LDR) is used to measure, approximately, the irradiance incident on the PV array. Experimental tests are executed using an Arduino board. The experimental results show that the fuzzy controller is capable of operating the system continuously throughout the day and controlling the direct current (DC) voltage level in the VSD with a good performance. PMID:26402688

A Fuzzy Query Mechanism for Human Resource Websites

NASA Astrophysics Data System (ADS)

Lai, Lien-Fu; Wu, Chao-Chin; Huang, Liang-Tsung; Kuo, Jung-Chih

Users' preferences often contain imprecision and uncertainty that are difficult for traditional human resource websites to deal with. In this paper, we apply the fuzzy logic theory to develop a fuzzy query mechanism for human resource websites. First, a storing mechanism is proposed to store fuzzy data into conventional database management systems without modifying DBMS models. Second, a fuzzy query language is proposed for users to make fuzzy queries on fuzzy databases. User's fuzzy requirement can be expressed by a fuzzy query which consists of a set of fuzzy conditions. Third, each fuzzy condition associates with a fuzzy importance to differentiate between fuzzy conditions according to their degrees of importance. Fourth, the fuzzy weighted average is utilized to aggregate all fuzzy conditions based on their degrees of importance and degrees of matching. Through the mutual compensation of all fuzzy conditions, the ordering of query results can be obtained according to user's preference.

Creating Clinical Fuzzy Automata with Fuzzy Arden Syntax.

PubMed

de Bruin, Jeroen S; Steltzer, Heinz; Rappelsberger, Andrea; Adlassnig, Klaus-Peter

2017-01-01

Formal constructs for fuzzy sets and fuzzy logic are incorporated into Arden Syntax version 2.9 (Fuzzy Arden Syntax). With fuzzy sets, the relationships between measured or observed data and linguistic terms are expressed as degrees of compatibility that model the unsharpness of the boundaries of linguistic terms. Propositional uncertainty due to incomplete knowledge of relationships between clinical linguistic concepts is modeled with fuzzy logic. Fuzzy Arden Syntax also supports the construction of fuzzy state monitors. The latter are defined as monitors that employ fuzzy automata to observe gradual transitions between different stages of disease. As a use case, we re-implemented FuzzyARDS, a previously published clinical monitoring system for patients suffering from acute respiratory distress syndrome (ARDS). Using the re-implementation as an example, we show how key concepts of fuzzy automata, i.e., fuzzy states and parallel fuzzy state transitions, can be implemented in Fuzzy Arden Syntax. The results showed that fuzzy state monitors can be implemented in a straightforward manner.

Fuzzy Behavior Modulation with Threshold Activation for Autonomous Vehicle Navigation

NASA Technical Reports Server (NTRS)

Tunstel, Edward

2000-01-01

This paper describes fuzzy logic techniques used in a hierarchical behavior-based architecture for robot navigation. An architectural feature for threshold activation of fuzzy-behaviors is emphasized, which is potentially useful for tuning navigation performance in real world applications. The target application is autonomous local navigation of a small planetary rover. Threshold activation of low-level navigation behaviors is the primary focus. A preliminary assessment of its impact on local navigation performance is provided based on computer simulations.

Distributed autonomous systems: resource management, planning, and control algorithms

NASA Astrophysics Data System (ADS)

Smith, James F., III; Nguyen, ThanhVu H.

2005-05-01

Distributed autonomous systems, i.e., systems that have separated distributed components, each of which, exhibit some degree of autonomy are increasingly providing solutions to naval and other DoD problems. Recently developed control, planning and resource allocation algorithms for two types of distributed autonomous systems will be discussed. The first distributed autonomous system (DAS) to be discussed consists of a collection of unmanned aerial vehicles (UAVs) that are under fuzzy logic control. The UAVs fly and conduct meteorological sampling in a coordinated fashion determined by their fuzzy logic controllers to determine the atmospheric index of refraction. Once in flight no human intervention is required. A fuzzy planning algorithm determines the optimal trajectory, sampling rate and pattern for the UAVs and an interferometer platform while taking into account risk, reliability, priority for sampling in certain regions, fuel limitations, mission cost, and related uncertainties. The real-time fuzzy control algorithm running on each UAV will give the UAV limited autonomy allowing it to change course immediately without consulting with any commander, request other UAVs to help it, alter its sampling pattern and rate when observing interesting phenomena, or to terminate the mission and return to base. The algorithms developed will be compared to a resource manager (RM) developed for another DAS problem related to electronic attack (EA). This RM is based on fuzzy logic and optimized by evolutionary algorithms. It allows a group of dissimilar platforms to use EA resources distributed throughout the group. For both DAS types significant theoretical and simulation results will be presented.

Trans-skull ultrasonic Doppler system aided by fuzzy logic

NASA Astrophysics Data System (ADS)

Hata, Yutaka; Nakamura, Masato; Yagi, Naomi; Ishikawa, Tomomoto

2012-06-01

This paper describes a trans-skull ultrasonic Doppler system for measuring the blood flow direction in brain under skull. In this system, we use an ultrasonic array probe with the center frequency of 1.0 MHz. The system determines the fuzzy degree of blood flow by Doppler Effect, thereby it locates blood vessel. This Doppler Effect is examined by the center of gravity shift of the frequency magnitudes. In in-vitro experiment, a cow bone was employed as the skull, and three silicon tubes were done as blood vessels, and bubble in water as blood. We received the ultrasonic waves through a protein, the skull and silicon tubes in order. In the system, fuzzy degrees are determined with respect to the Doppler shift, amplitude of the waves and attenuation of the tissues. The fuzzy degrees of bone and blood direction are calculated by them. The experimental results showed that the system successfully visualized the skull and flow direction, compared with the location and flow direction of the phantom. Thus, it detected the flow direction by Doppler Effect under skull, and automatically extracted the region of skull and blood vessel.

Obtaining ABET Student Outcome Satisfaction from Course Learning Outcome Data Using Fuzzy Logic

ERIC Educational Resources Information Center

Imam, Muhammad Hasan; Tasadduq, Imran Ali; Ahmad, Abdul-Rahim; Aldosari, Fahd

2017-01-01

One of the approaches for obtaining the satisfaction data for ABET "Student Outcomes" (SOs) is to transform Course Learning Outcomes (CLOs) satisfaction data obtained through assessment of CLOs to SO satisfaction data. Considering the fuzzy nature of metrics of CLOs and SOs, a Fuzzy Logic algorithm has been proposed to extract SO…

Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach.

PubMed

Julie, E Golden; Selvi, S Tamil

2016-01-01

Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.

Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach

PubMed Central

Julie, E. Golden; Selvi, S. Tamil

2016-01-01

Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes. PMID:26881269

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 modeling of high performance rechargeable batteries

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

Singh, P.; Fennie, C. Jr.; Reisner, D.E.

1998-07-01

Accurate battery state-of-charge (SOC) measurements are critical in many portable electronic device applications. Yet conventional techniques for battery SOC estimation are limited in their accuracy, reliability, and flexibility. In this paper the authors present a powerful new approach to estimate battery SOC using a fuzzy logic-based methodology. This approach provides a universally applicable, accurate method for battery SOC estimation either integrated within, or as an external monitor to, an electronic device. The methodology is demonstrated in modeling impedance measurements on Ni-MH cells and discharge voltage curves of Li-ion cells.

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.

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.

A fuzzy-logic antiswing controller for three-dimensional overhead cranes.

PubMed

Cho, Sung-Kun; Lee, Ho-Hoon

2002-04-01

In this paper, a new fuzzy antiswing control scheme is proposed for a three-dimensional overhead crane. The proposed control consists of a position servo control and a fuzzy-logic control. The position servo control is used to control crane position and rope length, and the fuzzy-logic control is used to suppress load swing. The proposed control guarantees not only prompt suppression of load swing but also accurate control of crane position and rope length for simultaneous travel, traverse, and hoisting motions of the crane. Furthermore, the proposed control provides practical gain tuning criteria for easy application. The effectiveness of the proposed control is shown by experiments with a three-dimensional prototype overhead crane.

Laser-Beam-Alignment Controller

NASA Technical Reports Server (NTRS)

Krasowski, M. J.; Dickens, D. E.

1995-01-01

In laser-beam-alignment controller, images from video camera compared to reference patterns by fuzzy-logic pattern comparator. Results processed by fuzzy-logic microcontroller, which sends control signals to motor driver adjusting lens and pinhole in spatial filter.

Dispositional logic

NASA Technical Reports Server (NTRS)

Le Balleur, J. C.

1988-01-01

The applicability of conventional mathematical analysis (based on the combination of two-valued logic and probability theory) to problems in which human judgment, perception, or emotions play significant roles is considered theoretically. It is shown that dispositional logic, a branch of fuzzy logic, has particular relevance to the common-sense reasoning typical of human decision-making. The concepts of dispositionality and usuality are defined analytically, and a dispositional conjunctive rule and dispositional modus ponens are derived.

Using fuzzy fractal features of digital images for the material surface analisys

NASA Astrophysics Data System (ADS)

Privezentsev, D. G.; Zhiznyakov, A. L.; Astafiev, A. V.; Pugin, E. V.

2018-01-01

Edge detection is an important task in image processing. There are a lot of approaches in this area: Sobel, Canny operators and others. One of the perspective techniques in image processing is the use of fuzzy logic and fuzzy sets theory. They allow us to increase processing quality by representing information in its fuzzy form. Most of the existing fuzzy image processing methods switch to fuzzy sets on very late stages, so this leads to some useful information loss. In this paper, a novel method of edge detection based on fuzzy image representation and fuzzy pixels is proposed. With this approach, we convert the image to fuzzy form on the first step. Different approaches to this conversion are described. Several membership functions for fuzzy pixel description and requirements for their form and view are given. A novel approach to edge detection based on Sobel operator and fuzzy image representation is proposed. Experimental testing of developed method was performed on remote sensing images.

Feedback error learning control of magnetic satellites using type-2 fuzzy neural networks with elliptic membership functions.

PubMed

Khanesar, Mojtaba Ahmadieh; Kayacan, Erdal; Reyhanoglu, Mahmut; Kaynak, Okyay

2015-04-01

A novel type-2 fuzzy membership function (MF) in the form of an ellipse has recently been proposed in literature, the parameters of which that represent uncertainties are de-coupled from its parameters that determine the center and the support. This property has enabled the proposers to make an analytical comparison of the noise rejection capabilities of type-1 fuzzy logic systems with its type-2 counterparts. In this paper, a sliding mode control theory-based learning algorithm is proposed for an interval type-2 fuzzy logic system which benefits from elliptic type-2 fuzzy MFs. The learning is based on the feedback error learning method and not only the stability of the learning is proved but also the stability of the overall system is shown by adding an additional component to the control scheme to ensure robustness. In order to test the efficiency and efficacy of the proposed learning and the control algorithm, the trajectory tracking problem of a magnetic rigid spacecraft is studied. The simulations results show that the proposed control algorithm gives better performance results in terms of a smaller steady state error and a faster transient response as compared to conventional control algorithms.

FuzzObserver

NASA Technical Reports Server (NTRS)

Howard, Ayanna; Bayard, David

2006-01-01

Fuzzy Feature Observation Planner for Small Body Proximity Observations (FuzzObserver) is a developmental computer program, to be used along with other software, for autonomous planning of maneuvers of a spacecraft near an asteroid, comet, or other small astronomical body. Selection of terrain features and estimation of the position of the spacecraft relative to these features is an essential part of such planning. FuzzObserver contributes to the selection and estimation by generating recommendations for spacecraft trajectory adjustments to maintain the spacecraft's ability to observe sufficient terrain features for estimating position. The input to FuzzObserver consists of data from terrain images, including sets of data on features acquired during descent toward, or traversal of, a body of interest. The name of this program reflects its use of fuzzy logic to reason about the terrain features represented by the data and extract corresponding trajectory-adjustment rules. Linguistic fuzzy sets and conditional statements enable fuzzy systems to make decisions based on heuristic rule-based knowledge derived by engineering experts. A major advantage of using fuzzy logic is that it involves simple arithmetic calculations that can be performed rapidly enough to be useful for planning within the short times typically available for spacecraft maneuvers.

Nonlinear Aerodynamic Modeling From Flight Data Using Advanced Piloted Maneuvers and Fuzzy Logic

NASA Technical Reports Server (NTRS)

Brandon, Jay M.; Morelli, Eugene A.

2012-01-01

Results of the Aeronautics Research Mission Directorate Seedling Project Phase I research project entitled "Nonlinear Aerodynamics Modeling using Fuzzy Logic" are presented. Efficient and rapid flight test capabilities were developed for estimating highly nonlinear models of airplane aerodynamics over a large flight envelope. Results showed that the flight maneuvers developed, used in conjunction with the fuzzy-logic system identification algorithms, produced very good model fits of the data, with no model structure inputs required, for flight conditions ranging from cruise to departure and spin conditions.

The semantics of fuzzy logic

NASA Technical Reports Server (NTRS)

Ruspini, Enrique H.

1991-01-01

Summarized here are the results of recent research on the conceptual foundations of fuzzy logic. The focus is primarily on the principle characteristics of a model that quantifies resemblance between possible worlds by means of a similarity function that assigns a number between 0 and 1 to every pair of possible worlds. Introduction of such a function permits one to interpret the major constructs and methods of fuzzy logic: conditional and unconditional possibility and necessity distributions and the generalized modus ponens of Zadeh on the basis of related metric relationships between subsets of possible worlds.

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.

An Interval Type-2 Fuzzy Multiple Echelon Supply Chain Model

NASA Astrophysics Data System (ADS)

Miller, Simon; John, Robert

Planning resources for a supply chain is a major factor determining its success or failure. In this paper we build on previous work introducing an Interval Type-2 Fuzzy Logic model of a multiple echelon supply chain. It is believed that the additional degree of uncertainty provided by Interval Type-2 Fuzzy Logic will allow for better representation of the uncertainty and vagueness present in resource planning models. First, the subject of Supply Chain Management is introduced, then some background is given on related work using Type-1 Fuzzy Logic. A description of the Interval Type-2 Fuzzy model is given, and a test scenario detailed. A Genetic Algorithm uses the model to search for a near-optimal plan for the scenario. A discussion of the results follows, along with conclusions and details of intended further work.

Neural Modeling of Fuzzy Controllers for Maximum Power Point Tracking in Photovoltaic Energy Systems

NASA Astrophysics Data System (ADS)

Lopez-Guede, Jose Manuel; Ramos-Hernanz, Josean; Altın, Necmi; Ozdemir, Saban; Kurt, Erol; Azkune, Gorka

2018-06-01

One field in which electronic materials have an important role is energy generation, especially within the scope of photovoltaic energy. This paper deals with one of the most relevant enabling technologies within that scope, i.e, the algorithms for maximum power point tracking implemented in the direct current to direct current converters and its modeling through artificial neural networks (ANNs). More specifically, as a proof of concept, we have addressed the problem of modeling a fuzzy logic controller that has shown its performance in previous works, and more specifically the dimensionless duty cycle signal that controls a quadratic boost converter. We achieved a very accurate model since the obtained medium squared error is 3.47 × 10-6, the maximum error is 16.32 × 10-3 and the regression coefficient R is 0.99992, all for the test dataset. This neural implementation has obvious advantages such as a higher fault tolerance and a simpler implementation, dispensing with all the complex elements needed to run a fuzzy controller (fuzzifier, defuzzifier, inference engine and knowledge base) because, ultimately, ANNs are sums and products.

Implementation of fuzzy-sliding mode based control of a grid connected photovoltaic system.

PubMed

Menadi, Abdelkrim; Abdeddaim, Sabrina; Ghamri, Ahmed; Betka, Achour

2015-09-01

The present work describes an optimal operation of a small scale photovoltaic system connected to a micro-grid, based on both sliding mode and fuzzy logic control. Real time implementation is done through a dSPACE 1104 single board, controlling a boost chopper on the PV array side and a voltage source inverter (VSI) on the grid side. The sliding mode controller tracks permanently the maximum power of the PV array regardless of atmospheric condition variations, while The fuzzy logic controller (FLC) regulates the DC-link voltage, and ensures via current control of the VSI a quasi-total transit of the extracted PV power to the grid under a unity power factor operation. Simulation results, carried out via Matlab-Simulink package were approved through experiment, showing the effectiveness of the proposed control techniques. Copyright © 2015. Published by Elsevier Ltd.

Tuberculosis disease diagnosis using artificial immune recognition system.

PubMed

Shamshirband, Shahaboddin; Hessam, Somayeh; Javidnia, Hossein; Amiribesheli, Mohsen; Vahdat, Shaghayegh; Petković, Dalibor; Gani, Abdullah; Kiah, Miss Laiha Mat

2014-01-01

There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. This study is aimed at diagnosing TB using hybrid machine learning approaches. Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm. Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.

Fuzzy – PI controller to control the velocity parameter of Induction Motor

NASA Astrophysics Data System (ADS)

Malathy, R.; Balaji, V.

2018-04-01

The major application of Induction motor includes the usage of the same in industries because of its high robustness, reliability, low cost, highefficiency and good self-starting capability. Even though it has the above mentioned advantages, it also have some limitations: (1) the standard motor is not a true constant-speed machine, itsfull-load slip varies less than 1 % (in high-horsepower motors).And (2) it is not inherently capable of providing variable-speedoperation. In order to solve the above mentioned problem smart motor controls and variable speed controllers are used. Motor applications involve non linearity features, which can be controlled by Fuzzy logic controller as it is capable of handling those features with high efficiency and it act similar to human operator. This paper presents individuality of the plant modelling. The fuzzy logic controller (FLC)trusts on a set of linguistic if-then rules, a rule-based Mamdani for closed loop Induction Motor model. Themotor model is designed and membership functions are chosenaccording to the parameters of the motor model. Simulation results contains non linearity in induction motor model. A conventional PI controller iscompared practically to fuzzy logic controller using Simulink.

Mamdani Fuzzy System for Indoor Autonomous Mobile Robot

NASA Astrophysics Data System (ADS)

Khan, M. K. A. Ahamed; Rashid, Razif; Elamvazuthi, I.

2011-06-01

Several control algorithms for autonomous mobile robot navigation have been proposed in the literature. Recently, the employment of non-analytical methods of computing such as fuzzy logic, evolutionary computation, and neural networks has demonstrated the utility and potential of these paradigms for intelligent control of mobile robot navigation. In this paper, Mamdani fuzzy system for an autonomous mobile robot is developed. The paper begins with the discussion on the conventional controller and then followed by the description of fuzzy logic controller in detail.

Robust Fuzzy Logic Stabilization with Disturbance Elimination

PubMed Central

Danapalasingam, Kumeresan A.

2014-01-01

A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic controller that addresses unknown model uncertainties and an unmeasured disturbance. Since it is challenging to derive a highly accurate mathematical model, the proposed controller requires only nominal functions of a control system. In this paper, a mathematical derivation is carried out to prove that the controller is able to achieve asymptotic stability by processing state measurements. Robustness here refers to the ability of the controller to asymptotically steer the state vector towards the origin in the presence of model uncertainties and a disturbance input. Simulation results of the robust fuzzy logic controller application in a magnetic levitation system demonstrate the feasibility of the control design. PMID:25177713

Formation of an internal model of environment dynamics during upper limb reaching movements: a fuzzy approach.

PubMed

MacDonald, Chad; Moussavi, Zahra; Sarkodie-Gyan, Thompson

2007-01-01

This paper presents the development and simulation of a fuzzy logic based learning mechanism to emulate human motor learning. In particular, fuzzy inference was used to develop an internal model of a novel dynamic environment experienced during planar reaching movements with the upper limb. A dynamic model of the human arm was developed and a fuzzy if-then rule base was created to relate trajectory movement and velocity errors to internal model update parameters. An experimental simulation was performed to compare the fuzzy system's performance with that of human subjects. It was found that the dynamic model behaved as expected, and the fuzzy learning mechanism created an internal model that was capable of opposing the environmental force field to regain a trajectory closely resembling the desired ideal.

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.

The knowledge model of MedFrame/CADIAG-IV.

PubMed

Sageder, B; Boegl, K; Adlassnig, K P; Kolousek, G; Trummer, B

1997-01-01

The medical consultation system MedFrame/CADIAG-IV is a successor of the prior CADIAG projects. It is the result of a complete redesign to account for today's demands on state-of-the-art software. Its knowledge representation and inference process are based on fuzzy set theory and fuzzy logic. Fuzzy sets are used for conversions from measured numeric values and observational data into symbolic ones. Medical relationships between findings, diseases, and therapies, the rules, are represented by fuzzy relations, that express positive or negative associations. Findings, diseases, and therapies are organised in hierarchies.

