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.
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.
An Adaptive Fuzzy-Logic Traffic Control System in Conditions of Saturated Transport Stream
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
A new adaptive configuration of PID type fuzzy logic controller.
Fereidouni, Alireza; Masoum, Mohammad A S; Moghbel, Moayed
2015-05-01
In this paper, an adaptive configuration for PID type fuzzy logic controller (FLC) is proposed to improve the performances of both conventional PID (C-PID) controller and conventional PID type FLC (C-PID-FLC). The proposed configuration is called adaptive because its output scaling factors (SFs) are dynamically tuned while the controller is functioning. The initial values of SFs are calculated based on its well-tuned counterpart while the proceeding values are generated using a proposed stochastic hybrid bacterial foraging particle swarm optimization (h-BF-PSO) algorithm. The performance of the proposed configuration is evaluated through extensive simulations for different operating conditions (changes in reference, load disturbance and noise signals). The results reveal that the proposed scheme performs significantly better over the C-PID controller and the C-PID-FLC in terms of several performance indices (integral absolute error (IAE), integral-of-time-multiplied absolute error (ITAE) and integral-of-time-multiplied squared error (ITSE)), overshoot and settling time for plants with and without dead time.
Fuzzy logic controller optimization
Sepe, Jr., Raymond B; Miller, John Michael
2004-03-23
A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.
NASA Technical Reports Server (NTRS)
Howard, Ayanna
2005-01-01
The Fuzzy Logic Engine is a software package that enables users to embed fuzzy-logic modules into their application programs. Fuzzy logic is useful as a means of formulating human expert knowledge and translating it into software to solve problems. Fuzzy logic provides flexibility for modeling relationships between input and output information and is distinguished by its robustness with respect to noise and variations in system parameters. In addition, linguistic fuzzy sets and conditional statements allow systems to make decisions based on imprecise and incomplete information. The user of the Fuzzy Logic Engine need not be an expert in fuzzy logic: it suffices to have a basic understanding of how linguistic rules can be applied to the user's problem. The Fuzzy Logic Engine is divided into two modules: (1) a graphical-interface software tool for creating linguistic fuzzy sets and conditional statements and (2) a fuzzy-logic software library for embedding fuzzy processing capability into current application programs. The graphical- interface tool was developed using the Tcl/Tk programming language. The fuzzy-logic software library was written in the C programming language.
Adaptive fuzzy logic control of a static VAR system
Dash, P.K.; Routray, A.; Panda, P.C.; Panda, S.K.
1995-12-31
A fuzzy gain scheduling scheme for PID controller for transient and dynamic voltage stabilization of power transmission systems has been presented in this paper. Fuzzy rules and reasoning are utilized on-line to determine the controller parameters based on the error signal and its derivative. The static VAR controller is designed with the bus angle deviation and its rate as the input signal to a fuzzy PI or PID control loop. This control is tested for a power transmission system supplying dynamic loads and provides superior performance.
Hansen, M; Haugland, M K
2001-01-01
Adaptive restriction rules based on fuzzy logic have been developed to eliminate errors and to increase stimulation safety in the foot-drop correction application, specifically when using adaptive logic networks to provide a stimulation control signal based on neural activity recorded from peripheral sensory nerve branches. The fuzzy rules were designed to increase flexibility and offer easier customization, compared to earlier versions of restriction rules. The rules developed quantified the duration of swing and stance phases into states of accepting or rejecting new transitions, based on the cyclic nature of gait and statistics on the current gait patterns. The rules were easy to custom design for a specific application, using linguistic terms to model the actions of the rules. The rules were tested using pre-recorded gait data processed through a gait event detector and proved to reduce detection delay and the number of errors, compared to conventional rules.
Flight test results of the fuzzy logic adaptive controller-helicopter (FLAC-H)
NASA Astrophysics Data System (ADS)
Wade, Robert L.; Walker, Gregory W.