Building Better Discipline Strategies for Schools by Fuzzy Logics

ERIC Educational Resources Information Center

Chang, Dian-Fu; Juan, Ya-Yun; Chou, Wen-Ching

2014-01-01

This study aims to realize better discipline strategies for applying in high schools. We invited 400 teachers to participate the survey and collected their perceptions on the discipline strategies in terms of the acceptance of strategies and their effectiveness in schools. Based on the idea of fuzzy statistics, this study transformed the fuzzy…

Usage Intention Framework Model: A Fuzzy Logic Interpretation of the Classical Utaut Model

ERIC Educational Resources Information Center

Sandaire, Johnny

2009-01-01

A fuzzy conjoint analysis (FCA: Turksen, 1992) model for enhancing management decision in the technology adoption domain was implemented as an extension to the UTAUT model (Venkatesh, Morris, Davis, & Davis, 2003). Additionally, a UTAUT-based Usage Intention Framework Model (UIFM) introduced a closed-loop feedback system. The empirical evidence…

Fuzzy and process modelling of contour ridge water dynamics

NASA Astrophysics Data System (ADS)

Mhizha, Alexander; Ndiritu, John

2018-05-01

Contour ridges are an in-situ rainwater harvesting technology developed initially for soil erosion control but are currently also widely promoted for rainwater harvesting. The effectiveness of contour ridges depends on geophysical, hydro-climatic and socio economic factors that are highly varied in time and space. Furthermore, field-scale data on these factors are often unavailable. This together with the complexity of hydrological processes at field scale limits the application of classical distributed process modelling to highly-instrumented experimental fields. This paper presents a framework that combines fuzzy logic and process-based approach for modelling contour ridges for rainwater harvesting where detailed field data are not available. Water balance for a representative contour-ridged field incorporating the water flow processes across the boundaries is integrated with fuzzy logic to incorporate the uncertainties in estimating runoff. The model is tested using data collected during the 2009/2010 and 2010/2011 rainfall seasons from two contour-ridged fields in Zhulube located in the semi-arid parts of Zimbabwe. The model is found to replicate soil moisture in the root zone reasonably well (NSE = 0.55 to 0.66 and PBIAS = -1.3 to 6.1 %). The results show that combining fuzzy logic and process based approaches can adequately model soil moisture in a contour ridged-field and could help to assess the water dynamics in contour ridged fields.

Fuzzy Logic Based Autonomous Parallel Parking System with Kalman Filtering

NASA Astrophysics Data System (ADS)

Panomruttanarug, Benjamas; Higuchi, Kohji

This paper presents an emulation of fuzzy logic control schemes for an autonomous parallel parking system in a backward maneuver. There are four infrared sensors sending the distance data to a microcontroller for generating an obstacle-free parking path. Two of them mounted on the front and rear wheels on the parking side are used as the inputs to the fuzzy rules to calculate a proper steering angle while backing. The other two attached to the front and rear ends serve for avoiding collision with other cars along the parking space. At the end of parking processes, the vehicle will be in line with other parked cars and positioned in the middle of the free space. Fuzzy rules are designed based upon a wall following process. Performance of the infrared sensors is improved using Kalman filtering. The design method needs extra information from ultrasonic sensors. Starting from modeling the ultrasonic sensor in 1-D state space forms, one makes use of the infrared sensor as a measurement to update the predicted values. Experimental results demonstrate the effectiveness of sensor improvement.

Dispositional logic

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

Zadeh, L.A.

1988-01-01

The applicability of conventional mathematical analysis (based on the combination of two-valued logic and probability theory) to problems in which human judgment, perception, or emotions play significant roles is considered theoretically. It is shown that dispositional logic, a branch of fuzzy logic, has particular relevance to the common-sense reasoning typical of human decision-making. The concepts of dispositionality and usuality are defined analytically, and a dispositional conjunctive rule and dispositional modus ponens are derived. 7 references.

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.

Assessment of Seismic Damage on The Exist Buildings Using Fuzzy Logic

NASA Astrophysics Data System (ADS)

Pınar, USTA; Nihat, MOROVA; EVCİ, Ahmet; ERGÜN, Serap

2018-01-01

Earthquake as a natural disaster could damage the lives of many people and buildings all over the world. These is micvulnerability of the buildings needs to be evaluated. Accurate evaluation of damage sustained by buildings during natural disaster events is critical to determine the buildings safety and their suitability for future occupancy. The earthquake is one of the disasters that structures face the most. There fore, there is a need to evaluate seismic damage and vulnerability of the buildings to protect them. These days fuzzy systems have been widely used in different fields of science because of its simpli city and efficiency. Fuzzy logic provides a suitable framework for reasoning, deduction, and decision making in fuzzy conditions. In this paper, studies on earthquake hazard evaluation of buildings by fuzzy logic modeling concepts in the literature have been investigated and evaluated, as a whole.

Efficiency of optical-electronic systems: methods application for the analysis of structural changes in the process of eye grounds diagnosis

NASA Astrophysics Data System (ADS)

Saldan, Yosyp R.; Pavlov, Sergii V.; Vovkotrub, Dina V.; Saldan, Yulia Y.; Vassilenko, Valentina B.; Mazur, Nadia I.; Nikolaichuk, Daria V.; Wójcik, Waldemar; Romaniuk, Ryszard; Suleimenov, Batyrbek; Bainazarov, Ulan

2017-08-01

Process of eye tomogram obtaining by means of optical coherent tomography is studied. Stages of idiopathic macula holes formation in the process of eye grounds diagnostics are considered. Main stages of retina pathology progression are determined: Fuzzy logic units for obtaining reliable conclusions regarding the result of diagnosis are developed. By the results of theoretical and practical research system and technique of retinal macular region of the eye state analysis is developed ; application of the system, based on fuzzy logic device, improves the efficiency of eye retina complex.

Fuzzy Logic Based Controller for a Grid-Connected Solid Oxide Fuel Cell Power Plant.

PubMed

Chatterjee, Kalyan; Shankar, Ravi; Kumar, Amit

2014-10-01

This paper describes a mathematical model of a solid oxide fuel cell (SOFC) power plant integrated in a multimachine power system. The utilization factor of a fuel stack maintains steady state by tuning the fuel valve in the fuel processor at a rate proportional to a current drawn from the fuel stack. A suitable fuzzy logic control is used for the overall system, its objective being controlling the current drawn by the power conditioning unit and meet a desirable output power demand. The proposed control scheme is verified through computer simulations.

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.

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.

On the fusion of tuning parameters of fuzzy rules and neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.

Human action quality evaluation based on fuzzy logic with application in underground coal mining.

PubMed

Ionica, Andreea; Leba, Monica

2015-01-01

The work system is defined by its components, their roles and the relationships between them. Any work system gravitates around the human resource and the interdependencies between human factor and the other components of it. Researches in this field agreed that the human factor and its actions are difficult to quantify and predict. The objective of this paper is to apply a method of human actions evaluation in order to estimate possible risks and prevent possible system faults, both at human factor level and at equipment level. In order to point out the importance of the human factor influence on all the elements of the working systems we propose a fuzzy logic based methodology for quality evaluation of human actions. This methodology has a multidisciplinary character, as it gathers ideas and methods from: quality management, ergonomics, work safety and artificial intelligence. The results presented refer to a work system with a high degree of specificity, namely, underground coal mining and are valuable for human resources risk evaluation pattern. The fuzzy logic evaluation of the human actions leads to early detection of possible dangerous evolutions of the work system and alarm the persons in charge.

A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application.

PubMed

Mostafa, Salama A; Mustapha, Aida; Mohammed, Mazin Abed; Ahmad, Mohd Sharifuddin; Mahmoud, Moamin A

2018-04-01

Autonomous agents are being widely used in many systems, such as ambient assisted-living systems, to perform tasks on behalf of humans. However, these systems usually operate in complex environments that entail uncertain, highly dynamic, or irregular workload. In such environments, autonomous agents tend to make decisions that lead to undesirable outcomes. In this paper, we propose a fuzzy-logic-based adjustable autonomy (FLAA) model to manage the autonomy of multi-agent systems that are operating in complex environments. This model aims to facilitate the autonomy management of agents and help them make competent autonomous decisions. The FLAA model employs fuzzy logic to quantitatively measure and distribute autonomy among several agents based on their performance. We implement and test this model in the Automated Elderly Movements Monitoring (AEMM-Care) system, which uses agents to monitor the daily movement activities of elderly users and perform fall detection and prevention tasks in a complex environment. The test results show that the FLAA model improves the accuracy and performance of these agents in detecting and preventing falls. Copyright © 2018 Elsevier B.V. All rights reserved.

Fuzzy logic control and optimization system

DOEpatents

Lou, Xinsheng [West Hartford, CT

2012-04-17

A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.

Fuzzy-logic-based network for complex systems risk assessment: application to ship performance analysis.

PubMed

Abou, Seraphin C

2012-03-01

In this paper, a new interpretation of intuitionistic fuzzy sets in the advanced framework of the Dempster-Shafer theory of evidence is extended to monitor safety-critical systems' performance. Not only is the proposed approach more effective, but it also takes into account the fuzzy rules that deal with imperfect knowledge/information and, therefore, is different from the classical Takagi-Sugeno fuzzy system, which assumes that the rule (the knowledge) is perfect. We provide an analytical solution to the practical and important problem of the conceptual probabilistic approach for formal ship safety assessment using the fuzzy set theory that involves uncertainties associated with the reliability input data. Thus, the overall safety of the ship engine is investigated as an object of risk analysis using the fuzzy mapping structure, which considers uncertainty and partial truth in the input-output mapping. The proposed method integrates direct evidence of the frame of discernment and is demonstrated through references to examples where fuzzy set models are informative. These simple applications illustrate how to assess the conflict of sensor information fusion for a sufficient cooling power system of vessels under extreme operation conditions. It was found that propulsion engine safety systems are not only a function of many environmental and operation profiles but are also dynamic and complex. Copyright © 2011 Elsevier Ltd. All rights reserved.

Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic.

PubMed

Dehghani Soufi, Mahsa; Samad-Soltani, Taha; Shams Vahdati, Samad; Rezaei-Hachesu, Peyman

2018-06-01

Fast and accurate patient triage for the response process is a critical first step in emergency situations. This process is often performed using a paper-based mode, which intensifies workload and difficulty, wastes time, and is at risk of human errors. This study aims to design and evaluate a decision support system (DSS) to determine the triage level. A combination of the Rule-Based Reasoning (RBR) and Fuzzy Logic Classifier (FLC) approaches were used to predict the triage level of patients according to the triage specialist's opinions and Emergency Severity Index (ESI) guidelines. RBR was applied for modeling the first to fourth decision points of the ESI algorithm. The data relating to vital signs were used as input variables and modeled using fuzzy logic. Narrative knowledge was converted to If-Then rules using XML. The extracted rules were then used to create the rule-based engine and predict the triage levels. Fourteen RBR and 27 fuzzy rules were extracted and used in the rule-based engine. The performance of the system was evaluated using three methods with real triage data. The accuracy of the clinical decision support systems (CDSSs; in the test data) was 99.44%. The evaluation of the error rate revealed that, when using the traditional method, 13.4% of the patients were miss-triaged, which is statically significant. The completeness of the documentation also improved from 76.72% to 98.5%. Designed system was effective in determining the triage level of patients and it proved helpful for nurses as they made decisions, generated nursing diagnoses based on triage guidelines. The hybrid approach can reduce triage misdiagnosis in a highly accurate manner and improve the triage outcomes. Copyright © 2018 Elsevier B.V. All rights reserved.

Certainty, Determinism, and Predictability in Theories of Instructional Design: Lessons from Science.

ERIC Educational Resources Information Center

Jonassen, David H.; And Others

1997-01-01

The strongly positivist beliefs on which traditional conceptions of instructional design (ID) are based derive from Aristotelian logic and oversimplify the world, reducing human learning and performance to a repertoire of manipulable behaviors. Reviews the cases against deterministic predictability and discusses hermeneutic, fuzzy logic, and chaos…

Fuzzy Traffic Control with Vehicle-to-Everything Communication.

PubMed

Salman, Muntaser A; Ozdemir, Suat; Celebi, Fatih V

2018-01-27

Traffic signal control (TSC) with vehicle-to everything (V2X) communication can be a very efficient solution to traffic congestion problem. Ratio of vehicles equipped with V2X communication capability in the traffic to the total number of vehicles (called penetration rate PR) is still low, thus V2X based TSC systems need to be supported by some other mechanisms. PR is the major factor that affects the quality of TSC process along with the evaluation interval. Quality of the TSC in each direction is a function of overall TSC quality of an intersection. Hence, quality evaluation of each direction should follow the evaluation of the overall intersection. Computational intelligence, more specifically swarm algorithm, has been recently used in this field in a European Framework Program FP7 supported project called COLOMBO. In this paper, using COLOMBO framework, further investigations have been done and two new methodologies using simple and fuzzy logic have been proposed. To evaluate the performance of our proposed methods, a comparison with COLOMBOs approach has been realized. The results reveal that TSC problem can be solved as a logical problem rather than an optimization problem. Performance of the proposed approaches is good enough to be suggested for future work under realistic scenarios even under low PR.

Fuzzy Traffic Control with Vehicle-to-Everything Communication

PubMed Central

Ozdemir, Suat; Celebi, Fatih V.

2018-01-01

Traffic signal control (TSC) with vehicle-to everything (V2X) communication can be a very efficient solution to traffic congestion problem. Ratio of vehicles equipped with V2X communication capability in the traffic to the total number of vehicles (called penetration rate PR) is still low, thus V2X based TSC systems need to be supported by some other mechanisms. PR is the major factor that affects the quality of TSC process along with the evaluation interval. Quality of the TSC in each direction is a function of overall TSC quality of an intersection. Hence, quality evaluation of each direction should follow the evaluation of the overall intersection. Computational intelligence, more specifically swarm algorithm, has been recently used in this field in a European Framework Program FP7 supported project called COLOMBO. In this paper, using COLOMBO framework, further investigations have been done and two new methodologies using simple and fuzzy logic have been proposed. To evaluate the performance of our proposed methods, a comparison with COLOMBOs approach has been realized. The results reveal that TSC problem can be solved as a logical problem rather than an optimization problem. Performance of the proposed approaches is good enough to be suggested for future work under realistic scenarios even under low PR. PMID:29382053

An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller

ERIC Educational Resources Information Center

Mamdani, E. H.; Assilian, S.

1975-01-01

This paper describes an experiment on the "linguistic" synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. (Author)

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.

Systematic design of membership functions for fuzzy-logic control: A case study on one-stage partial nitritation/anammox treatment systems.

PubMed

Boiocchi, Riccardo; Gernaey, Krist V; Sin, Gürkan

2016-10-01

A methodology is developed to systematically design the membership functions of fuzzy-logic controllers for multivariable systems. The methodology consists of a systematic derivation of the critical points of the membership functions as a function of predefined control objectives. Several constrained optimization problems corresponding to different qualitative operation states of the system are defined and solved to identify, in a consistent manner, the critical points of the membership functions for the input variables. The consistently identified critical points, together with the linguistic rules, determine the long term reachability of the control objectives by the fuzzy logic controller. The methodology is highlighted using a single-stage side-stream partial nitritation/Anammox reactor as a case study. As a result, a new fuzzy-logic controller for high and stable total nitrogen removal efficiency is designed. Rigorous simulations are carried out to evaluate and benchmark the performance of the controller. The results demonstrate that the novel control strategy is capable of rejecting the long-term influent disturbances, and can achieve a stable and high TN removal efficiency. Additionally, the controller was tested, and showed robustness, against measurement noise levels typical for wastewater sensors. A feedforward-feedback configuration using the present controller would give even better performance. In comparison, a previously developed fuzzy-logic controller using merely expert and intuitive knowledge performed worse. This proved the importance of using a systematic methodology for the derivation of the membership functions for multivariable systems. These results are promising for future applications of the controller in real full-scale plants. Furthermore, the methodology can be used as a tool to help systematically design fuzzy logic control applications for other biological processes. Copyright © 2016 Elsevier Ltd. All rights reserved.

On structuring the rules of a fuzzy controller

NASA Technical Reports Server (NTRS)

Zhou, Jun; Raju, G. V. S.

1993-01-01

Since the pioneering work of Zadeh and Mamdani and Assilian, fuzzy logic control has emerged as one of the most active and fruitful research areas. The applications of fuzzy logic control can be found in many fields such as control of stream generators, automatic train operation systems, elevator control, nuclear reactor control, automobile transmission control, etc. In this paper, two new structures of hierarchical fuzzy rule-based controller are proposed to reduce the number of rules in a complete rule set of a controller. In one approach, the overall system is split into sub-systems which are treated independently in parallel. A coordinator is then used to take into account the interactions. This is done via an iterating information exchange between the lower level and the coordinator level. From the point of view of information used, this structure is very similar to central structure in that the coordinator can have at least in principle, all the information that the local controllers have.

A fuzzy Petri-net-based mode identification algorithm for fault diagnosis of complex systems

NASA Astrophysics Data System (ADS)

Propes, Nicholas C.; Vachtsevanos, George

2003-08-01

Complex dynamical systems such as aircraft, manufacturing systems, chillers, motor vehicles, submarines, etc. exhibit continuous and event-driven dynamics. These systems undergo several discrete operating modes from startup to shutdown. For example, a certain shipboard system may be operating at half load or full load or may be at start-up or shutdown. Of particular interest are extreme or "shock" operating conditions, which tend to severely impact fault diagnosis or the progression of a fault leading to a failure. Fault conditions are strongly dependent on the operating mode. Therefore, it is essential that in any diagnostic/prognostic architecture, the operating mode be identified as accurately as possible so that such functions as feature extraction, diagnostics, prognostics, etc. can be correlated with the predominant operating conditions. This paper introduces a mode identification methodology that incorporates both time- and event-driven information about the process. A fuzzy Petri net is used to represent the possible successive mode transitions and to detect events from processed sensor signals signifying a mode change. The operating mode is initialized and verified by analysis of the time-driven dynamics through a fuzzy logic classifier. An evidence combiner module is used to combine the results from both the fuzzy Petri net and the fuzzy logic classifier to determine the mode. Unlike most event-driven mode identifiers, this architecture will provide automatic mode initialization through the fuzzy logic classifier and robustness through the combining of evidence of the two algorithms. The mode identification methodology is applied to an AC Plant typically found as a component of a shipboard system.

Using fuzzy logic analysis for siting decisions of infiltration trenches for highway runoff control.

PubMed

Ki, Seo Jin; Ray, Chittaranjan

2014-09-15

Determining optimal locations for best management practices (BMPs), including their field considerations and limitations, plays an important role for effective stormwater management. However, these issues have been often overlooked in modeling studies that focused on downstream water quality benefits. This study illustrates the methodology of locating infiltration trenches at suitable locations from spatial overlay analyses which combine multiple layers that address different aspects of field application into a composite map. Using seven thematic layers for each analysis, fuzzy logic was employed to develop a site suitability map for infiltration trenches, whereas the DRASTIC method was used to produce a groundwater vulnerability map on the island of Oahu, Hawaii, USA. In addition, the analytic hierarchy process (AHP), one of the most popular overlay analyses, was used for comparison to fuzzy logic. The results showed that the AHP and fuzzy logic methods developed significantly different index maps in terms of best locations and suitability scores. Specifically, the AHP method provided a maximum level of site suitability due to its inherent aggregation approach of all input layers in a linear equation. The most eligible areas in locating infiltration trenches were determined from the superposition of the site suitability and groundwater vulnerability maps using the fuzzy AND operator. The resulting map successfully balanced qualification criteria for a low risk of groundwater contamination and the best BMP site selection. The results of the sensitivity analysis showed that the suitability scores were strongly affected by the algorithms embedded in fuzzy logic; therefore, caution is recommended with their use in overlay analysis. Accordingly, this study demonstrates that the fuzzy logic analysis can not only be used to improve spatial decision quality along with other overlay approaches, but also is combined with general water quality models for initial and refined searches for the best locations of BMPs at the sub-basin level. Copyright © 2014. Published by Elsevier B.V.

Fuzzy Logic Decoupled Longitudinal Control for General Aviation Airplanes

NASA Technical Reports Server (NTRS)

Duerksen, Noel

1996-01-01

It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control difference airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control throttle position and another to control elevator position. These two controllers were used to control flight path angle and airspeed for both a piston powered single engine airplane simulation and a business jet simulation. Overspeed protection and stall protection were incorporated in the form of expert systems supervisors. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic longitudinal controller could be successfully used on two general aviation aircraft types that have very difference characteristics. These controllers worked for both airplanes over their entire flight envelopes including configuration changes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle lever travel, etc.). The controllers also handled configuration changes without mode switching or knowledge of the current configuration. This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.

Neuro-Fuzzy Support of Knowledge Management in Social Regulation

NASA Astrophysics Data System (ADS)

Petrovic-Lazarevic, Sonja; Coghill, Ken; Abraham, Ajith

2002-09-01

The aim of the paper is to demonstrate the neuro-fuzzy support of knowledge management in social regulation. Knowledge could be understood for social regulation purposes as explicit and tacit. Explicit knowledge relates to the community culture indicating how things work in the community based on social policies and procedures. Tacit knowledge is ethics and norms of the community. The former could be codified, stored and transferable in order to support decision making, while the latter being based on personal knowledge, experience and judgments is difficult to codify and store. Tacit knowledge expressed through linguistic information can be stored and used to support knowledge management in social regulation through the application of fuzzy and neuro-fuzzy logic.

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.

Construction safety monitoring based on the project's characteristic with fuzzy logic approach

NASA Astrophysics Data System (ADS)

Winanda, Lila Ayu Ratna; Adi, Trijoko Wahyu; Anwar, Nadjadji; Wahyuni, Febriana Santi

2017-11-01

Construction workers accident is the highest number compared with other industries and falls are the main cause of fatal and serious injuries in high rise projects. Generally, construction workers accidents are caused by unsafe act and unsafe condition that can occur separately or together, thus a safety monitoring system based on influencing factors is needed to achieve zero accident in construction industry. The dynamic characteristic in construction causes high mobility for workers while doing the task, so it requires a continuously monitoring system to detect unsafe condition and to protect workers from potential hazards. In accordance with the unique nature of project, fuzzy logic approach is one of the appropriate methods for workers safety monitoring on site. In this study, the focus of discussion is based on the characteristic of construction projects in analyzing "potential hazard" and the "protection planning" to be used in accident prevention. The data have been collected from literature review, expert opinion and institution of safety and health. This data used to determine hazard identification. Then, an application model is created using Delphi programming. The process in fuzzy is divided into fuzzification, inference and defuzzification, according to the data collection. Then, the input and final output data are given back to the expert for assessment as a validation of application model. The result of the study showed that the potential hazard of construction workers accident could be analysed based on characteristic of project and protection system on site and fuzzy logic approach can be used for construction workers accident analysis. Based on case study and the feedback assessment from expert, it showed that the application model can be used as one of the safety monitoring tools.

Convergent method of and apparatus for distributed control of robotic systems using fuzzy logic

DOEpatents

Feddema, John T.; Driessen, Brian J.; Kwok, Kwan S.

2002-01-01

A decentralized fuzzy logic control system for one vehicle or for multiple robotic vehicles provides a way to control each vehicle to converge on a goal without collisions between vehicles or collisions with other obstacles, in the presence of noisy input measurements and a limited amount of compute-power and memory on board each robotic vehicle. The fuzzy controller demonstrates improved robustness to noise relative to an exact controller.

A fuzzy logic approach to control anaerobic digestion.

PubMed

Domnanovich, A M; Strik, D P; Zani, L; Pfeiffer, B; Karlovits, M; Braun, R; Holubar, P

2003-01-01

One of the goals of the EU-Project AMONCO (Advanced Prediction, Monitoring and Controlling of Anaerobic Digestion Process Behaviour towards Biogas Usage in Fuel Cells) is to create a control tool for the anaerobic digestion process, which predicts the volumetric organic loading rate (Bv) for the next day, to obtain a high biogas quality and production. The biogas should contain a high methane concentration (over 50%) and a low concentration of components toxic for fuel cells, e.g. hydrogen sulphide, siloxanes, ammonia and mercaptanes. For producing data to test the control tool, four 20 l anaerobic Continuously Stirred Tank Reactors (CSTR) are operated. For controlling two systems were investigated: a pure fuzzy logic system and a hybrid-system which contains a fuzzy based reactor condition calculation and a hierachial neural net in a cascade of optimisation algorithms.

Fuzzy control for nonlinear structure with semi-active friction damper

NASA Astrophysics Data System (ADS)

Zhao, Da-Hai; Li, Hong-Nan

2007-04-01

The implementation of semi-active friction damper for vibration mitigation of seismic structure generally requires an efficient control strategy. In this paper, the fuzzy logic based on Takagi-Sugeno model is proposed for controlling a semi-active friction damper that is installed on a nonlinear building subjected to strong earthquakes. The continuous Bouc-Wen hysteretic model for the stiffness is used to describe nonlinear characteristic of the building. The optimal sliding force with friction damper is determined by nonlinear time history analysis under normal earthquakes. The Takagi-Sugeno fuzzy logic model is employed to adjust the clamping force acted on the friction damper according to the semi-active control strategy. Numerical simulation results demonstrate that the proposed method is very efficient in reducing the peak inter-story drift and acceleration of the nonlinear building structure under earthquake excitations.

Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation

PubMed Central

Masmoudi, Mohamed Slim; Masmoudi, Mohamed

2016-01-01

This paper describes the design and the implementation of a trajectory tracking controller using fuzzy logic for mobile robot to navigate in indoor environments. Most of the previous works used two independent controllers for navigation and avoiding obstacles. The main contribution of the paper can be summarized in the fact that we use only one fuzzy controller for navigation and obstacle avoidance. The used mobile robot is equipped with DC motor, nine infrared range (IR) sensors to measure the distance to obstacles, and two optical encoders to provide the actual position and speeds. To evaluate the performances of the intelligent navigation algorithms, different trajectories are used and simulated using MATLAB software and SIMIAM navigation platform. Simulation results show the performances of the intelligent navigation algorithms in terms of simulation times and travelled path. PMID:27688748

Ontology-based vector space model and fuzzy query expansion to retrieve knowledge on medical computational problem solutions.

PubMed

Bratsas, Charalampos; Koutkias, Vassilis; Kaimakamis, Evangelos; Bamidis, Panagiotis; Maglaveras, Nicos

2007-01-01

Medical Computational Problem (MCP) solving is related to medical problems and their computerized algorithmic solutions. In this paper, an extension of an ontology-based model to fuzzy logic is presented, as a means to enhance the information retrieval (IR) procedure in semantic management of MCPs. We present herein the methodology followed for the fuzzy expansion of the ontology model, the fuzzy query expansion procedure, as well as an appropriate ontology-based Vector Space Model (VSM) that was constructed for efficient mapping of user-defined MCP search criteria and MCP acquired knowledge. The relevant fuzzy thesaurus is constructed by calculating the simultaneous occurrences of terms and the term-to-term similarities derived from the ontology that utilizes UMLS (Unified Medical Language System) concepts by using Concept Unique Identifiers (CUI), synonyms, semantic types, and broader-narrower relationships for fuzzy query expansion. The current approach constitutes a sophisticated advance for effective, semantics-based MCP-related IR.

Boolean logic tree of graphene-based chemical system for molecular computation and intelligent molecular search query.

PubMed

Huang, Wei Tao; Luo, Hong Qun; Li, Nian Bing

2014-05-06

The most serious, and yet unsolved, problem of constructing molecular computing devices consists in connecting all of these molecular events into a usable device. This report demonstrates the use of Boolean logic tree for analyzing the chemical event network based on graphene, organic dye, thrombin aptamer, and Fenton reaction, organizing and connecting these basic chemical events. And this chemical event network can be utilized to implement fluorescent combinatorial logic (including basic logic gates and complex integrated logic circuits) and fuzzy logic computing. On the basis of the Boolean logic tree analysis and logic computing, these basic chemical events can be considered as programmable "words" and chemical interactions as "syntax" logic rules to construct molecular search engine for performing intelligent molecular search query. Our approach is helpful in developing the advanced logic program based on molecules for application in biosensing, nanotechnology, and drug delivery.

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.

Computer-aided bone age assessment for ethnically diverse older children using integrated fuzzy logic system

NASA Astrophysics Data System (ADS)

Ma, Kevin; Moin, Paymann; Zhang, Aifeng; Liu, Brent

2010-03-01

Bone Age Assessment (BAA) of children is a clinical procedure frequently performed in pediatric radiology to evaluate the stage of skeletal maturation based on the left hand x-ray radiograph. The current BAA standard in the US is using the Greulich & Pyle (G&P) Hand Atlas, which was developed fifty years ago and was only based on Caucasian population from the Midwest US. To bring the BAA procedure up-to-date with today's population, a Digital Hand Atlas (DHA) consisting of 1400 hand images of normal children of different ethnicities, age, and gender. Based on the DHA and to solve inter- and intra-observer reading discrepancies, an automatic computer-aided bone age assessment system has been developed and tested in clinical environments. The algorithm utilizes features extracted from three regions of interests: phalanges, carpal, and radius. The features are aggregated into a fuzzy logic system, which outputs the calculated bone age. The previous BAA system only uses features from phalanges and carpal, thus BAA result for children over age of 15 is less accurate. In this project, the new radius features are incorporated into the overall BAA system. The bone age results, calculated from the new fuzzy logic system, are compared against radiologists' readings based on G&P atlas, and exhibits an improvement in reading accuracy for older children.

Experimental Verification of Electric Drive Technologies Based on Artificial Intelligence Tools

NASA Technical Reports Server (NTRS)

Rubaai, Ahmed; Kankam, David (Technical Monitor)

2003-01-01

A laboratory implementation of a fuzzy logic-tracking controller using a low cost Motorola MC68HC11E9 microprocessor is described in this report. The objective is to design the most optimal yet practical controller that can be implemented and marketed, and which gives respectable performance, even when the system loads, inertia and parameters are varying. A distinguishing feature of this work is the by-product goal of developing a marketable, simple, functional and low cost controller. Additionally, real-time nonlinearities are not ignored, and a mathematical model is not required. A number of components have been designed, built and tested individually, and in various combinations of hardware and software segments. These components have been integrated with a brushless motor to constitute the drive system. A microprocessor-based FLC is incorporated to provide robust speed and position control. Design objectives that are difficult to express mathematically can be easily incorporated in a fuzzy logic-based controller by linguistic information (in the form of fuzzy IF-THEN rules). The theory and design are tested in the laboratory using a hardware setup. Several test cases have been conducted to confirm the effectiveness of the proposed controller. The results indicate excellent tracking performance for both speed and position trajectories. For the purpose of comparison, a bang-bang controller has been tested. The fuzzy logic controller performs significantly better than the traditional bang-bang controller. The bang-bang controller has been shown to be relatively inaccurate and lacking in robustness. Description of the implementation hardware system is also given.

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.

Command Filtering-Based Fuzzy Control for Nonlinear Systems With Saturation Input.

PubMed

Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Lin, Chong

2017-09-01

In this paper, command filtering-based fuzzy control is designed for uncertain multi-input multioutput (MIMO) nonlinear systems with saturation nonlinearity input. First, the command filtering method is employed to deal with the explosion of complexity caused by the derivative of virtual controllers. Then, fuzzy logic systems are utilized to approximate the nonlinear functions of MIMO systems. Furthermore, error compensation mechanism is introduced to overcome the drawback of the dynamics surface approach. The developed method will guarantee all signals of the systems are bounded. The effectiveness and advantages of the theoretic result are obtained by a simulation example.

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.

Lithium-ion battery state of function estimation based on fuzzy logic algorithm with associated variables

NASA Astrophysics Data System (ADS)

Gan, L.; Yang, F.; Shi, Y. F.; He, H. L.

2017-11-01

Many occasions related to batteries demand to know how much continuous and instantaneous power can batteries provide such as the rapidly developing electric vehicles. As the large-scale applications of lithium-ion batteries, lithium-ion batteries are used to be our research object. Many experiments are designed to get the lithium-ion battery parameters to ensure the relevance and reliability of the estimation. To evaluate the continuous and instantaneous load capability of a battery called state-of-function (SOF), this paper proposes a fuzzy logic algorithm based on battery state-of-charge(SOC), state-of-health(SOH) and C-rate parameters. Simulation and experimental results indicate that the proposed approach is suitable for battery SOF estimation.

A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

PubMed Central

Thounaojam, Dalton Meitei; Khelchandra, Thongam; Singh, Kh. Manglem; Roy, Sudipta

2016-01-01

This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter. PMID:27127500

Intelligent control of a multi-degree-of freedom reaction compensating platform system using fuzzy logic

NASA Technical Reports Server (NTRS)

Choi, Benjamin B.; Lawrence, Charles; Lin, Yueh-Jaw

1994-01-01

This paper presents the development of a general-purpose fuzzy logic (FL) control methodology for isolating the external vibratory disturbances of space-based devices. According to the desired performance specifications, a full investigation regarding the development of an FL controller was done using different scenarios, such as variances of passive reaction-compensating components and external disturbance load. It was shown that the proposed FL controller is robust in that the FL-controlled system closely follows the prespecified ideal reference model. The comparative study also reveals that the FL-controlled system achieves significant improvement in reducing vibrations over passive systems.

Automating Battlefield Event Reporting Using Conceptual Spaces and Fuzzy Logic for Passive Speech Interpretation

DTIC Science & Technology

2009-10-01

SPACES AND FUZZY LOGIC FOR PASSIVE SPEECH INTERPRETATION Katie T. McConky Research Scientist CUBRC Buffalo, NY, U.S.A. mcconky@cubrc.org...ORGANIZATION NAME(S) AND ADDRESS(ES) CUBRC ,4455 Genesee Street, Suite 106,Buffalo,NY,14225 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING

Fuzzy Logic as a Tool to Compare Reliability of Torsion Bar System

DTIC Science & Technology

2009-12-17

A paper by Arati Dexit, Harpreet Singh and Kassem Saab presents a possible scenario of simulating reliability using Fuzzy Logic.[4] This...29, Issue: 3 [2]. Weibell.com: “ Overview of System Reliability” [3]. Arati MDixit, Harpreet Singh, and Kassem Saab Department of Electrical

Fuzzy Logic for Determination of Crack Severity in Defense Applications

DTIC Science & Technology

2010-03-31

FUZZY LOGIC FOR DETERMINATION OF CRACK SEVERITY IN DEFENSE APPLICATIONS Vijay Sharma , Harpreet Singh, Arati M. Dixit, Ahmed Mekki Department of... Vijay Sharma ; Harpreet Singh; Arati M. Dixit; Ahmed Mekki 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S

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.

Fuzzy Control of Robotic Arm

NASA Astrophysics Data System (ADS)

Lin, Kyaw Kyaw; Soe, Aung Kyaw; Thu, Theint Theint

2008-10-01

This research work investigates a Self-Tuning Proportional Derivative (PD) type Fuzzy Logic Controller (STPDFLC) for a two link robot system. The proposed scheme adjusts on-line the output Scaling Factor (SF) by fuzzy rules according to the current trend of the robot. The rule base for tuning the output scaling factor is defined on the error (e) and change in error (de). The scheme is also based on the fact that the controller always tries to manipulate the process input. The rules are in the familiar if-then format. All membership functions for controller inputs (e and de) and controller output (UN) are defined on the common interval [-1,1]; whereas the membership functions for the gain updating factor (α) is defined on [0,1]. There are various methods to calculate the crisp output of the system. Center of Gravity (COG) method is used in this application due to better results it gives. Performances of the proposed STPDFLC are compared with those of their corresponding PD-type conventional Fuzzy Logic Controller (PDFLC). The proposed scheme shows a remarkably improved performance over its conventional counterpart especially under parameters variation (payload). The two-link results of analysis are simulated. These simulation results are illustrated by using MATLAB® programming.

Conditioning of high voltage radio frequency cavities by using fuzzy logic in connection with rule based programming

NASA Astrophysics Data System (ADS)

Perreard, S.; Wildner, E.

1994-12-01

Many processes are controlled by experts using some kind of mental model to decide on actions and make conclusions. This model, based on heuristic knowledge, can often be represented by rules and does not have to be particularly accurate. Such is the case for the problem of conditioning high voltage RF cavities; the expert has to decide, by observing some criteria, whether to increase or to decrease the voltage and by how much. A program has been implemented which can be applied to a class of similar problems. The kernel of the program is a small rule base, which is independent of the kind of cavity. To model a specific cavity, we use fuzzy logic which is implemented as a separate routine called by the rule base, to translate from numeric to symbolic information.

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.

Implementation of a new fuzzy vector control of induction motor.

PubMed

Rafa, Souad; Larabi, Abdelkader; Barazane, Linda; Manceur, Malik; Essounbouli, Najib; Hamzaoui, Abdelaziz

2014-05-01

The aim of this paper is to present a new approach to control an induction motor using type-1 fuzzy logic. The induction motor has a nonlinear model, uncertain and strongly coupled. The vector control technique, which is based on the inverse model of the induction motors, solves the coupling problem. Unfortunately, in practice this is not checked because of model uncertainties. Indeed, the presence of the uncertainties led us to use human expertise such as the fuzzy logic techniques. In order to maintain the decoupling and to overcome the problem of the sensitivity to the parametric variations, the field-oriented control is replaced by a new block control. The simulation results show that the both control schemes provide in their basic configuration, comparable performances regarding the decoupling. However, the fuzzy vector control provides the insensitivity to the parametric variations compared to the classical one. The fuzzy vector control scheme is successfully implemented in real-time using a digital signal processor board dSPACE 1104. The efficiency of this technique is verified as well as experimentally at different dynamic operating conditions such as sudden loads change, parameter variations, speed changes, etc. The fuzzy vector control is found to be a best control for application in an induction motor. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Predicting subcontractor performance using web-based Evolutionary Fuzzy Neural Networks.

PubMed

Ko, Chien-Ho

2013-01-01

Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.

Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks

PubMed Central

2013-01-01

Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism. PMID:23864830

Sensor Fusion Based Model for Collision Free Mobile Robot Navigation

PubMed Central

Almasri, Marwah; Elleithy, Khaled; Alajlan, Abrar

2015-01-01

Autonomous mobile robots have become a very popular and interesting topic in the last decade. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors. These sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. This paper presents a collision free mobile robot navigation based on the fuzzy logic fusion model. Eight distance sensors and a range finder camera are used for the collision avoidance approach where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs which are the eight distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot’s wheels, and 24 fuzzy rules for the robot’s movement. Webots Pro simulator is used for modeling the environment and the robot. The proposed methodology, which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real time experiments. Various scenarios have been presented with static and dynamic obstacles using one robot and two robots while avoiding obstacles in different shapes and sizes. PMID:26712766

Sensor Fusion Based Model for Collision Free Mobile Robot Navigation.

PubMed

Almasri, Marwah; Elleithy, Khaled; Alajlan, Abrar

2015-12-26

Autonomous mobile robots have become a very popular and interesting topic in the last decade. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors. These sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. This paper presents a collision free mobile robot navigation based on the fuzzy logic fusion model. Eight distance sensors and a range finder camera are used for the collision avoidance approach where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs which are the eight distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot's wheels, and 24 fuzzy rules for the robot's movement. Webots Pro simulator is used for modeling the environment and the robot. The proposed methodology, which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real time experiments. Various scenarios have been presented with static and dynamic obstacles using one robot and two robots while avoiding obstacles in different shapes and sizes.

The Design of Artificial Intelligence Robot Based on Fuzzy Logic Controller Algorithm

NASA Astrophysics Data System (ADS)

Zuhrie, M. S.; Munoto; Hariadi, E.; Muslim, S.

2018-04-01

Artificial Intelligence Robot is a wheeled robot driven by a DC motor that moves along the wall using an ultrasonic sensor as a detector of obstacles. This study uses ultrasonic sensors HC-SR04 to measure the distance between the robot with the wall based ultrasonic wave. This robot uses Fuzzy Logic Controller to adjust the speed of DC motor. When the ultrasonic sensor detects a certain distance, sensor data is processed on ATmega8 then the data goes to ATmega16. From ATmega16, sensor data is calculated based on Fuzzy rules to drive DC motor speed. The program used to adjust the speed of a DC motor is CVAVR program (Code Vision AVR). The readable distance of ultrasonic sensor is 3 cm to 250 cm with response time 0.5 s. Testing of robots on walls with a setpoint value of 9 cm to 10 cm produce an average error value of -12% on the wall of L, -8% on T walls, -8% on U wall, and -1% in square wall.

Classification of Children Intelligence with Fuzzy Logic Method

NASA Astrophysics Data System (ADS)

Syahminan; ika Hidayati, Permata

2018-04-01

Intelligence of children s An Important Thing To Know The Parents Early on. Typing Can be done With a Child’s intelligence Grouping Dominant Characteristics Of each Type of Intelligence. To Make it easier for Parents in Determining The type of Children’s intelligence And How to Overcome them, for It Created A Classification System Intelligence Grouping Children By Using Fuzzy logic method For determination Of a Child’s degree of intelligence type. From the analysis We concluded that The presence of Intelligence Classification systems Pendulum Children With Fuzzy Logic Method Of determining The type of The Child’s intelligence Can be Done in a way That is easier And The results More accurate Conclusions Than Manual tests.

Linguistic Summarization of Video for Fall Detection Using Voxel Person and Fuzzy Logic

PubMed Central

Anderson, Derek; Luke, Robert H.; Keller, James M.; Skubic, Marjorie; Rantz, Marilyn; Aud, Myra

2009-01-01

In this paper, we present a method for recognizing human activity from linguistic summarizations of temporal fuzzy inference curves representing the states of a three-dimensional object called voxel person. A hierarchy of fuzzy logic is used, where the output from each level is summarized and fed into the next level. We present a two level model for fall detection. The first level infers the states of the person at each image. The second level operates on linguistic summarizations of voxel person’s states and inference regarding activity is performed. The rules used for fall detection were designed under the supervision of nurses to ensure that they reflect the manner in which elders perform these activities. The proposed framework is extremely flexible. Rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability. PMID:20046216

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.

Fuzzy logic and optical correlation-based face recognition method for patient monitoring application in home video surveillance

NASA Astrophysics Data System (ADS)

Elbouz, Marwa; Alfalou, Ayman; Brosseau, Christian

2011-06-01

Home automation is being implemented into more and more domiciles of the elderly and disabled in order to maintain their independence and safety. For that purpose, we propose and validate a surveillance video system, which detects various posture-based events. One of the novel points of this system is to use adapted Vander-Lugt correlator (VLC) and joint-transfer correlator (JTC) techniques to make decisions on the identity of a patient and his three-dimensional (3-D) positions in order to overcome the problem of crowd environment. We propose a fuzzy logic technique to get decisions on the subject's behavior. Our system is focused on the goals of accuracy, convenience, and cost, which in addition does not require any devices attached to the subject. The system permits one to study and model subject responses to behavioral change intervention because several levels of alarm can be incorporated according different situations considered. Our algorithm performs a fast 3-D recovery of the subject's head position by locating eyes within the face image and involves a model-based prediction and optical correlation techniques to guide the tracking procedure. The object detection is based on (hue, saturation, value) color space. The system also involves an adapted fuzzy logic control algorithm to make a decision based on information given to the system. Furthermore, the principles described here are applicable to a very wide range of situations and robust enough to be implementable in ongoing experiments.

Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories

NASA Technical Reports Server (NTRS)

Burchett, Bradley T.

2003-01-01

The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.

Fuzzy parametric uncertainty analysis of linear dynamical systems: A surrogate modeling approach

NASA Astrophysics Data System (ADS)

Chowdhury, R.; Adhikari, S.

2012-10-01

Uncertainty propagation engineering systems possess significant computational challenges. This paper explores the possibility of using correlated function expansion based metamodelling approach when uncertain system parameters are modeled using Fuzzy variables. In particular, the application of High-Dimensional Model Representation (HDMR) is proposed for fuzzy finite element analysis of dynamical systems. The HDMR expansion is a set of quantitative model assessment and analysis tools for capturing high-dimensional input-output system behavior based on a hierarchy of functions of increasing dimensions. The input variables may be either finite-dimensional (i.e., a vector of parameters chosen from the Euclidean space RM) or may be infinite-dimensional as in the function space CM[0,1]. The computational effort to determine the expansion functions using the alpha cut method scales polynomially with the number of variables rather than exponentially. This logic is based on the fundamental assumption underlying the HDMR representation that only low-order correlations among the input variables are likely to have significant impacts upon the outputs for most high-dimensional complex systems. The proposed method is integrated with a commercial Finite Element software. Modal analysis of a simplified aircraft wing with Fuzzy parameters has been used to illustrate the generality of the proposed approach. In the numerical examples, triangular membership functions have been used and the results have been validated against direct Monte Carlo simulations.

Autonomous vehicle motion control, approximate maps, and fuzzy logic

NASA Technical Reports Server (NTRS)

Ruspini, Enrique H.

1993-01-01

Progress on research on the control of actions of autonomous mobile agents using fuzzy logic is presented. The innovations described encompass theoretical and applied developments. At the theoretical level, results of research leading to the combined utilization of conventional artificial planning techniques with fuzzy logic approaches for the control of local motion and perception actions are presented. Also formulations of dynamic programming approaches to optimal control in the context of the analysis of approximate models of the real world are examined. Also a new approach to goal conflict resolution that does not require specification of numerical values representing relative goal importance is reviewed. Applied developments include the introduction of the notion of approximate map. A fuzzy relational database structure for the representation of vague and imprecise information about the robot's environment is proposed. Also the central notions of control point and control structure are discussed.

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.

Algorithm design, user interface, and optimization procedure for a fuzzy logic ramp metering algorithm : a training manual for freeway operations engineers

DOT National Transportation Integrated Search

2000-02-01

This training manual describes the fuzzy logic ramp metering algorithm in detail, as implemented system-wide in the greater Seattle area. The method of defining the inputs to the controller and optimizing the performance of the algorithm is explained...

Fuzzy efficiency optimization of AC induction motors

NASA Technical Reports Server (NTRS)

Jani, Yashvant; Sousa, Gilberto; Turner, Wayne; Spiegel, Ron; Chappell, Jeff

1993-01-01

This paper describes the early states of work to implement a fuzzy logic controller to optimize the efficiency of AC induction motor/adjustable speed drive (ASD) systems running at less than optimal speed and torque conditions. In this paper, the process by which the membership functions of the controller were tuned is discussed and a controller which operates on frequency as well as voltage is proposed. The membership functions for this dual-variable controller are sketched. Additional topics include an approach for fuzzy logic to motor current control which can be used with vector-controlled drives. Incorporation of a fuzzy controller as an application-specific integrated circuit (ASIC) microchip is planned.

Quantifying favorableness for occurrence of a mineral deposit type using fuzzy logic; an example from Arizona

USGS Publications Warehouse

Gettings, Mark E.; Bultman, Mark W.

1993-01-01

An application of possibility theory from fuzzy logic to the quantification of favorableness for quartz-carbonate vein deposits in the southern Santa Rita Mountains of southeastern Arizona is described. Three necessary but probably not sufficient conditions for the formation of these deposits were defined as the occurrence of carbonate berain rocks within hypabyssal depths, significant fracturing of the rocks, and proximity to a felsic intrusive. The quality of data available to evaluate these conditions is variable over the study area. The possibility of each condition was represented as a fuzzy set enumerated over the area. The intersection of the sets measures the degree of simultaneous occurrence of hte necessary factors and provides a measure of the possibility of deposit occurrence. Using fuzzy set technicques, the effect of one or more fuzzy sets relative to the others in the intersection can be controlled and logical combinations of the sets can be used to impose a time sequential constraint on the necessary conditions. Other necessary conditions, and supplementary conditions such as variable data quality or intensity of exploration can be included in the analysis by their proper representation as fuzzy sets.

Posture recognition based on fuzzy logic for home monitoring of the elderly.

PubMed

Brulin, Damien; Benezeth, Yannick; Courtial, Estelle

2012-09-01

We propose in this paper a computer vision-based posture recognition method for home monitoring of the elderly. The proposed system performs human detection prior to the posture analysis; posture recognition is performed only on a human silhouette. The human detection approach has been designed to be robust to different environmental stimuli. Thus, posture is analyzed with simple and efficient features that are not designed to manage constraints related to the environment but only designed to describe human silhouettes. The posture recognition method, based on fuzzy logic, identifies four static postures and is robust to variation in the distance between the camera and the person, and to the person's morphology. With an accuracy of 74.29% of satisfactory posture recognition, this approach can detect emergency situations such as a fall within a health smart home.

Approximate reasoning-based learning and control for proximity operations and docking in space

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Jani, Yashvant; Lea, Robert N.

1991-01-01

A recently proposed hybrid-neutral-network and fuzzy-logic-control architecture is applied to a fuzzy logic controller developed for attitude control of the Space Shuttle. A model using reinforcement learning and learning from past experience for fine-tuning its knowledge base is proposed. Two main components of this approximate reasoning-based intelligent control (ARIC) model - an action-state evaluation network and action selection network are described as well as the Space Shuttle attitude controller. An ARIC model for the controller is presented, and it is noted that the input layer in each network includes three nodes representing the angle error, angle error rate, and bias node. Preliminary results indicate that the controller can hold the pitch rate within its desired deadband and starts to use the jets at about 500 sec in the run.

Study on some useful Operators for Graph-theoretic Image Processing

NASA Astrophysics Data System (ADS)

Moghani, Ali; Nasiri, Parviz

2010-11-01

In this paper we describe a human perception based approach to pixel color segmentation which applied in color reconstruction by numerical method associated with graph-theoretic image processing algorithm typically in grayscale. Fuzzy sets defined on the Hue, Saturation and Value components of the HSV color space, provide a fuzzy logic model that aims to follow the human intuition of color classification.

Showcase for Biotechnology 2005

DTIC Science & Technology

2006-11-01

environmental factors essential in understanding health. This includes the experience of illness; how people think about and cope with disease, and... Uncertainty in the Assessment of Diagnostic and Prognostic Factors in Cancer Huseyin Seker, Ph.D, MSc, BSc, MIEEE, MSPS Coordinator of Bio-Health...namely fuzzy logic and hybrid neuro- fuzzy rule-based systems, have been incorporated will be presented to show how uncertainty in the assessment of these

A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals

PubMed Central

Castañón–Puga, Manuel; Salazar, Abby Stephanie; Aguilar, Leocundo; Gaxiola-Pacheco, Carelia; Licea, Guillermo

2015-01-01

The increasing use of mobile devices in indoor spaces brings challenges to location methods. This work presents a hybrid intelligent method based on data mining and Type-2 fuzzy logic to locate mobile devices in an indoor space by zones using Wi-Fi signals from selected access points (APs). This approach takes advantage of wireless local area networks (WLANs) over other types of architectures and implements the complete method in a mobile application using the developed tools. Besides, the proposed approach is validated by experimental data obtained from case studies and the cross-validation technique. For the purpose of generating the fuzzy rules that conform to the Takagi–Sugeno fuzzy system structure, a semi-supervised data mining technique called subtractive clustering is used. This algorithm finds centers of clusters from the radius map given by the collected signals from APs. Measurements of Wi-Fi signals can be noisy due to several factors mentioned in this work, so this method proposed the use of Type-2 fuzzy logic for modeling and dealing with such uncertain information. PMID:26633417

A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals.

PubMed

Castañón-Puga, Manuel; Salazar, Abby Stephanie; Aguilar, Leocundo; Gaxiola-Pacheco, Carelia; Licea, Guillermo

2015-12-02

The increasing use of mobile devices in indoor spaces brings challenges to location methods. This work presents a hybrid intelligent method based on data mining and Type-2 fuzzy logic to locate mobile devices in an indoor space by zones using Wi-Fi signals from selected access points (APs). This approach takes advantage of wireless local area networks (WLANs) over other types of architectures and implements the complete method in a mobile application using the developed tools. Besides, the proposed approach is validated by experimental data obtained from case studies and the cross-validation technique. For the purpose of generating the fuzzy rules that conform to the Takagi-Sugeno fuzzy system structure, a semi-supervised data mining technique called subtractive clustering is used. This algorithm finds centers of clusters from the radius map given by the collected signals from APs. Measurements of Wi-Fi signals can be noisy due to several factors mentioned in this work, so this method proposed the use of Type-2 fuzzy logic for modeling and dealing with such uncertain information.

Formalization of software requirements for information systems using fuzzy logic

NASA Astrophysics Data System (ADS)

Yegorov, Y. S.; Milov, V. R.; Kvasov, A. S.; Sorokoumova, S. N.; Suvorova, O. V.

2018-05-01

The paper considers an approach to the design of information systems based on flexible software development methodologies. The possibility of improving the management of the life cycle of information systems by assessing the functional relationship between requirements and business objectives is described. An approach is proposed to establish the relationship between the degree of achievement of business objectives and the fulfillment of requirements for the projected information system. It describes solutions that allow one to formalize the process of formation of functional and non-functional requirements with the help of fuzzy logic apparatus. The form of the objective function is formed on the basis of expert knowledge and is specified via learning from very small data set.

Boolean and fuzzy logic implemented at the molecular level

NASA Astrophysics Data System (ADS)

Gentili, Pier Luigi

2007-07-01

In this work, it is shown how to implement both hard and soft computing by means of two structurally related heterocyclic compounds: flindersine (FL) and 6(5H)-phenanthridinone (PH). Since FL and PH have a carbonyl group in their molecular skeletons, they exhibit Proximity Effects in their photophysics. In other words, they have an emission power that can be modulated through external inputs such as temperature ( T) and hydrogen-bonding donation (HBD) ability of solvents. This phenomenology can be exploited to implement both crisp and fuzzy logic. Fuzzy Logic Systems (FLSs) wherein the antecedents of the rules are connected through the AND operator, are built by both the Mamdani's and Sugeno's models. Finally, they are adopted as approximators of the proximity effect phenomenon and tested for their prediction capabilities. Moreover, FL as photochromic compound is also a multiply configurable crisp logic molecular element.

Landslide susceptibility mapping by combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process in Dozain basin

NASA Astrophysics Data System (ADS)

Tazik, E.; Jahantab, Z.; Bakhtiari, M.; Rezaei, A.; Kazem Alavipanah, S.

2014-10-01

Landslides are among the most important natural hazards that lead to modification of the environment. Therefore, studying of this phenomenon is so important in many areas. Because of the climate conditions, geologic, and geomorphologic characteristics of the region, the purpose of this study was landslide hazard assessment using Fuzzy Logic, frequency ratio and Analytical Hierarchy Process method in Dozein basin, Iran. At first, landslides occurred in Dozein basin were identified using aerial photos and field studies. The influenced landslide parameters that were used in this study including slope, aspect, elevation, lithology, precipitation, land cover, distance from fault, distance from road and distance from river were obtained from different sources and maps. Using these factors and the identified landslide, the fuzzy membership values were calculated by frequency ratio. Then to account for the importance of each of the factors in the landslide susceptibility, weights of each factor were determined based on questionnaire and AHP method. Finally, fuzzy map of each factor was multiplied to its weight that obtained using AHP method. At the end, for computing prediction accuracy, the produced map was verified by comparing to existing landslide locations. These results indicate that the combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process method are relatively good estimators of landslide susceptibility in the study area. According to landslide susceptibility map about 51% of the occurred landslide fall into the high and very high susceptibility zones of the landslide susceptibility map, but approximately 26 % of them indeed located in the low and very low susceptibility zones.

Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems

ERIC Educational Resources Information Center

Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.

2003-01-01

Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…

Mars Rover imaging systems and directional filtering

NASA Technical Reports Server (NTRS)

Wang, Paul P.

1989-01-01

Computer literature searches were carried out at Duke University and NASA Langley Research Center. The purpose is to enhance personal knowledge based on the technical problems of pattern recognition and image understanding which must be solved for the Mars Rover and Sample Return Mission. Intensive study effort of a large collection of relevant literature resulted in a compilation of all important documents in one place. Furthermore, the documents are being classified into: Mars Rover; computer vision (theory); imaging systems; pattern recognition methodologies; and other smart techniques (AI, neural networks, fuzzy logic, etc).

Knowledge and intelligent computing system in medicine.

PubMed

Pandey, Babita; Mishra, R B

2009-03-01

Knowledge-based systems (KBS) and intelligent computing systems have been used in the medical planning, diagnosis and treatment. The KBS consists of rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) whereas intelligent computing method (ICM) encompasses genetic algorithm (GA), artificial neural network (ANN), fuzzy logic (FL) and others. The combination of methods in KBS such as CBR-RBR, CBR-MBR and RBR-CBR-MBR and the combination of methods in ICM is ANN-GA, fuzzy-ANN, fuzzy-GA and fuzzy-ANN-GA. The combination of methods from KBS to ICM is RBR-ANN, CBR-ANN, RBR-CBR-ANN, fuzzy-RBR, fuzzy-CBR and fuzzy-CBR-ANN. In this paper, we have made a study of different singular and combined methods (185 in number) applicable to medical domain from mid 1970s to 2008. The study is presented in tabular form, showing the methods and its salient features, processes and application areas in medical domain (diagnosis, treatment and planning). It is observed that most of the methods are used in medical diagnosis very few are used for planning and moderate number in treatment. The study and its presentation in this context would be helpful for novice researchers in the area of medical expert system.

Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes

NASA Astrophysics Data System (ADS)

Feizizadeh, Bakhtiar; Blaschke, Thomas; Tiede, Dirk; Moghaddam, Mohammad Hossein Rezaei

2017-09-01

This article presents a method of object-based image analysis (OBIA) for landslide delineation and landslide-related change detection from multi-temporal satellite images. It uses both spatial and spectral information on landslides, through spectral analysis, shape analysis, textural measurements using a gray-level co-occurrence matrix (GLCM), and fuzzy logic membership functionality. Following an initial segmentation step, particular combinations of various information layers were investigated to generate objects. This was achieved by applying multi-resolution segmentation to IRS-1D, SPOT-5, and ALOS satellite imagery in sequential steps of feature selection and object classification, and using slope and flow direction derivatives from a digital elevation model together with topographically-oriented gray level co-occurrence matrices. Fuzzy membership values were calculated for 11 different membership functions using 20 landslide objects from a landslide training data. Six fuzzy operators were used for the final classification and the accuracies of the resulting landslide maps were compared. A Fuzzy Synthetic Evaluation (FSE) approach was adapted for validation of the results and for an accuracy assessment using the landslide inventory database. The FSE approach revealed that the AND operator performed best with an accuracy of 93.87% for 2005 and 94.74% for 2011, closely followed by the MEAN Arithmetic operator, while the OR and AND (*) operators yielded relatively low accuracies. An object-based change detection was then applied to monitor landslide-related changes that occurred in northern Iran between 2005 and 2011. Knowledge rules to detect possible landslide-related changes were developed by evaluating all possible landslide-related objects for both time steps.

Fuzzy logic modeling of the resistivity parameter and topography features for aquifer assessment in hydrogeological investigation of a crystalline basement complex

NASA Astrophysics Data System (ADS)

Adabanija, M. A.; Omidiora, E. O.; Olayinka, A. I.

2008-05-01

A linguistic fuzzy logic system (LFLS)-based expert system model has been developed for the assessment of aquifers for the location of productive water boreholes in a crystalline basement complex. The model design employed a multiple input/single output (MISO) approach with geoelectrical parameters and topographic features as input variables and control crisp value as the output. The application of the method to the data acquired in Khondalitic terrain, a basement complex in Vizianagaram District, south India, shows that potential groundwater resource zones that have control output values in the range 0.3295-0.3484 have a yield greater than 6,000 liters per hour (LPH). The range 0.3174-0.3226 gives a yield less than 4,000 LPH. The validation of the control crisp value using data acquired from Oban Massif, a basement complex in southeastern Nigeria, indicates a yield less than 3,000 LPH for control output values in the range 0.2938-0.3065. This validation corroborates the ability of control output values to predict a yield, thereby vindicating the applicability of linguistic fuzzy logic system in siting productive water boreholes in a basement complex.

A fuzzy MCDM framework based on fuzzy measure and fuzzy integral for agile supplier evaluation

NASA Astrophysics Data System (ADS)

Dursun, Mehtap

2017-06-01

Supply chains need to be agile in order to response quickly to the changes in today's competitive environment. The success of an agile supply chain depends on the firm's ability to select the most appropriate suppliers. This study proposes a multi-criteria decision making technique for conducting an analysis based on multi-level hierarchical structure and fuzzy logic for the evaluation of agile suppliers. The ideal and anti-ideal solutions are taken into consideration simultaneously in the developed approach. The proposed decision approach enables the decision-makers to use linguistic terms, and thus, reduce their cognitive burden in the evaluation process. Furthermore, a hierarchy of evaluation criteria and their related sub-criteria is employed in the presented approach in order to conduct a more effective analysis.

Intelligent control of non-linear dynamical system based on the adaptive neurocontroller

NASA Astrophysics Data System (ADS)

Engel, E.; Kovalev, I. V.; Kobezhicov, V.

2015-10-01

This paper presents an adaptive neuro-controller for intelligent control of non-linear dynamical system. The formed as the fuzzy selective neural net the adaptive neuro-controller on the base of system's state, creates the effective control signal under random perturbations. The validity and advantages of the proposed adaptive neuro-controller are demonstrated by numerical simulations. The simulation results show that the proposed controller scheme achieves real-time control speed and the competitive performance, as compared to PID, fuzzy logic controllers.

An architecture for designing fuzzy logic controllers using neural networks

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1991-01-01

Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.

Fuzzy logic algorithm for quantitative tissue characterization of diffuse liver diseases from ultrasound images.

PubMed

Badawi, A M; Derbala, A S; Youssef, A M

1999-08-01

Computerized ultrasound tissue characterization has become an objective means for diagnosis of liver diseases. It is difficult to differentiate diffuse liver diseases, namely cirrhotic and fatty liver by visual inspection from the ultrasound images. The visual criteria for differentiating diffused diseases are rather confusing and highly dependent upon the sonographer's experience. This often causes a bias effects in the diagnostic procedure and limits its objectivity and reproducibility. Computerized tissue characterization to assist quantitatively the sonographer for the accurate differentiation and to minimize the degree of risk is thus justified. Fuzzy logic has emerged as one of the most active area in classification. In this paper, we present an approach that employs Fuzzy reasoning techniques to automatically differentiate diffuse liver diseases using numerical quantitative features measured from the ultrasound images. Fuzzy rules were generated from over 140 cases consisting of normal, fatty, and cirrhotic livers. The input to the fuzzy system is an eight dimensional vector of feature values: the mean gray level (MGL), the percentile 10%, the contrast (CON), the angular second moment (ASM), the entropy (ENT), the correlation (COR), the attenuation (ATTEN) and the speckle separation. The output of the fuzzy system is one of the three categories: cirrhosis, fatty or normal. The steps done for differentiating the pathologies are data acquisition and feature extraction, dividing the input spaces of the measured quantitative data into fuzzy sets. Based on the expert knowledge, the fuzzy rules are generated and applied using the fuzzy inference procedures to determine the pathology. Different membership functions are developed for the input spaces. This approach has resulted in very good sensitivities and specificity for classifying diffused liver pathologies. This classification technique can be used in the diagnostic process, together with the history information, laboratory, clinical and pathological examinations.

Forest fire autonomous decision system based on fuzzy logic

NASA Astrophysics Data System (ADS)

Lei, Z.; Lu, Jianhua

2010-11-01

The proposed system integrates GPS / pseudolite / IMU and thermal camera in order to autonomously process the graphs by identification, extraction, tracking of forest fire or hot spots. The airborne detection platform, the graph-based algorithms and the signal processing frame are analyzed detailed; especially the rules of the decision function are expressed in terms of fuzzy logic, which is an appropriate method to express imprecise knowledge. The membership function and weights of the rules are fixed through a supervised learning process. The perception system in this paper is based on a network of sensorial stations and central stations. The sensorial stations collect data including infrared and visual images and meteorological information. The central stations exchange data to perform distributed analysis. The experiment results show that working procedure of detection system is reasonable and can accurately output the detection alarm and the computation of infrared oscillations.

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.

System for corrosion monitoring in pipeline applying fuzzy logic mathematics

NASA Astrophysics Data System (ADS)

Kuzyakov, O. N.; Kolosova, A. L.; Andreeva, M. A.

2018-05-01

A list of factors influencing corrosion rate on the external side of underground pipeline is determined. Principles of constructing a corrosion monitoring system are described; the system performance algorithm and program are elaborated. A comparative analysis of methods for calculating corrosion rate is undertaken. Fuzzy logic mathematics is applied to reduce calculations while considering a wider range of corrosion factors.

Fuzzy logic control of rotating drum bioreactor for improved production of amylase and protease enzymes by Aspergillus oryzae in solid-state fermentation.

PubMed

Sukumprasertsri, Monton; Unrean, Pornkamol; Pimsamarn, Jindarat; Kitsubun, Panit; Tongta, Anan

2013-03-01

In this study, we compared the performance of two control systems, fuzzy logic control (FLC) and conventional control (CC). The control systems were applied for controlling temperature and substrate moisture content in a solidstate fermentation for the biosynthesis of amylase and protease enzymes by Aspergillus oryzae. The fermentation process was achieved in a 200 L rotating drum bioreactor. Three factors affecting temperature and moisture content in the solid-state fermentation were considered. They were inlet air velocity, speed of the rotating drum bioreactor, and spray water addition. The fuzzy logic control system was designed using four input variables: air velocity, substrate temperature, fermentation time, and rotation speed. The temperature was controlled by two variables, inlet air velocity and rotational speed of bioreactor, while the moisture content was controlled by spray water. Experimental results confirmed that the FLC system could effectively control the temperature and moisture content of substrate better than the CC system, resulting in an increased enzyme production by A. oryzae. Thus, the fuzzy logic control is a promising control system that can be applied for enhanced production of enzymes in solidstate fermentation.

Control Law for Automatic Landing Using Fuzzy Logic Control

NASA Astrophysics Data System (ADS)

Kato, Akio; Inagaki, Yoshiki

The effectiveness of fuzzy logic control law for automatic landing of aircraft, which cover both of control to lead aircraft from level flight at an altitude of 500m to the flight on the glide-path course near the runway and control for the aircraft to land smoothly on a runway, was studied. The control law of the automatic landing was designed to match the design goals of leading from the horizontal flight to the flight on the glide-path course quickly and smoothly and of landing smoothly on a runway. Because there is the ground effect at landing, design of control law and evaluation of control performance were done in consideration of the ground effect. As a result, it was confirmed that the design objective was achieved. Even if the characteristics of the plant changes greatly, this control law was able to maintain the control performance. Moreover, it was confirmed to be able to land safely when there was air turbulence. This paper shows that fuzzy logic control is an effective and flexible method when applied to control law for automatic landing and the design method of control law using fuzzy logic control was obtained.

Performance Analysis of a Semiactive Suspension System with Particle Swarm Optimization and Fuzzy Logic Control

PubMed Central

Qazi, Abroon Jamal; de Silva, Clarence W.

2014-01-01

This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control. PMID:24574868

Genetic algorithm optimized rainfall-runoff fuzzy inference system for row crop watersheds with claypan soils

USDA-ARS?s Scientific Manuscript database

The fuzzy logic algorithm has the ability to describe knowledge in a descriptive human-like manner in the form of simple rules using linguistic variables, and provides a new way of modeling uncertain or naturally fuzzy hydrological processes like non-linear rainfall-runoff relationships. Fuzzy infe...

Multi-Criteria Decision-Making Methods and Their Applications for Human Resources

NASA Astrophysics Data System (ADS)

D'Urso, M. G.; Masi, D.

2015-05-01

Both within the formation field and the labor market Multi-Criteria Decision Methods (MCDM) provide a significant support to the management of human resources in which the best choice among several alternatives can be very complex. This contribution addresses fuzzy logic in multi-criteria decision techniques since they have several applications in the management of human resources with the advantage of ruling out mistakes due to the subjectivity of the person in charge of making a choice. Evaluating educational achievements as well as the professional profile of a technician more suitable for a job in a firm, industry or a professional office are valuable examples of fuzzy logic. For all of the previous issues subjectivity is a fundamental aspect so that fuzzy logic, due to the very meaning of the word fuzzy, should be the preferred choice. However, this is not sufficient to justify its use; fuzzy technique has to make the system of evaluation and choice more effective and objective. The methodological structure of the multi-criteria fuzzy criterion is hierarchic and allows one to select the best alternatives in all those cases in which several alternatives are possible; thus, the optimal choice can be achieved by analyzing the different scopes of each criterion and sub-criterion as well as the relevant weights.

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.

Individual Differences in Base Rate Neglect: A Fuzzy Processing Preference Index

PubMed Central

Wolfe, Christopher R.; Fisher, Christopher R.

2013-01-01

Little is known about individual differences in integrating numeric base-rates and qualitative text in making probability judgments. Fuzzy-Trace Theory predicts a preference for fuzzy processing. We conducted six studies to develop the FPPI, a reliable and valid instrument assessing individual differences in this fuzzy processing preference. It consists of 19 probability estimation items plus 4 "M-Scale" items that distinguish simple pattern matching from “base rate respect.” Cronbach's Alpha was consistently above 0.90. Validity is suggested by significant correlations between FPPI scores and three other measurers: "Rule Based" Process Dissociation Procedure scores; the number of conjunction fallacies in joint probability estimation; and logic index scores on syllogistic reasoning. Replicating norms collected in a university study with a web-based study produced negligible differences in FPPI scores, indicating robustness. The predicted relationships between individual differences in base rate respect and both conjunction fallacies and syllogistic reasoning were partially replicated in two web-based studies. PMID:23935255

Use of an Electronic Tongue System and Fuzzy Logic to Analyze Water Samples

NASA Astrophysics Data System (ADS)

Braga, Guilherme S.; Paterno, Leonardo G.; Fonseca, Fernando J.

2009-05-01

An electronic tongue (ET) system incorporating 8 chemical sensors was used in combination with two pattern recognition tools, namely principal component analysis (PCA) and Fuzzy logic for discriminating/classification of water samples from different sources (tap, distilled and three brands of mineral water). The Fuzzy program exhibited a higher accuracy than the PCA and allowed the ET to classify correctly 4 in 5 types of water. Exception was made for one brand of mineral water which was sometimes misclassified as tap water. On the other hand, the PCA grouped water samples in three clusters, one with the distilled water; a second with tap water and one brand of mineral water, and the third with the other two other brands of mineral water. Samples in the second and third clusters could not be distinguished. Nevertheless, close grouping between repeated tests indicated that the ET system response is reproducible. The potential use of the Fuzzy logic as the data processing tool in combination with an electronic tongue system is discussed.

Discriminating between intentional and unintentional gaze fixation using multimodal-based fuzzy logic algorithm for gaze tracking system with NIR camera sensor

NASA Astrophysics Data System (ADS)

Naqvi, Rizwan Ali; Park, Kang Ryoung

2016-06-01

Gaze tracking systems are widely used in human-computer interfaces, interfaces for the disabled, game interfaces, and for controlling home appliances. Most studies on gaze detection have focused on enhancing its accuracy, whereas few have considered the discrimination of intentional gaze fixation (looking at a target to activate or select it) from unintentional fixation while using gaze detection systems. Previous research methods based on the use of a keyboard or mouse button, eye blinking, and the dwell time of gaze position have various limitations. Therefore, we propose a method for discriminating between intentional and unintentional gaze fixation using a multimodal fuzzy logic algorithm applied to a gaze tracking system with a near-infrared camera sensor. Experimental results show that the proposed method outperforms the conventional method for determining gaze fixation.

Fuzzy Logic-based Intelligent Scheme for Enhancing QoS of Vertical Handover Decision in Vehicular Ad-hoc Networks

NASA Astrophysics Data System (ADS)

Azzali, F.; Ghazali, O.; Omar, M. H.

2017-08-01

The design of next generation networks in various technologies under the “Anywhere, Anytime” paradigm offers seamless connectivity across different coverage. A conventional algorithm such as RSSThreshold algorithm, that only uses the received strength signal (RSS) as a metric, will decrease handover performance regarding handover latency, delay, packet loss, and handover failure probability. Moreover, the RSS-based algorithm is only suitable for horizontal handover decision to examine the quality of service (QoS) compared to the vertical handover decision in advanced technologies. In the next generation network, vertical handover can be started based on the user’s convenience or choice rather than connectivity reasons. This study proposes a vertical handover decision algorithm that uses a Fuzzy Logic (FL) algorithm, to increase QoS performance in heterogeneous vehicular ad-hoc networks (VANET). The study uses network simulator 2.29 (NS 2.29) along with the mobility traffic network and generator to implement simulation scenarios and topologies. This helps the simulation to achieve a realistic VANET mobility scenario. The required analysis on the performance of QoS in the vertical handover can thus be conducted. The proposed Fuzzy Logic algorithm shows improvement over the conventional algorithm (RSSThreshold) in the average percentage of handover QoS whereby it achieves 20%, 21% and 13% improvement on handover latency, delay, and packet loss respectively. This is achieved through triggering a process in layer two and three that enhances the handover performance.

Active control of flexible structures using a fuzzy logic algorithm

NASA Astrophysics Data System (ADS)

Cohen, Kelly; Weller, Tanchum; Ben-Asher, Joseph Z.

2002-08-01

This study deals with the development and application of an active control law for the vibration suppression of beam-like flexible structures experiencing transient disturbances. Collocated pairs of sensors/actuators provide active control of the structure. A design methodology for the closed-loop control algorithm based on fuzzy logic is proposed. First, the behavior of the open-loop system is observed. Then, the number and locations of collocated actuator/sensor pairs are selected. The proposed control law, which is based on the principles of passivity, commands the actuator to emulate the behavior of a dynamic vibration absorber. The absorber is tuned to a targeted frequency, whereas the damping coefficient of the dashpot is varied in a closed loop using a fuzzy logic based algorithm. This approach not only ensures inherent stability associated with passive absorbers, but also circumvents the phenomenon of modal spillover. The developed controller is applied to the AFWAL/FIB 10 bar truss. Simulated results using MATLAB© show that the closed-loop system exhibits fairly quick settling times and desirable performance, as well as robustness characteristics. To demonstrate the robustness of the control system to changes in the temporal dynamics of the flexible structure, the transient response to a considerably perturbed plant is simulated. The modal frequencies of the 10 bar truss were raised as well as lowered substantially, thereby significantly perturbing the natural frequencies of vibration. For these cases, too, the developed control law provides adequate settling times and rates of vibrational energy dissipation.

Classification and Quality Evaluation of Tobacco Leaves Based on Image Processing and Fuzzy Comprehensive Evaluation

PubMed Central

Zhang, Fan; Zhang, Xinhong

2011-01-01

Most of classification, quality evaluation or grading of the flue-cured tobacco leaves are manually operated, which relies on the judgmental experience of experts, and inevitably limited by personal, physical and environmental factors. The classification and the quality evaluation are therefore subjective and experientially based. In this paper, an automatic classification method of tobacco leaves based on the digital image processing and the fuzzy sets theory is presented. A grading system based on image processing techniques was developed for automatically inspecting and grading flue-cured tobacco leaves. This system uses machine vision for the extraction and analysis of color, size, shape and surface texture. Fuzzy comprehensive evaluation provides a high level of confidence in decision making based on the fuzzy logic. The neural network is used to estimate and forecast the membership function of the features of tobacco leaves in the fuzzy sets. The experimental results of the two-level fuzzy comprehensive evaluation (FCE) show that the accuracy rate of classification is about 94% for the trained tobacco leaves, and the accuracy rate of the non-trained tobacco leaves is about 72%. We believe that the fuzzy comprehensive evaluation is a viable way for the automatic classification and quality evaluation of the tobacco leaves. PMID:22163744

Sparking-out optimization while surface grinding aluminum alloy 1933T2 parts using fuzzy logic

NASA Astrophysics Data System (ADS)

Soler, Ya I.; Salov, V. M.; Kien Nguyen, Chi

2018-03-01

The article presents the results of a search for optimal sparing-out strokes when surface grinding aluminum parts by high-porous wheels Norton of black silicon carbide 37C80K12VP using fuzzy logic. The topography of grinded surface is evaluated according to the following parameters: roughness – Ra, Rmax, Sm; indicators of flatness deviation – EFEmax, EFEa, EFEq; microhardness HV, each of these parameters is represented by two measures of position and dispersion. The simulation results of fuzzy logic in the Matlab medium establish that during the grinding of alloy 1933T2, the best integral performance evaluation of sparking-out was given to two double-strokes (d=0.827) and the worst – to three ones (d=0.405).

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.

Fuzzy logic, PSO based fuzzy logic algorithm and current controls comparative for grid-connected hybrid system

NASA Astrophysics Data System (ADS)

Borni, A.; Abdelkrim, T.; Zaghba, L.; Bouchakour, A.; Lakhdari, A.; Zarour, L.

2017-02-01

In this paper the model of a grid connected hybrid system is presented. The hybrid system includes a variable speed wind turbine controlled by aFuzzy MPPT control, and a photovoltaic generator controlled with PSO Fuzzy MPPT control to compensate the power fluctuations caused by the wind in a short and long term, the inverter currents injected to the grid is controlled by a decoupled PI current control. In the first phase, we start by modeling of the conversion system components; the wind system is consisted of a turbine coupled to a gearless permanent magnet generator (PMG), the AC/DC and DC-DC (Boost) converter are responsible to feed the electric energy produced by the PMG to the DC-link. The solar system consists of a photovoltaic generator (GPV) connected to a DC/DC boost converter controlled by a PSO fuzzy MPPT control to extract at any moment the maximum available power at the GPV terminals, the system is based on maximum utilization of both of sources because of their complementary. At the end. The active power reached to the DC-link is injected to the grid through a DC/AC inverter, this function is achieved by controlling the DC bus voltage to keep it constant and close to its reference value, The simulation studies have been performed using Matlab/Simulink. It can be concluded that a good control system performance can be achieved.

Fuzzy energy management for hybrid fuel cell/battery systems for more electric aircraft

NASA Astrophysics Data System (ADS)

Corcau, Jenica-Ileana; Dinca, Liviu; Grigorie, Teodor Lucian; Tudosie, Alexandru-Nicolae

2017-06-01

In this paper is presented the simulation and analysis of a Fuzzy Energy Management for Hybrid Fuel cell/Battery Systems used for More Electric Aircraft. The fuel cell hybrid system contains of fuel cell, lithium-ion batteries along with associated dc to dc boost converters. In this configuration the battery has a dc to dc converter, because it is an active in the system. The energy management scheme includes the rule based fuzzy logic strategy. This scheme has a faster response to load change and is more robust to measurement imprecisions. Simulation will be provided using Matlab/Simulink based models. Simulation results are given to show the overall system performance.

Fuzzy Modelling for Human Dynamics Based on Online Social Networks

PubMed Central

Cuenca-Jara, Jesus; Valdes-Vela, Mercedes; Skarmeta, Antonio F.

2017-01-01

Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities. PMID:28837120

Fuzzy Modelling for Human Dynamics Based on Online Social Networks.

PubMed

Cuenca-Jara, Jesus; Terroso-Saenz, Fernando; Valdes-Vela, Mercedes; Skarmeta, Antonio F

2017-08-24

Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities.

Visibility enhancement of color images using Type-II fuzzy membership function

NASA Astrophysics Data System (ADS)

Singh, Harmandeep; Khehra, Baljit Singh

2018-04-01

Images taken in poor environmental conditions decrease the visibility and hidden information of digital images. Therefore, image enhancement techniques are necessary for improving the significant details of these images. An extensive review has shown that histogram-based enhancement techniques greatly suffer from over/under enhancement issues. Fuzzy-based enhancement techniques suffer from over/under saturated pixels problems. In this paper, a novel Type-II fuzzy-based image enhancement technique has been proposed for improving the visibility of images. The Type-II fuzzy logic can automatically extract the local atmospheric light and roughly eliminate the atmospheric veil in local detail enhancement. The proposed technique has been evaluated on 10 well-known weather degraded color images and is also compared with four well-known existing image enhancement techniques. The experimental results reveal that the proposed technique outperforms others regarding visible edge ratio, color gradients and number of saturated pixels.

Maximum power point tracking algorithm based on sliding mode and fuzzy logic for photovoltaic sources under variable environmental conditions

NASA Astrophysics Data System (ADS)

Atik, L.; Petit, P.; Sawicki, J. P.; Ternifi, Z. T.; Bachir, G.; Della, M.; Aillerie, M.

2017-02-01

Solar panels have a nonlinear voltage-current characteristic, with a distinct maximum power point (MPP), which depends on the environmental factors, such as temperature and irradiation. In order to continuously harvest maximum power from the solar panels, they have to operate at their MPP despite the inevitable changes in the environment. Various methods for maximum power point tracking (MPPT) were developed and finally implemented in solar power electronic controllers to increase the efficiency in the electricity production originate from renewables. In this paper we compare using Matlab tools Simulink, two different MPP tracking methods, which are, fuzzy logic control (FL) and sliding mode control (SMC), considering their efficiency in solar energy production.

Self-Tuning of Design Variables for Generalized Predictive Control

NASA Technical Reports Server (NTRS)

Lin, Chaung; Juang, Jer-Nan

2000-01-01

Three techniques are introduced to determine the order and control weighting for the design of a generalized predictive controller. These techniques are based on the application of fuzzy logic, genetic algorithms, and simulated annealing to conduct an optimal search on specific performance indexes or objective functions. Fuzzy logic is found to be feasible for real-time and on-line implementation due to its smooth and quick convergence. On the other hand, genetic algorithms and simulated annealing are applicable for initial estimation of the model order and control weighting, and final fine-tuning within a small region of the solution space, Several numerical simulations for a multiple-input and multiple-output system are given to illustrate the techniques developed in this paper.

Application of fuzzy logic to the control of wind tunnel settling chamber temperature

NASA Technical Reports Server (NTRS)

Gwaltney, David A.; Humphreys, Gregory L.

1994-01-01

The application of Fuzzy Logic Controllers (FLC's) to the control of nonlinear processes, typically controlled by a human operator, is a topic of much study. Recent application of a microprocessor-based FLC to the control of temperature processes in several wind tunnels has proven to be very successful. The control of temperature processes in the wind tunnels requires the ability to monitor temperature feedback from several points and to accommodate varying operating conditions in the wind tunnels. The FLC has an intuitive and easily configurable structure which incorporates the flexibility required to have such an ability. The design and implementation of the FLC is presented along with process data from the wind tunnels under automatic control.

Flexible body control using neural networks

NASA Technical Reports Server (NTRS)

Mccullough, Claire L.

1992-01-01

Progress is reported on the control of Control Structures Interaction suitcase demonstrator (a flexible structure) using neural networks and fuzzy logic. It is concluded that while control by neural nets alone (i.e., allowing the net to design a controller with no human intervention) has yielded less than optimal results, the neural net trained to emulate the existing fuzzy logic controller does produce acceptible system responses for the initial conditions examined. Also, a neural net was found to be very successful in performing the emulation step necessary for the anticipatory fuzzy controller for the CSI suitcase demonstrator. The fuzzy neural hybrid, which exhibits good robustness and noise rejection properties, shows promise as a controller for practical flexible systems, and should be further evaluated.

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.

Extending human perception of electromagnetic radiation to the UV region through biologically inspired photochromic fuzzy logic (BIPFUL) systems.

PubMed

Gentili, Pier Luigi; Rightler, Amanda L; Heron, B Mark; Gabbutt, Christopher D

2016-01-25

Photochromic fuzzy logic systems have been designed that extend human visual perception into the UV region. The systems are founded on a detailed knowledge of the activation wavelengths and quantum yields of a series of thermally reversible photochromic compounds. By appropriate matching of the photochromic behaviour unique colour signatures are generated in response differing UV activation frequencies.

Differentiating Small (≤1 cm) Focal Liver Lesions as Metastases or Cysts by means of Computed Tomography: A Case-Study to Illustrate a Fuzzy Logic-Based Method to Assess the Impact of Diagnostic Confidence on Radiological Diagnosis

PubMed Central

Zanella, Gloria; Pullini, Serena; Como, Giuseppe; Bazzocchi, Massimo

2014-01-01

Purpose. To quantify the impact of diagnostic confidence on radiological diagnosis with a fuzzy logic-based method. Materials and Methods. Twenty-two oncologic patients with 20 cysts and 30 metastases ≤1 cm in size found at 64-row computed tomography were included. Two readers (R1/R2) expressed diagnoses as a subjective level of confidence P(d) in malignancy within the interval [0,1] rather than on a “crisp” basis (malignant/benign); confidence in benignancy was 1 − p(d). When cross-tabulating data according to the standard of reference, 2 × 2 table cells resulted from the aggregation between p(d)/1 − p(d) and final diagnosis. We then assessed (i) readers diagnostic performance on a fuzzy and crisp basis; (ii) the “divergence” δ(F, C) (%) as a measure of how confidence impacted on crisp diagnosis. Results. Diagnoses expressed with lower confidence increased fuzzy false positives compared to crisp ones (from 0 to 0.2 for R1; from 1 to 2.4 for R2). Crisp/fuzzy accuracy was 94.0%/93.6% (R1) and 94.0/91.6% (R2). δ(F, C) (%) was larger in the case of the less experienced reader (R2) (up to +7.95% for specificity). According to simulations, δ(F, C) (%) was negative/positive depending on the level of confidence in incorrect diagnoses. Conclusion. Fuzzy evaluation shows a measurable effect of uncertainty on radiological diagnoses. PMID:24587815

Handheld CZT radiation detector

DOEpatents

Murray, William S.; Butterfield, Kenneth B.; Baird, William

2004-08-24

A handheld CZT radiation detector having a CZT gamma-ray sensor, a multichannel analyzer, a fuzzy-logic component, and a display component is disclosed. The CZT gamma-ray sensor may be a coplanar grid CZT gamma-ray sensor, which provides high-quality gamma-ray analysis at a wide range of operating temperatures. The multichannel analyzer categorizes pulses produce by the CZT gamma-ray sensor into channels (discrete energy levels), resulting in pulse height data. The fuzzy-logic component analyzes the pulse height data and produces a ranked listing of radioisotopes. The fuzzy-logic component is flexible and well-suited to in-field analysis of radioisotopes. The display component may be a personal data assistant, which provides a user-friendly method of interacting with the detector. In addition, the radiation detector may be equipped with a neutron sensor to provide an enhanced mechanism of sensing radioactive materials.

Implementation Of Fuzzy Automated Brake Controller Using TSK Algorithm

NASA Astrophysics Data System (ADS)

Mittal, Ruchi; Kaur, Magandeep

2010-11-01

In this paper an application of Fuzzy Logic for Automatic Braking system is proposed. Anti-blocking system (ABS) brake controllers pose unique challenges to the designer: a) For optimal performance, the controller must operate at an unstable equilibrium point, b) Depending on road conditions, the maximum braking torque may vary over a wide range, c) The tire slippage measurement signal, crucial for controller performance, is both highly uncertain and noisy. A digital controller design was chosen which combines a fuzzy logic element and a decision logic network. The controller identifies the current road condition and generates a command braking pressure signal Depending upon the speed and distance of train. This paper describes design criteria, and the decision and rule structure of the control system. The simulation results present the system's performance depending upon the varying speed and distance of the train.

Performance of Geno-Fuzzy Model on rainfall-runoff predictions in claypan watersheds

USDA-ARS?s Scientific Manuscript database

Fuzzy logic provides a relatively simple approach to simulate complex hydrological systems while accounting for the uncertainty of environmental variables. The objective of this study was to develop a fuzzy inference system (FIS) with genetic algorithm (GA) optimization for membership functions (MF...

The gap values in the profile matching method by fuzzy logic

NASA Astrophysics Data System (ADS)

Sitepu, S. A.; Efendi, S.; Situmorang, Z.

2018-03-01

In this research, the determination of the appropriate values of Gap for the assessment of promotion criteria of position in an institution / company. In this study the authors use Fuzzy Sugeno logic on the determination of Gap values used in Profile Matching method. Test results of 5 employees obtained the eligibility of promotion with the position of Z* values between in 3.20 to 4.11.

A fuzzy-logic-based controller for methane production in anaerobic fixed-film reactors.

PubMed

Robles, A; Latrille, E; Ruano, M V; Steyer, J P

2017-01-01

The main objective of this work was to develop a controller for biogas production in continuous anaerobic fixed-bed reactors, which used effluent total volatile fatty acids (VFA) concentration as control input in order to prevent process acidification at closed loop. To this aim, a fuzzy-logic-based control system was developed, tuned and validated in an anaerobic fixed-bed reactor at pilot scale that treated industrial winery wastewater. The proposed controller varied the flow rate of wastewater entering the system as a function of the gaseous outflow rate of methane and VFA concentration. Simulation results show that the proposed controller is capable to achieve great process stability even when operating at high VFA concentrations. Pilot results showed the potential of this control approach to maintain the process working properly under similar conditions to the ones expected at full-scale plants.

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

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.

Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic Inferences

PubMed Central

Lo, Benjamin W. Y.; Macdonald, R. Loch; Baker, Andrew; Levine, Mitchell A. H.

2013-01-01

Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH). Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients). Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique) denoted cut-off points for poor prognosis at greater than 2.5 clusters. Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication. PMID:23690884

Experimental Evaluation of Fuzzy Logic Control of a Flexible Arm Manipulator

DTIC Science & Technology

1993-12-09

temperature into a fuzzy context), and humidity is musty, Then air conditioner power is high. The database and knowledge base combine to form the...this case, the output, perhaps air conditioner power, would be medium to a degree of 50%. However, as shown in Table 3.2, Oare are more possible...OF WASHINGTON AFIT/CI/CIA-93-167 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSORING/MONITORING DEPARTMENI OF THE AIR FORCE AGENCY

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.

Site selection for drinking-water pumping boreholes using a fuzzy spatial decision support system in the Korinthia prefecture, SE Greece

NASA Astrophysics Data System (ADS)

Antonakos, Andreas K.; Voudouris, Konstantinos S.; Lambrakis, Nikolaos I.

2014-12-01

The implementation of a geographic information system (GIS)/fuzzy spatial decision support system in the selection of sites for drinking-water pumping boreholes is described. Groundwater is the main source of domestic supply and irrigation in Korinthia prefecture, south-eastern Greece. Water demand has increased considerably over the last 30 years and is mainly met by groundwater abstracted via numerous wells and boreholes. The definition of the most "suitable" site for the drilling of new boreholes is a major issue in this area. A method of allocating suitable locations has been developed based on multicriteria analysis and fuzzy logic. Twelve parameters were finally involved in the model, prearranged into three categories: borehole yield, groundwater quality, and economic and technical constraints. GIS was used to create a classification map of the research area, based on the suitability of each point for the placement of new borehole fields. The coastal part of the study area is completely unsuitable, whereas high values of suitability are recorded in the south-western part. The study demonstrated that the method of multicriteria analysis in combination with fuzzy logic is a useful tool for selecting the best sites for new borehole drilling on a regional scale. The results could be used by local authorities and decision-makers for integrated groundwater resources management.

Groundwater vulnerability assessment for organic compounds: fuzzy multi-criteria approach for Mexico city.

PubMed

Mazari-Hiriart, Marisa; Cruz-Bello, Gustavo; Bojórquez-Tapia, Luis A; Juárez-Marusich, Lourdes; Alcantar-López, Georgina; Marín, Luis E; Soto-Galera, Ernesto

2006-03-01

This study was based on a groundwater vulnerability assessment approach implemented for the Mexico City Metropolitan Area (MCMA). The approach is based on a fuzzy multi-criteria procedure integrated in a geographic information system. The approach combined the potential contaminant sources with the permeability of geological materials. Initially, contaminant sources were ranked by experts through the Analytic Hierarchy Process. An aggregated contaminant sources map layer was obtained through the simple additive weighting method, using a scalar multiplication of criteria weights and binary maps showing the location of each source. A permeability map layer was obtained through the reclassification of a geology map using the respective hydraulic conductivity values, followed by a linear normalization of these values against a compatible scale. A fuzzy logic procedure was then applied to transform and combine the two map layers, resulting in a groundwater vulnerability map layer of five classes: very low, low, moderate, high, and very high. Results provided a more coherent assessment of the policy-making priorities considered when discussing the vulnerability of groundwater to organic compounds. The very high and high vulnerability areas covered a relatively small area (71 km(2) or 1.5% of the total study area), allowing the identification of the more critical locations. The advantage of a fuzzy logic procedure is that it enables the best possible use to be made of the information available regarding groundwater vulnerability in the MCMA.

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.

Earthquake hazard assessment in the Zagros Orogenic Belt of Iran using a fuzzy rule-based model

NASA Astrophysics Data System (ADS)

Farahi Ghasre Aboonasr, Sedigheh; Zamani, Ahmad; Razavipour, Fatemeh; Boostani, Reza

2017-08-01

Producing accurate seismic hazard map and predicting hazardous areas is necessary for risk mitigation strategies. In this paper, a fuzzy logic inference system is utilized to estimate the earthquake potential and seismic zoning of Zagros Orogenic Belt. In addition to the interpretability, fuzzy predictors can capture both nonlinearity and chaotic behavior of data, where the number of data is limited. In this paper, earthquake pattern in the Zagros has been assessed for the intervals of 10 and 50 years using fuzzy rule-based model. The Molchan statistical procedure has been used to show that our forecasting model is reliable. The earthquake hazard maps for this area reveal some remarkable features that cannot be observed on the conventional maps. Regarding our achievements, some areas in the southern (Bandar Abbas), southwestern (Bandar Kangan) and western (Kermanshah) parts of Iran display high earthquake severity even though they are geographically far apart.

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.

Model Based Predictive Control of Multivariable Hammerstein Processes with Fuzzy Logic Hypercube Interpolated Models

PubMed Central

Coelho, Antonio Augusto Rodrigues

2016-01-01

This paper introduces the Fuzzy Logic Hypercube Interpolator (FLHI) and demonstrates applications in control of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) processes with Hammerstein nonlinearities. FLHI consists of a Takagi-Sugeno fuzzy inference system where membership functions act as kernel functions of an interpolator. Conjunction of membership functions in an unitary hypercube space enables multivariable interpolation of N-dimensions. Membership functions act as interpolation kernels, such that choice of membership functions determines interpolation characteristics, allowing FLHI to behave as a nearest-neighbor, linear, cubic, spline or Lanczos interpolator, to name a few. The proposed interpolator is presented as a solution to the modeling problem of static nonlinearities since it is capable of modeling both a function and its inverse function. Three study cases from literature are presented, a single-input single-output (SISO) system, a MISO and a MIMO system. Good results are obtained regarding performance metrics such as set-point tracking, control variation and robustness. Results demonstrate applicability of the proposed method in modeling Hammerstein nonlinearities and their inverse functions for implementation of an output compensator with Model Based Predictive Control (MBPC), in particular Dynamic Matrix Control (DMC). PMID:27657723

GUI Type Fault Diagnostic Program for a Turboshaft Engine Using Fuzzy and Neural Networks

NASA Astrophysics Data System (ADS)

Kong, Changduk; Koo, Youngju

2011-04-01

The helicopter to be operated in a severe flight environmental condition must have a very reliable propulsion system. On-line condition monitoring and fault detection of the engine can promote reliability and availability of the helicopter propulsion system. A hybrid health monitoring program using Fuzzy Logic and Neural Network Algorithms can be proposed. In this hybrid method, the Fuzzy Logic identifies easily the faulted components from engine measuring parameter changes, and the Neural Networks can quantify accurately its identified faults. In order to use effectively the fault diagnostic system, a GUI (Graphical User Interface) type program is newly proposed. This program is composed of the real time monitoring part, the engine condition monitoring part and the fault diagnostic part. The real time monitoring part can display measuring parameters of the study turboshaft engine such as power turbine inlet temperature, exhaust gas temperature, fuel flow, torque and gas generator speed. The engine condition monitoring part can evaluate the engine condition through comparison between monitoring performance parameters the base performance parameters analyzed by the base performance analysis program using look-up tables. The fault diagnostic part can identify and quantify the single faults the multiple faults from the monitoring parameters using hybrid method.

A wavelet-fuzzy logic based energy management strategy for a fuel cell/battery/ultra-capacitor hybrid vehicular power system

NASA Astrophysics Data System (ADS)

Erdinc, O.; Vural, B.; Uzunoglu, M.

Due to increasing concerns on environmental pollution and depleting fossil fuels, fuel cell (FC) vehicle technology has received considerable attention as an alternative to the conventional vehicular systems. However, a FC system combined with an energy storage system (ESS) can display a preferable performance for vehicle propulsion. As the additional ESS can fulfill the transient power demand fluctuations, the fuel cell can be downsized to fit the average power demand without facing peak loads. Besides, braking energy can be recovered by the ESS. This study focuses on a vehicular system powered by a fuel cell and equipped with two secondary energy storage devices: battery and ultra-capacitor (UC). However, an advanced energy management strategy is quite necessary to split the power demand of a vehicle in a suitable way for the on-board power sources in order to maximize the performance while promoting the fuel economy and endurance of hybrid system components. In this study, a wavelet and fuzzy logic based energy management strategy is proposed for the developed hybrid vehicular system. Wavelet transform has great capability for analyzing signals consisting of instantaneous changes like a hybrid electric vehicle (HEV) power demand. Besides, fuzzy logic has a quite suitable structure for the control of hybrid systems. The mathematical and electrical models of the hybrid vehicular system are developed in detail and simulated using MATLAB ®, Simulink ® and SimPowerSystems ® environments.

A Numerical Optimization Approach for Tuning Fuzzy Logic Controllers

NASA Technical Reports Server (NTRS)

Woodard, Stanley E.; Garg, Devendra P.

1998-01-01

This paper develops a method to tune fuzzy controllers using numerical optimization. The main attribute of this approach is that it allows fuzzy logic controllers to be tuned to achieve global performance requirements. Furthermore, this approach allows design constraints to be implemented during the tuning process. The method tunes the controller by parameterizing the membership functions for error, change-in-error and control output. The resulting parameters form a design vector which is iteratively changed to minimize an objective function. The minimal objective function results in an optimal performance of the system. A spacecraft mounted science instrument line-of-sight pointing control is used to demonstrate results.

DFP: a Bioconductor package for fuzzy profile identification and gene reduction of microarray data

PubMed Central

Glez-Peña, Daniel; Álvarez, Rodrigo; Díaz, Fernando; Fdez-Riverola, Florentino

2009-01-01

Background Expression profiling assays done by using DNA microarray technology generate enormous data sets that are not amenable to simple analysis. The greatest challenge in maximizing the use of this huge amount of data is to develop algorithms to interpret and interconnect results from different genes under different conditions. In this context, fuzzy logic can provide a systematic and unbiased way to both (i) find biologically significant insights relating to meaningful genes, thereby removing the need for expert knowledge in preliminary steps of microarray data analyses and (ii) reduce the cost and complexity of later applied machine learning techniques being able to achieve interpretable models. Results DFP is a new Bioconductor R package that implements a method for discretizing and selecting differentially expressed genes based on the application of fuzzy logic. DFP takes advantage of fuzzy membership functions to assign linguistic labels to gene expression levels. The technique builds a reduced set of relevant genes (FP, Fuzzy Pattern) able to summarize and represent each underlying class (pathology). A last step constructs a biased set of genes (DFP, Discriminant Fuzzy Pattern) by intersecting existing fuzzy patterns in order to detect discriminative elements. In addition, the software provides new functions and visualisation tools that summarize achieved results and aid in the interpretation of differentially expressed genes from multiple microarray experiments. Conclusion DFP integrates with other packages of the Bioconductor project, uses common data structures and is accompanied by ample documentation. It has the advantage that its parameters are highly configurable, facilitating the discovery of biologically relevant connections between sets of genes belonging to different pathologies. This information makes it possible to automatically filter irrelevant genes thereby reducing the large volume of data supplied by microarray experiments. Based on these contributions GENECBR, a successful tool for cancer diagnosis using microarray datasets, has recently been released. PMID:19178723

DFP: a Bioconductor package for fuzzy profile identification and gene reduction of microarray data.

PubMed

Glez-Peña, Daniel; Alvarez, Rodrigo; Díaz, Fernando; Fdez-Riverola, Florentino

2009-01-29

Expression profiling assays done by using DNA microarray technology generate enormous data sets that are not amenable to simple analysis. The greatest challenge in maximizing the use of this huge amount of data is to develop algorithms to interpret and interconnect results from different genes under different conditions. In this context, fuzzy logic can provide a systematic and unbiased way to both (i) find biologically significant insights relating to meaningful genes, thereby removing the need for expert knowledge in preliminary steps of microarray data analyses and (ii) reduce the cost and complexity of later applied machine learning techniques being able to achieve interpretable models. DFP is a new Bioconductor R package that implements a method for discretizing and selecting differentially expressed genes based on the application of fuzzy logic. DFP takes advantage of fuzzy membership functions to assign linguistic labels to gene expression levels. The technique builds a reduced set of relevant genes (FP, Fuzzy Pattern) able to summarize and represent each underlying class (pathology). A last step constructs a biased set of genes (DFP, Discriminant Fuzzy Pattern) by intersecting existing fuzzy patterns in order to detect discriminative elements. In addition, the software provides new functions and visualisation tools that summarize achieved results and aid in the interpretation of differentially expressed genes from multiple microarray experiments. DFP integrates with other packages of the Bioconductor project, uses common data structures and is accompanied by ample documentation. It has the advantage that its parameters are highly configurable, facilitating the discovery of biologically relevant connections between sets of genes belonging to different pathologies. This information makes it possible to automatically filter irrelevant genes thereby reducing the large volume of data supplied by microarray experiments. Based on these contributions GENECBR, a successful tool for cancer diagnosis using microarray datasets, has recently been released.

A fuzzy logic controller for hormone administration using an implantable pump

NASA Technical Reports Server (NTRS)

Coles, L. Stephen; Wells, George H., Jr.

1994-01-01

This paper describes the requirements for a Fuzzy Logic Controller for the physiologic administration of hormones by means of a FDA-approved surgically implantable infusion pump. Results of a LabVIEW computer simulation for the administration of insulin for diabetic adult patients as well as human growth hormone for pediatric patients are presented. A VHS video tape of the simulation in action has been prepared and is available for viewing.

Intelligent system design for bionanorobots in drug delivery.

PubMed

Fletcher, Mark; Biglarbegian, Mohammad; Neethirajan, Suresh

A nanorobot is defined as any smart structure which is capable of actuation, sensing, manipulation, intelligence, and swarm behavior at the nanoscale. In this study, we designed an intelligent system using fuzzy logic for diagnosis and treatment of tumors inside the human body using bionanorobots. We utilize fuzzy logic and a combination of thermal, magnetic, optical, and chemical nanosensors to interpret the uncertainty associated with the sensory information. Two different fuzzy logic structures, for diagnosis (Mamdani structure) and for cure (Takagi-Sugeno structure), were developed to efficiently identify the tumors and treat them through delivery of effective dosages of a drug. Validation of the designed system with simulated conditions proved that the drug delivery of bionanorobots was robust to reasonable noise that may occur in the bionanorobot sensors during navigation, diagnosis, and curing of the cancer cells. Bionanorobots represent a great hope for successful cancer therapy in the near future.

Towards the Application of Fuzzy Logic for Developing a Novel Indoor Air Quality Index (FIAQI).

PubMed

Javid, Allahbakhsh; Hamedian, Amir Abbas; Gharibi, Hamed; Sowlat, Mohammad Hossein

2016-02-01

In the past few decades, Indoor Air Pollution (IAP) has become a primary concern to the point. It is increasingly believed to be of equal or greater importance to human health compared to ambient air. However, due to the lack of comprehensive indices for the integrated assessment of indoor air quality (IAQ), we aimed to develop a novel, Fuzzy-Based Indoor Air Quality Index (FIAQI) to bridge the existing gap in this area. We based our index on fuzzy logic, which enables us to overcome the limitations of traditional methods applied to develop environmental quality indices. Fifteen parameters, including the criteria air pollutants, volatile organic compounds, and bioaerosols were included in the FIAQI due mainly to their significant health effects. Weighting factors were assigned to the parameters based on the medical evidence available in the literature on their health effects. The final FIAQI consisted of 108 rules. In order to demonstrate the performance of the index, data were intentionally generated to cover a variety of quality levels. In addition, a sensitivity analysis was conducted to assess the validity of the index. The FIAQI tends to be a comprehensive tool to classify IAQ and produce accurate results. It seems useful and reliable to be considered by authorities to assess IAQ environments.

Idiopathic interstitial pneumonias and emphysema: detection and classification using a texture-discriminative approach

NASA Astrophysics Data System (ADS)

Fetita, C.; Chang-Chien, K. C.; Brillet, P. Y.; Pr"teux, F.; Chang, R. F.

2012-03-01

Our study aims at developing a computer-aided diagnosis (CAD) system for fully automatic detection and classification of pathological lung parenchyma patterns in idiopathic interstitial pneumonias (IIP) and emphysema using multi-detector computed tomography (MDCT). The proposed CAD system is based on three-dimensional (3-D) mathematical morphology, texture and fuzzy logic analysis, and can be divided into four stages: (1) a multi-resolution decomposition scheme based on a 3-D morphological filter was exploited to discriminate the lung region patterns at different analysis scales. (2) An additional spatial lung partitioning based on the lung tissue texture was introduced to reinforce the spatial separation between patterns extracted at the same resolution level in the decomposition pyramid. Then, (3) a hierarchic tree structure was exploited to describe the relationship between patterns at different resolution levels, and for each pattern, six fuzzy membership functions were established for assigning a probability of association with a normal tissue or a pathological target. Finally, (4) a decision step exploiting the fuzzy-logic assignments selects the target class of each lung pattern among the following categories: normal (N), emphysema (EM), fibrosis/honeycombing (FHC), and ground glass (GDG). According to a preliminary evaluation on an extended database, the proposed method can overcome the drawbacks of a previously developed approach and achieve higher sensitivity and specificity.

Towards the Application of Fuzzy Logic for Developing a Novel Indoor Air Quality Index (FIAQI)

PubMed Central

JAVID, Allahbakhsh; HAMEDIAN, Amir Abbas; GHARIBI, Hamed; SOWLAT, Mohammad Hossein

2016-01-01

Background: In the past few decades, Indoor Air Pollution (IAP) has become a primary concern to the point. It is increasingly believed to be of equal or greater importance to human health compared to ambient air. However, due to the lack of comprehensive indices for the integrated assessment of indoor air quality (IAQ), we aimed to develop a novel, Fuzzy-Based Indoor Air Quality Index (FIAQI) to bridge the existing gap in this area. Methods: We based our index on fuzzy logic, which enables us to overcome the limitations of traditional methods applied to develop environmental quality indices. Fifteen parameters, including the criteria air pollutants, volatile organic compounds, and bioaerosols were included in the FIAQI due mainly to their significant health effects. Weighting factors were assigned to the parameters based on the medical evidence available in the literature on their health effects. The final FIAQI consisted of 108 rules. In order to demonstrate the performance of the index, data were intentionally generated to cover a variety of quality levels. In addition, a sensitivity analysis was conducted to assess the validity of the index. Results: The FIAQI tends to be a comprehensive tool to classify IAQ and produce accurate results. Conclusion: It seems useful and reliable to be considered by authorities to assess IAQ environments. PMID:27114985

High dimensional model representation method for fuzzy structural dynamics

NASA Astrophysics Data System (ADS)

Adhikari, S.; Chowdhury, R.; Friswell, M. I.

2011-03-01

Uncertainty propagation in multi-parameter complex structures possess significant computational challenges. This paper investigates the possibility of using the High Dimensional Model Representation (HDMR) approach when uncertain system parameters are modeled using fuzzy variables. In particular, the application of HDMR is proposed for fuzzy finite element analysis of linear dynamical systems. The HDMR expansion is an efficient formulation for high-dimensional mapping in complex systems if the higher order variable correlations are weak, thereby permitting the input-output relationship behavior to be captured by the terms of low-order. The computational effort to determine the expansion functions using the α-cut method scales polynomically with the number of variables rather than exponentially. This logic is based on the fundamental assumption underlying the HDMR representation that only low-order correlations among the input variables are likely to have significant impacts upon the outputs for most high-dimensional complex systems. The proposed method is first illustrated for multi-parameter nonlinear mathematical test functions with fuzzy variables. The method is then integrated with a commercial finite element software (ADINA). Modal analysis of a simplified aircraft wing with fuzzy parameters has been used to illustrate the generality of the proposed approach. In the numerical examples, triangular membership functions have been used and the results have been validated against direct Monte Carlo simulations. It is shown that using the proposed HDMR approach, the number of finite element function calls can be reduced without significantly compromising the accuracy.

Fuzzy-logic detection and probability of hail exploiting short-range X-band weather radar

NASA Astrophysics Data System (ADS)

Capozzi, Vincenzo; Picciotti, Errico; Mazzarella, Vincenzo; Marzano, Frank Silvio; Budillon, Giorgio

2018-03-01

This work proposes a new method for hail precipitation detection and probability, based on single-polarization X-band radar measurements. Using a dataset consisting of reflectivity volumes, ground truth observations and atmospheric sounding data, a probability of hail index, which provides a simple estimate of the hail potential, has been trained and adapted within Naples metropolitan environment study area. The probability of hail has been calculated starting by four different hail detection methods. The first two, based on (1) reflectivity data and temperature measurements and (2) on vertically-integrated liquid density product, respectively, have been selected from the available literature. The other two techniques are based on combined criteria of the above mentioned methods: the first one (3) is based on the linear discriminant analysis, whereas the other one (4) relies on the fuzzy-logic approach. The latter is an innovative criterion based on a fuzzyfication step performed through ramp membership functions. The performances of the four methods have been tested using an independent dataset: the results highlight that the fuzzy-oriented combined method performs slightly better in terms of false alarm ratio, critical success index and area under the relative operating characteristic. An example of application of the proposed hail detection and probability products is also presented for a relevant hail event, occurred on 21 July 2014.

Novel intelligent real-time position tracking system using FPGA and fuzzy logic.

PubMed

Soares dos Santos, Marco P; Ferreira, J A F

2014-03-01

The main aim of this paper is to test if FPGAs are able to achieve better position tracking performance than software-based soft real-time platforms. For comparison purposes, the same controller design was implemented in these architectures. A Multi-state Fuzzy Logic controller (FLC) was implemented both in a Xilinx(®) Virtex-II FPGA (XC2v1000) and in a soft real-time platform NI CompactRIO(®)-9002. The same sampling time was used. The comparative tests were conducted using a servo-pneumatic actuation system. Steady-state errors lower than 4 μm were reached for an arbitrary vertical positioning of a 6.2 kg mass when the controller was embedded into the FPGA platform. Performance gains up to 16 times in the steady-state error, up to 27 times in the overshoot and up to 19.5 times in the settling time were achieved by using the FPGA-based controller over the software-based FLC controller. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Robust Fault Detection for Aircraft Using Mixed Structured Singular Value Theory and Fuzzy Logic

NASA Technical Reports Server (NTRS)

Collins, Emmanuel G.

2000-01-01

The purpose of fault detection is to identify when a fault or failure has occurred in a system such as an aircraft or expendable launch vehicle. The faults may occur in sensors, actuators, structural components, etc. One of the primary approaches to model-based fault detection relies on analytical redundancy. That is the output of a computer-based model (actually a state estimator) is compared with the sensor measurements of the actual system to determine when a fault has occurred. Unfortunately, the state estimator is based on an idealized mathematical description of the underlying plant that is never totally accurate. As a result of these modeling errors, false alarms can occur. This research uses mixed structured singular value theory, a relatively recent and powerful robustness analysis tool, to develop robust estimators and demonstrates the use of these estimators in fault detection. To allow qualitative human experience to be effectively incorporated into the detection process fuzzy logic is used to predict the seriousness of the fault that has occurred.

Fuzzy logic-based approach to detecting a passive RFID tag in an outpatient clinic.

PubMed

Min, Daiki; Yih, Yuehwern

2011-06-01

This study is motivated by the observations on the data collected by radio frequency identification (RFID) readers in a pilot study, which was used to investigate the feasibility of implementing an RFID-based monitoring system in an outpatient eye clinic. The raw RFID data collected from RFID readers contain noise and missing reads, which prevent us from determining the tag location. In this paper, fuzzy logic-based algorithms are proposed to interpret the raw RFID data to extract accurate information. The proposed algorithms determine the location of an RFID tag by evaluating its possibility of presence and absence. To evaluate the performance of the proposed algorithms, numerical experiments are conducted using the data observed in the outpatient eye clinic. Experiments results showed that the proposed algorithms outperform existing static smoothing method in terms of minimizing both false positives and false negatives. Furthermore, the proposed algorithms are applied to a set of simulated data to show the robustness of the proposed algorithms at various levels of RFID reader reliability.

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.

Fuzzy Rule Suram for Wood Drying

NASA Astrophysics Data System (ADS)

Situmorang, Zakarias

2017-12-01

Implemented of fuzzy rule must used a look-up table as defuzzification analysis. Look-up table is the actuator plant to doing the value of fuzzification. Rule suram based of fuzzy logic with variables of weather is temperature ambient and humidity ambient, it implemented for wood drying process. The membership function of variable of state represented in error value and change error with typical map of triangle and map of trapezium. Result of analysis to reach 4 fuzzy rule in 81 conditions to control the output system can be constructed in a number of way of weather and conditions of air. It used to minimum of the consumption of electric energy by heater. One cycle of schedule drying is a serial of condition of chamber to process as use as a wood species.

Fuzzy regression modeling for tool performance prediction and degradation detection.

PubMed

Li, X; Er, M J; Lim, B S; Zhou, J H; Gan, O P; Rutkowski, L

2010-10-01

In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study - namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52-54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.

Ontology-Based Learner Categorization through Case Based Reasoning and Fuzzy Logic

ERIC Educational Resources Information Center

Sarwar, Sohail; García-Castro, Raul; Qayyum, Zia Ul; Safyan, Muhammad; Munir, Rana Faisal

2017-01-01

Learner categorization has a pivotal role in making e-learning systems a success. However, learner characteristics exploited at abstract level of granularity by contemporary techniques cannot categorize the learners effectively. In this paper, an architecture of e-learning framework has been presented that exploits the machine learning based…

Fuzzy health, illness, and disease.

PubMed

Sadegh-Zadeh, K

2000-10-01

The notions of health, illness, and disease are fuzzy-theoretically analyzed. They present themselves as non-Aristotelian concepts violating basic principles of classical logic. A recursive scheme for defining the controversial notion of disease is proposed that also supports a concept of fuzzy disease. A sketch is given of the prototype resemblance theory of disease.

Neuro-fuzzy controller to navigate an unmanned vehicle.

PubMed

Selma, Boumediene; Chouraqui, Samira

2013-12-01

A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN).

Path planning of decentralized multi-quadrotor based on fuzzy-cell decomposition algorithm

NASA Astrophysics Data System (ADS)

Iswanto, Wahyunggoro, Oyas; Cahyadi, Adha Imam

2017-04-01

The paper aims to present a design algorithm for multi quadrotor lanes in order to move towards the goal quickly and avoid obstacles in an area with obstacles. There are several problems in path planning including how to get to the goal position quickly and avoid static and dynamic obstacles. To overcome the problem, therefore, the paper presents fuzzy logic algorithm and fuzzy cell decomposition algorithm. Fuzzy logic algorithm is one of the artificial intelligence algorithms which can be applied to robot path planning that is able to detect static and dynamic obstacles. Cell decomposition algorithm is an algorithm of graph theory used to make a robot path map. By using the two algorithms the robot is able to get to the goal position and avoid obstacles but it takes a considerable time because they are able to find the shortest path. Therefore, this paper describes a modification of the algorithms by adding a potential field algorithm used to provide weight values on the map applied for each quadrotor by using decentralized controlled, so that the quadrotor is able to move to the goal position quickly by finding the shortest path. The simulations conducted have shown that multi-quadrotor can avoid various obstacles and find the shortest path by using the proposed algorithms.

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.

Optimal operating rules definition in complex water resource systems combining fuzzy logic, expert criteria and stochastic programming

NASA Astrophysics Data System (ADS)

Macian-Sorribes, Hector; Pulido-Velazquez, Manuel

2016-04-01

This contribution presents a methodology for defining optimal seasonal operating rules in multireservoir systems coupling expert criteria and stochastic optimization. Both sources of information are combined using fuzzy logic. The structure of the operating rules is defined based on expert criteria, via a joint expert-technician framework consisting in a series of meetings, workshops and surveys carried out between reservoir managers and modelers. As a result, the decision-making process used by managers can be assessed and expressed using fuzzy logic: fuzzy rule-based systems are employed to represent the operating rules and fuzzy regression procedures are used for forecasting future inflows. Once done that, a stochastic optimization algorithm can be used to define optimal decisions and transform them into fuzzy rules. Finally, the optimal fuzzy rules and the inflow prediction scheme are combined into a Decision Support System for making seasonal forecasts and simulate the effect of different alternatives in response to the initial system state and the foreseen inflows. The approach presented has been applied to the Jucar River Basin (Spain). Reservoir managers explained how the system is operated, taking into account the reservoirs' states at the beginning of the irrigation season and the inflows previewed during that season. According to the information given by them, the Jucar River Basin operating policies were expressed via two fuzzy rule-based (FRB) systems that estimate the amount of water to be allocated to the users and how the reservoir storages should be balanced to guarantee those deliveries. A stochastic optimization model using Stochastic Dual Dynamic Programming (SDDP) was developed to define optimal decisions, which are transformed into optimal operating rules embedding them into the two FRBs previously created. As a benchmark, historical records are used to develop alternative operating rules. A fuzzy linear regression procedure was employed to foresee future inflows depending on present and past hydrological and meteorological variables actually used by the reservoir managers to define likely inflow scenarios. A Decision Support System (DSS) was created coupling the FRB systems and the inflow prediction scheme in order to give the user a set of possible optimal releases in response to the reservoir states at the beginning of the irrigation season and the fuzzy inflow projections made using hydrological and meteorological information. The results show that the optimal DSS created using the FRB operating policies are able to increase the amount of water allocated to the users in 20 to 50 Mm3 per irrigation season with respect to the current policies. Consequently, the mechanism used to define optimal operating rules and transform them into a DSS is able to increase the water deliveries in the Jucar River Basin, combining expert criteria and optimization algorithms in an efficient way. This study has been partially supported by the IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO (Ministerio de Economía y Competitividad) and FEDER funds. It also has received funding from the European Union's Horizon 2020 research and innovation programme under the IMPREX project (grant agreement no: 641.811).

Profitability analysis of a femtosecond laser system for cataract surgery using a fuzzy logic approach.

PubMed

Trigueros, José Antonio; Piñero, David P; Ismail, Mahmoud M

2016-01-01

To define the financial and management conditions required to introduce a femtosecond laser system for cataract surgery in a clinic using a fuzzy logic approach. In the simulation performed in the current study, the costs associated to the acquisition and use of a commercially available femtosecond laser platform for cataract surgery (VICTUS, TECHNOLAS Perfect Vision GmbH, Bausch & Lomb, Munich, Germany) during a period of 5y were considered. A sensitivity analysis was performed considering such costs and the countable amortization of the system during this 5y period. Furthermore, a fuzzy logic analysis was used to obtain an estimation of the money income associated to each femtosecond laser-assisted cataract surgery (G). According to the sensitivity analysis, the femtosecond laser system under evaluation can be profitable if 1400 cataract surgeries are performed per year and if each surgery can be invoiced more than $500. In contrast, the fuzzy logic analysis confirmed that the patient had to pay more per surgery, between $661.8 and $667.4 per surgery, without considering the cost of the intraocular lens (IOL). A profitability of femtosecond laser systems for cataract surgery can be obtained after a detailed financial analysis, especially in those centers with large volumes of patients. The cost of the surgery for patients should be adapted to the real flow of patients with the ability of paying a reasonable range of cost.

Application of fuzzy logic approach for wind erosion hazard mapping in Laghouat region (Algeria) using remote sensing and GIS

NASA Astrophysics Data System (ADS)

Saadoud, Djouher; Hassani, Mohamed; Martin Peinado, Francisco José; Guettouche, Mohamed Saïd

2018-06-01

Wind erosion is one of the most serious environmental problems in Algeria that threatens human activities and socio-economic development. The main goal of this study is to apply a fuzzy logic approach to wind erosion sensitivity mapping in the Laghouat region, Algeria. Six causative factors, obtained by applying fuzzy membership functions to each used parameter, are considered: soil, vegetation cover, wind factor, soil dryness, land topography and land cover sensitivity. Different fuzzy operators (AND, OR, SUM, PRODUCT, and GAMMA) are applied to generate wind-erosion hazard map. Success rate curves reveal that the fuzzy gamma (γ) operator, with γ equal to 0.9, gives the best prediction accuracy with an area under curve of 85.2%. The resulting wind-erosion sensitivity map delineates the area into different zones of five relative sensitivity classes: very high, high, moderate, low and very low. The estimated result was verified by field measurements and the high statistically significant value of a chi-square test.

Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli

PubMed Central

Morris, Melody K.; Saez-Rodriguez, Julio; Clarke, David C.; Sorger, Peter K.; Lauffenburger, Douglas A.

2011-01-01

Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone. PMID:21408212

A Fuzzy-Decision Based Approach for Composite Event Detection in Wireless Sensor Networks

PubMed Central

Zhang, Shukui; Chen, Hao; Zhu, Qiaoming

2014-01-01

The event detection is one of the fundamental researches in wireless sensor networks (WSNs). Due to the consideration of various properties that reflect events status, the Composite event is more consistent with the objective world. Thus, the research of the Composite event becomes more realistic. In this paper, we analyze the characteristics of the Composite event; then we propose a criterion to determine the area of the Composite event and put forward a dominating set based network topology construction algorithm under random deployment. For the unreliability of partial data in detection process and fuzziness of the event definitions in nature, we propose a cluster-based two-dimensional τ-GAS algorithm and fuzzy-decision based composite event decision mechanism. In the case that the sensory data of most nodes are normal, the two-dimensional τ-GAS algorithm can filter the fault node data effectively and reduce the influence of erroneous data on the event determination. The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic. PMID:25136690

Multi-objective design of fuzzy logic controller in supply chain

NASA Astrophysics Data System (ADS)

Ghane, Mahdi; Tarokh, Mohammad Jafar

2012-08-01

Unlike commonly used methods, in this paper, we have introduced a new approach for designing fuzzy controllers. In this approach, we have simultaneously optimized both objective functions of a supply chain over a two-dimensional space. Then, we have obtained a spectrum of optimized points, each of which represents a set of optimal parameters which can be chosen by the manager according to the importance of objective functions. Our used supply chain model is a member of inventory and order-based production control system family, a generalization of the periodic review which is termed `Order-Up-To policy.' An auto rule maker, based on non-dominated sorting genetic algorithm-II, has been applied to the experimental initial fuzzy rules. According to performance measurement, our results indicate the efficiency of the proposed approach.

Application of multi response optimization with grey relational analysis and fuzzy logic method

NASA Astrophysics Data System (ADS)

Winarni, Sri; Wahyu Indratno, Sapto

2018-01-01

Multi-response optimization is an optimization process by considering multiple responses simultaneously. The purpose of this research is to get the optimum point on multi-response optimization process using grey relational analysis and fuzzy logic method. The optimum point is determined from the Fuzzy-GRG (Grey Relational Grade) variable which is the conversion of the Signal to Noise Ratio of the responses involved. The case study used in this research are case optimization of electrical process parameters in electrical disharge machining. It was found that the combination of treatments resulting to optimum MRR and SR was a 70 V gap voltage factor, peak current 9 A and duty factor 0.8.

Application of fuzzy logic in multicomponent analysis by optodes.

PubMed

Wollenweber, M; Polster, J; Becker, T; Schmidt, H L

1997-01-01

Fuzzy logic can be a useful tool for the determination of substrate concentrations applying optode arrays in combination with flow injection analysis, UV-VIS spectroscopy and kinetics. The transient diffuse reflectance spectra in the visible wavelength region from four optodes were evaluated to carry out the simultaneous determination of artificial mixtures of ampicillin and penicillin. The discrimination of the samples was achieved by changing the composition of the receptor gel and working pH. Different algorithms of pre-processing were applied on the data to reduce the spectral information to a few analytic-specific variables. These variables were used to develop the fuzzy model. After calibration the model was validated by an independent test data set.

A neural network architecture for implementation of expert systems for real time monitoring

NASA Technical Reports Server (NTRS)

Ramamoorthy, P. A.

1991-01-01

Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

NASA/ARC proposed training in intelligent control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1990-01-01

Viewgraphs on NASA Ames Research Center proposed training in intelligent control was presented. Topics covered include: fuzzy logic control; neural networks in control; artificial intelligence in control; hybrid approaches; hands on experience; and fuzzy controllers.

Coastal vulnerability assessment using Fuzzy Logic and Bayesian Belief Network approaches

NASA Astrophysics Data System (ADS)

Valentini, Emiliana; Nguyen Xuan, Alessandra; Filipponi, Federico; Taramelli, Andrea

2017-04-01

Natural hazards such as sea surge are threatening low-lying coastal plains. In order to deal with disturbances a deeper understanding of benefits deriving from ecosystem services assessment, management and planning can contribute to enhance the resilience of coastal systems. In this frame assessing current and future vulnerability is a key concern of many Systems Of Systems SOS (social, ecological, institutional) that deals with several challenges like the definition of Essential Variables (EVs) able to synthesize the required information, the assignment of different weight to be attributed to each considered variable, the selection of method for combining the relevant variables. It is widely recognized that ecosystems contribute to human wellbeing and then their conservation increases the resilience capacities and could play a key role in reducing climate related risk and thus physical and economic losses. A way to fully exploit ecosystems potential, i.e. their so called ecopotential (see H2020 EU funded project "ECOPOTENTIAL"), is the Ecosystem based Adaptation (EbA): the use of ecosystem services as part of an adaptation strategy. In order to provide insight in understanding regulating ecosystem services to surge and which variables influence them and to make the best use of available data and information (EO products, in situ data and modelling), we propose a multi-component surge vulnerability assessment, focusing on coastal sandy dunes as natural barriers. The aim is to combine together eco-geomorphological and socio-economic variables with the hazard component on the base of different approaches: 1) Fuzzy Logic; 2) Bayesian Belief Networks (BBN). The Fuzzy Logic approach is very useful to get a spatialized information and it can easily combine variables coming from different sources. It provides information on vulnerability moving along-shore and across-shore (beach-dune transect), highlighting the variability of vulnerability conditions in the spatial dimension. According to the results using fuzzy operators, the analysis greatest weakness is the limited capacity to represent the relation among the different considered variables. The BBN approach, based on the definition of conditional probabilities, has allowed determining the trend of distributions of vulnerability along-shore, highlighting which parts of the coast are most likely to have higher or lower vulnerability than others. In BBN analysis, the greatest weakness emerge in the case of arbitrary definition of conditional probabilities (i.e. when there is a lack of information on the past hazardous events) because it is not possible to derive the individual contribution of each variable. As conclusion, the two approaches could be used together in the perspective of enhancing the multiple components in vulnerability assessment: the BBN as a preliminary assessment to provide a coarse description of the vulnerability distribution, and the Fuzzy Logic as an extended assessment to provide more space based information.

Multi-Source Sensor Fusion for Small Unmanned Aircraft Systems Using Fuzzy Logic

NASA Technical Reports Server (NTRS)

Cook, Brandon; Cohen, Kelly

2017-01-01

As the applications for using small Unmanned Aircraft Systems (sUAS) beyond visual line of sight (BVLOS) continue to grow in the coming years, it is imperative that intelligent sensor fusion techniques be explored. In BVLOS scenarios the vehicle position must accurately be tracked over time to ensure no two vehicles collide with one another, no vehicle crashes into surrounding structures, and to identify off-nominal scenarios. Therefore, in this study an intelligent systems approach is used to estimate the position of sUAS given a variety of sensor platforms, including, GPS, radar, and on-board detection hardware. Common research challenges include, asynchronous sensor rates and sensor reliability. In an effort to realize these challenges, techniques such as a Maximum a Posteriori estimation and a Fuzzy Logic based sensor confidence determination are used.

Development of an Expert System as a Diagnostic Support of Cervical Cancer in Atypical Glandular Cells, Based on Fuzzy Logics and Image Interpretation

PubMed Central

Domínguez Hernández, Karem R.; Aguilar Lasserre, Alberto A.; Posada Gómez, Rubén; Palet Guzmán, José A.; González Sánchez, Blanca E.

2013-01-01

Cervical cancer is the second largest cause of death among women worldwide. Nowadays, this disease is preventable and curable at low cost and low risk when an accurate diagnosis is done in due time, since it is the neoplasm with the highest prevention potential. This work describes the development of an expert system able to provide a diagnosis to cervical neoplasia (CN) precursor injuries through the integration of fuzzy logics and image interpretation techniques. The key contribution of this research focuses on atypical cases, specifically on atypical glandular cells (AGC). The expert system consists of 3 phases: (1) risk diagnosis which consists of the interpretation of a patient's clinical background and the risks for contracting CN according to specialists; (2) cytology images detection which consists of image interpretation (IM) and the Bethesda system for cytology interpretation, and (3) determination of cancer precursor injuries which consists of in retrieving the information from the prior phases and integrating the expert system by means of a fuzzy logics (FL) model. During the validation stage of the system, 21 already diagnosed cases were tested with a positive correlation in which 100% effectiveness was obtained. The main contribution of this work relies on the reduction of false positives and false negatives by providing a more accurate diagnosis for CN. PMID:23690881

Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

NASA Astrophysics Data System (ADS)

Kong, Changduk; Lim, Semyeong

2011-12-01

Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.

NetWeaver for EMDS user guide (version 1.1): a knowledge base development system.

Treesearch

Keith M. Reynolds

1999-01-01

The guide describes use of the NetWeaver knowledge base development system. Knowledge representation in NetWeaver is based on object-oriented fuzzy-logic networks that offer several significant advantages over the more traditional rulebased representation. Compared to rule-based knowledge bases, NetWeaver knowledge bases are easier to build, test, and maintain because...

Hardware implementation of fuzzy Petri net as a controller.

PubMed

Gniewek, Lesław; Kluska, Jacek

2004-06-01

The paper presents a new approach to fuzzy Petri net (FPN) and its hardware implementation. The authors' motivation is as follows. Complex industrial processes can be often decomposed into many parallelly working subprocesses, which can, in turn, be modeled using Petri nets. If all the process variables (or events) are assumed to be two-valued signals, then it is possible to obtain a hardware or software control device, which works according to the algorithm described by conventional Petri net. However, the values of real signals are contained in some bounded interval and can be interpreted as events which are not only true or false, but rather true in some degree from the interval [0, 1]. Such a natural interpretation from multivalued logic (fuzzy logic) point of view, concerns sensor outputs, control signals, time expiration, etc. It leads to the idea of FPN as a controller, which one can rather simply obtain, and which would be able to process both analog, and binary signals. In the paper both graphical, and algebraic representations of the proposed FPN are given. The conditions under which transitions can be fired are described. The algebraic description of the net and a theorem which enables computation of new marking in the net, based on current marking, are formulated. Hardware implementation of the FPN, which uses fuzzy JK flip-flops and fuzzy gates, are proposed. An example illustrating usefulness of the proposed FPN for control algorithm description and its synthesis as a controller device for the concrete production process are presented.

Modelling Of Anticipated Damage Ratio On Breakwaters Using Fuzzy Logic

NASA Astrophysics Data System (ADS)

Mercan, D. E.; Yagci, O.; Kabdasli, S.

2003-04-01

In breakwater design the determination of armour unit weight is especially important in terms of the structure's life. In a typical experimental breakwater stability study, different wave series composed of different wave heights; wave period and wave steepness characteristics are applied in order to investigate performance the structure. Using a classical approach, a regression equation is generated for damage ratio as a function of characteristic wave height. The parameters wave period and wave steepness are not considered. In this study, differing from the classical approach using a fuzzy logic, a relationship between damage ratio as a function of mean wave period (T_m), wave steepness (H_s/L_m) and significant wave height (H_s) was further generated. The system's inputs were mean wave period (T_m), wave steepness (H_s/L_m) and significant wave height (H_s). For fuzzification all input variables were divided into three fuzzy subsets, their membership functions were defined using method developed by Mandani (Mandani, 1974) and the rules were written. While for defuzzification the centroid method was used. In order to calibrate and test the generated models an experimental study was conducted. The experiments were performed in a wave flume (24 m long, 1.0 m wide and 1.0 m high) using 20 different irregular wave series (P-M spectrum). Throughout the study, the water depth was 0.6 m and the breakwater cross-sectional slope was 1V/2H. In the armour layer, a type of artificial armour unit known as antifer cubes were used. The results of the established fuzzy logic model and regression equation model was compared with experimental data and it was determined that the established fuzzy logic model gave a more accurate prediction of the damage ratio on this type of breakwater. References Mandani, E.H., "Application of Fuzzy Algorithms for Control of Simple Dynamic Plant", Proc. IEE, vol. 121, no. 12, December 1974.

The Accuracy Of Fuzzy Sugeno Method With Antropometry On Determination Natural Patient Status

NASA Astrophysics Data System (ADS)

Syahputra, Dinur; Tulus; Sawaluddin

2017-12-01

Anthropometry is one of the processes that can be used to assess nutritional status. In general anthropometry is defined as body size in terms of nutrition, then anthropometry is reviewed from various age levels and nutritional levels. Nutritional status is a description of the balance between nutritional intake with the needs of the body individually. Fuzzy logic is a logic that has a vagueness between right and wrong or between 0 and 1. Sugeno method is used because in the process of calculating nutritional status so far is still done by anthropometry. Currently information technology is growing in any aspect, one of them in the aspect of calculation with data taken from anthropometry. In this case the calculation can use the Fuzzy Sugeno Method, in order to know the great accuracy obtained. Then the results obtained using fuzzy sugeno integrated with anthropometry has an accuracy of 81.48%.

The stock-flow model of spatial data infrastructure development refined by fuzzy logic.

PubMed

Abdolmajidi, Ehsan; Harrie, Lars; Mansourian, Ali

2016-01-01

The system dynamics technique has been demonstrated to be a proper method by which to model and simulate the development of spatial data infrastructures (SDI). An SDI is a collaborative effort to manage and share spatial data at different political and administrative levels. It is comprised of various dynamically interacting quantitative and qualitative (linguistic) variables. To incorporate linguistic variables and their joint effects in an SDI-development model more effectively, we suggest employing fuzzy logic. Not all fuzzy models are able to model the dynamic behavior of SDIs properly. Therefore, this paper aims to investigate different fuzzy models and their suitability for modeling SDIs. To that end, two inference and two defuzzification methods were used for the fuzzification of the joint effect of two variables in an existing SDI model. The results show that the Average-Average inference and Center of Area defuzzification can better model the dynamics of SDI development.

Search and selection hotel system in Surabaya based on geographic information system (GIS) with fuzzy logic

NASA Astrophysics Data System (ADS)

Purbandini, Taufik

2016-03-01

Surabaya is a metropolitan city in Indonesia. When the visitor has an interest in Surabaya for several days, then the visitor was looking for lodging that is closest to the interests of making it more efficient and practical. It was not a waste of time for the businessman because of congestion and so we need full information about the hotel as an inn during a stay in Surabaya began name, address of the hotel, the hotel's website, the distance from the hotel to the destination until the display of the map along the route with the help of Google Maps. This system was designed using fuzzy logic which aims to assist the user in making decisions. Design of hotel search and selection system was done through four stages. The first phase was the collection of data and as the factors that influence the decision-making along with the limit values of these factors. Factors that influence covers a distance of the hotel, the price of hotel rooms, and hotel reviews. The second stage was the processing of data and information by creating membership functions. The third stage was the analysis of systems with fuzzy logic. The steps were performed in systems analysis, namely fuzzification, inference using Mamdani, and defuzzification. The last stage was the design and construction of the system. Designing the system using use case diagrams and activity diagram to describe any process that occurs. Development system includes system implementation and evaluation systems. Implementation of mobile with Android-based system so that these applications were user friendly.

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

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.

PLL Based Energy Efficient PV System with Fuzzy Logic Based Power Tracker for Smart Grid Applications.

PubMed

Rohini, G; Jamuna, V

This work aims at improving the dynamic performance of the available photovoltaic (PV) system and maximizing the power obtained from it by the use of cascaded converters with intelligent control techniques. Fuzzy logic based maximum power point technique is embedded on the first conversion stage to obtain the maximum power from the available PV array. The cascading of second converter is needed to maintain the terminal voltage at grid potential. The soft-switching region of three-stage converter is increased with the proposed phase-locked loop based control strategy. The proposed strategy leads to reduction in the ripple content, rating of components, and switching losses. The PV array is mathematically modeled and the system is simulated and the results are analyzed. The performance of the system is compared with the existing maximum power point tracking algorithms. The authors have endeavored to accomplish maximum power and improved reliability for the same insolation of the PV system. Hardware results of the system are also discussed to prove the validity of the simulation results.

PLL Based Energy Efficient PV System with Fuzzy Logic Based Power Tracker for Smart Grid Applications

PubMed Central

Rohini, G.; Jamuna, V.

2016-01-01

This work aims at improving the dynamic performance of the available photovoltaic (PV) system and maximizing the power obtained from it by the use of cascaded converters with intelligent control techniques. Fuzzy logic based maximum power point technique is embedded on the first conversion stage to obtain the maximum power from the available PV array. The cascading of second converter is needed to maintain the terminal voltage at grid potential. The soft-switching region of three-stage converter is increased with the proposed phase-locked loop based control strategy. The proposed strategy leads to reduction in the ripple content, rating of components, and switching losses. The PV array is mathematically modeled and the system is simulated and the results are analyzed. The performance of the system is compared with the existing maximum power point tracking algorithms. The authors have endeavored to accomplish maximum power and improved reliability for the same insolation of the PV system. Hardware results of the system are also discussed to prove the validity of the simulation results. PMID:27294189

Fuzzy control system for a remote focusing microscope

NASA Astrophysics Data System (ADS)

Weiss, Jonathan J.; Tran, Luc P.

1992-01-01

Space Station Crew Health Care System procedures require the use of an on-board microscope whose slide images will be transmitted for analysis by ground-based microbiologists. Focusing of microscope slides is low on the list of crew priorities, so NASA is investigating the option of telerobotic focusing controlled by the microbiologist on the ground, using continuous video feedback. However, even at Space Station distances, the transmission time lag may disrupt the focusing process, severely limiting the number of slides that can be analyzed within a given bandwidth allocation. Substantial time could be saved if on-board automation could pre-focus each slide before transmission. The authors demonstrate the feasibility of on-board automatic focusing using a fuzzy logic ruled-based system to bring the slide image into focus. The original prototype system was produced in under two months and at low cost. Slide images are captured by a video camera, then digitized by gray-scale value. A software function calculates an index of 'sharpness' based on gray-scale contrasts. The fuzzy logic rule-based system uses feedback to set the microscope's focusing control in an attempt to maximize sharpness. The systems as currently implemented performs satisfactorily in focusing a variety of slide types at magnification levels ranging from 10 to 1000x. Although feasibility has been demonstrated, the system's performance and usability could be improved substantially in four ways: by upgrading the quality and resolution of the video imaging system (including the use of full color); by empirically defining and calibrating the index of image sharpness; by letting the overall focusing strategy vary depending on user-specified parameters; and by fine-tuning the fuzzy rules, set definitions, and procedures used.

Energy Center Structure Optimization by using Smart Technologies in Process Control System

NASA Astrophysics Data System (ADS)

Shilkina, Svetlana V.

2018-03-01

The article deals with practical application of fuzzy logic methods in process control systems. A control object - agroindustrial greenhouse complex, which includes its own energy center - is considered. The paper analyzes object power supply options taking into account connection to external power grids and/or installation of own power generating equipment with various layouts. The main problem of a greenhouse facility basic process is extremely uneven power consumption, which forces to purchase redundant generating equipment idling most of the time, which quite negatively affects project profitability. Energy center structure optimization is largely based on solving the object process control system construction issue. To cut investor’s costs it was proposed to optimize power consumption by building an energy-saving production control system based on a fuzzy logic controller. The developed algorithm of automated process control system functioning ensured more even electric and thermal energy consumption, allowed to propose construction of the object energy center with a smaller number of units due to their more even utilization. As a result, it is shown how practical use of microclimate parameters fuzzy control system during object functioning leads to optimization of agroindustrial complex energy facility structure, which contributes to a significant reduction in object construction and operation costs.

Artificial intelligence-based computer modeling tools for controlling slag foaming in electric arc furnaces

NASA Astrophysics Data System (ADS)

Wilson, Eric Lee

Due to increased competition in a world economy, steel companies are currently interested in developing techniques that will allow for the improvement of the steelmaking process, either by increasing output efficiency or by improving the quality of their product, or both. Slag foaming is one practice that has been shown to contribute to both these goals. However, slag foaming is highly dynamic and difficult to model or control. This dissertation describes an effort to use artificial intelligence-based tools (genetic algorithms, fuzzy logic, and neural networks) to both model and control the slag foaming process. Specifically, a neural network is trained and tested on slag foaming data provided by a steel plant. This neural network model is then controlled by a fuzzy logic controller, which in turn is optimized by a genetic algorithm. This tuned controller is then installed at a steel plant and given control be a more efficient slag foaming controller than what was previously used by the steel plant.

Assessment of Power Quality based on Fuzzy Logic and Discrete Wavelet Transform for Nonstationary Disturbances

NASA Astrophysics Data System (ADS)

Sinha, Pampa; Nath, Sudipta

2010-10-01

The main aspects of power system delivery are reliability and quality. If all the customers of a power system get uninterrupted power through the year then the system is considered to be reliable. The term power quality may be referred to as maintaining near sinusoidal voltage at rated frequency at the consumers end. The power component definitions are defined according to the IEEE Standard 1459-2000 both for single phase and three phase unbalanced systems based on Fourier Transform (FFT). In the presence of nonstationary power quality (PQ) disturbances results in accurate values due to its sensitivity to the spectral leakage problem. To overcome these limitations the power quality components are calculated using Discrete Wavelet Transform (DWT). In order to handle the uncertainties associated with electric power systems operations fuzzy logic has been incorporated in this paper. A new power quality index has been introduced here which can assess the power quality under nonstationary disturbances.

A manufacturing quality assessment model based-on two stages interval type-2 fuzzy logic

NASA Astrophysics Data System (ADS)

Purnomo, Muhammad Ridwan Andi; Helmi Shintya Dewi, Intan

2016-01-01

This paper presents the development of an assessment models for manufacturing quality using Interval Type-2 Fuzzy Logic (IT2-FL). The proposed model is developed based on one of building block in sustainable supply chain management (SSCM), which is benefit of SCM, and focuses more on quality. The proposed model can be used to predict the quality level of production chain in a company. The quality of production will affect to the quality of product. Practically, quality of production is unique for every type of production system. Hence, experts opinion will play major role in developing the assessment model. The model will become more complicated when the data contains ambiguity and uncertainty. In this study, IT2-FL is used to model the ambiguity and uncertainty. A case study taken from a company in Yogyakarta shows that the proposed manufacturing quality assessment model can work well in determining the quality level of production.

Energy management strategy based on fuzzy logic for a fuel cell hybrid bus

NASA Astrophysics Data System (ADS)

Gao, Dawei; Jin, Zhenhua; Lu, Qingchun

Fuel cell vehicles, as a substitute for internal-combustion-engine vehicles, have become a research hotspot for most automobile manufacturers all over the world. Fuel cell systems have disadvantages, such as high cost, slow response and no regenerative energy recovery during braking; hybridization can be a solution to these drawbacks. This paper presents a fuel cell hybrid bus which is equipped with a fuel cell system and two energy storage devices, i.e., a battery and an ultracapacitor. An energy management strategy based on fuzzy logic, which is employed to control the power flow of the vehicular power train, is described. This strategy is capable of determining the desired output power of the fuel cell system, battery and ultracapacitor according to the propulsion power and recuperated braking power. Some tests to verify the strategy were developed, and the results of the tests show the effectiveness of the proposed energy management strategy and the good performance of the fuel cell hybrid bus.

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.

Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping

NASA Astrophysics Data System (ADS)

Yousefi, Mahyar; Carranza, Emmanuel John M.

2015-01-01

Complexities of geological processes portrayed as certain feature in a map (e.g., faults) are natural sources of uncertainties in decision-making for exploration of mineral deposits. Besides natural sources of uncertainties, knowledge-driven (e.g., fuzzy logic) mineral prospectivity mapping (MPM) is also plagued and incurs further uncertainty in subjective judgment of analyst when there is no reliable proven value of evidential scores corresponding to relative importance of geological features that can directly be measured. In this regard, analysts apply expert opinion to assess relative importance of spatial evidences as meaningful decision support. This paper aims for fuzzification of continuous spatial data used as proxy evidence to facilitate and to support fuzzy MPM to generate exploration target areas for further examination of undiscovered deposits. In addition, this paper proposes to adapt the concept of expected value to further improve fuzzy logic MPM because the analysis of uncertain variables can be presented in terms of their expected value. The proposed modified expected value approach to MPM is not only a multi-criteria approach but it also treats uncertainty of geological processes a depicted by maps or spatial data in term of biased weighting more realistically in comparison with classified evidential maps because fuzzy membership scores are defined continuously whereby, for example, there is no need to categorize distances from evidential features to proximity classes using arbitrary intervals. The proposed continuous weighting approach and then integrating the weighted evidence layers by using modified expected value function, described in this paper can be used efficiently in either greenfields or brownfields.

Synthesis of nonlinear control strategies from fuzzy logic control algorithms

NASA Technical Reports Server (NTRS)

Langari, Reza

1993-01-01

Fuzzy control has been recognized as an alternative to conventional control techniques in situations where the plant model is not sufficiently well known to warrant the application of conventional control techniques. Precisely what fuzzy control does and how it does what it does is not quite clear, however. This important issue is discussed and in particular it is shown how a given fuzzy control scheme can resolve into a nonlinear control law and that in those situations the success of fuzzy control hinges on its ability to compensate for nonlinearities in plant dynamics.

Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor

NASA Astrophysics Data System (ADS)

Rahman, Husna Abdul; Harun, Sulaiman Wadi; Arof, Hamzah; Irawati, Ninik; Musirin, Ismail; Ibrahim, Fatimah; Ahmad, Harith

2014-05-01

An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.

Gear Damage Detection Using Oil Debris Analysis

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.

2001-01-01

The purpose of this paper was to verify, when using an oil debris sensor, that accumulated mass predicts gear pitting damage and to identify a method to set threshold limits for damaged gears. Oil debris data was collected from 8 experiments with no damage and 8 with pitting damage in the NASA Glenn Spur Gear Fatigue Rig. Oil debris feature analysis was performed on this data. Video images of damage progression were also collected from 6 of the experiments with pitting damage. During each test, data from an oil debris sensor was monitored and recorded for the occurrence of pitting damage. The data measured from the oil debris sensor during experiments with damage and with no damage was used to identify membership functions to build a simple fuzzy logic model. Using fuzzy logic techniques and the oil debris data, threshold limits were defined that discriminate between stages of pitting wear. Results indicate accumulated mass combined with fuzzy logic analysis techniques is a good predictor of pitting damage on spur gears.

A fuzzy logic model to forecast stock market momentum in Indonesia's property and real estate sector

NASA Astrophysics Data System (ADS)

Penawar, H. K.; Rustam, Z.

2017-07-01

The Capital market has the important role in Indonesia's economy. The capital market does not only support the economy of Indonesia but also being an indicator Indonesia's economy improvement. Something that has been traded in the capital market is stock (stock market). Nowadays, the stock market is full of uncertainty. That uncertainty values make predicting stock market is all that we have to do before we make a decision in the stock market. One that can be predicted in the stock market is momentum. To forecast stock market momentum, it can use fuzzy logic model. In the process of modeling, it will be used 14 days historical data that consisting the value of open, high, low, and close, to predict the next 5 days momentum categories. There are three momentum categories namely Bullish, Neutral, and Bearish. To illustrate the fuzzy logic model, we will use stocks data from several companies that listed on Indonesia Stock Exchange (IDX) in property and real estate sector.

Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor.

PubMed

Rahman, Husna Abdul; Harun, Sulaiman Wadi; Arof, Hamzah; Irawati, Ninik; Musirin, Ismail; Ibrahim, Fatimah; Ahmad, Harith

2014-05-01

An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.

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.

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.