1996-05-01
The fuzzy logic adaptive controller for helicopters (FLAC-H) demonstration is a cooperative effort between the US Army Simulation, Training, and Instrumentation Command (STRICOM), the US Army Aviation and Troop Command, and the US Army Missile Command to demonstrate a low-cost drone control system for both full-scale and sub-scale helicopters. FLAC-H was demonstrated on one of STRICOM's fleet of full-scale rotary-winged target drones. FLAC-H exploits fuzzy logic in its flight control system to provide a robust solution to the control of the helicopter's dynamic, nonlinear system. Straight forward, common sense fuzzy rules governing helicopter flight are processed instead of complex mathematical models. This has resulted in a simplified solution to the complexities of helicopter flight. Incorporation of fuzzy logic reduced the cost of development and should also reduce the cost of maintenance of the system. An adaptive algorithm allows the FLAC-H to 'learn' how to fly the helicopter, enabling the control system to adjust to varying helicopter configurations. The adaptive algorithm, based on genetic algorithms, alters the fuzzy rules and their related sets to improve the performance characteristics of the system. This learning allows FLAC-H to automatically be integrated into a new airframe, reducing the development costs associated with altering a control system for a new or heavily modified aircraft. Successful flight tests of the FLAC-H on a UH-1H target drone were completed in September 1994 at the White Sands Missile Range in New Mexico. This paper discuses the objective of the system, its design, and performance.
Controlling chaos in a defined trajectory using adaptive fuzzy logic algorithm
NASA Astrophysics Data System (ADS)
Sadeghi, Maryam; Menhaj, Bagher
2012-09-01
Chaos is a nonlinear behavior of chaotic system with the extreme sensitivity to the initial conditions. Chaos control is so complicated that solutions never converge to a specific numbers and vary chaotically from one amount to the other next. A tiny perturbation in a chaotic system may result in chaotic, periodic, or stationary behavior. Modern controllers are introduced for controlling the chaotic behavior. In this research an adaptive Fuzzy Logic Controller (AFLC) is proposed to control the chaotic system with two equilibrium points. This method is introduced as an adaptive progressed fashion with the full ability to control the nonlinear systems even in the undertrained conditions. Using AFLC designers are released to determine the precise mathematical model of system and satisfy the vast adaption that is needed for a rapid variation which may be caused in the dynamic of nonlinear system. Rules and system parameters are generated through the AFLC and expert knowledge is downright only in the initialization stage. So if the knowledge was not assuring the dynamic of system it could be changed through the adaption procedure of parameters values. AFLC methodology is an advanced control fashion in control yielding to both robustness and smooth motion in nonlinear system control.
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.
NASA Technical Reports Server (NTRS)
2005-01-01
A new all-electronic Particle Image Velocimetry technique that can efficiently map high speed gas flows has been developed in-house at the NASA Lewis Research Center. Particle Image Velocimetry is an optical technique for measuring the instantaneous two component velocity field across a planar region of a seeded flow field. A pulsed laser light sheet is used to illuminate the seed particles entrained in the flow field at two instances in time. One or more charged coupled device (CCD) cameras can be used to record the instantaneous positions of particles. Using the time between light sheet pulses and determining either the individual particle displacements or the average displacement of particles over a small subregion of the recorded image enables the calculation of the fluid velocity. Fuzzy logic minimizes the required operator intervention in identifying particles and computing velocity. Using two cameras that have the same view of the illumination plane yields two single exposure image frames. Two competing techniques that yield unambiguous velocity vector direction information have been widely used for reducing the single-exposure, multiple image frame data: (1) cross-correlation and (2) particle tracking. Correlation techniques yield averaged velocity estimates over subregions of the flow, whereas particle tracking techniques give individual particle velocity estimates. For the correlation technique, the correlation peak corresponding to the average displacement of particles across the subregion must be identified. Noise on the images and particle dropout result in misidentification of the true correlation peak. The subsequent velocity vector maps contain spurious vectors where the displacement peaks have been improperly identified. Typically these spurious vectors are replaced by a weighted average of the neighboring vectors, thereby decreasing the independence of the measurements. In this work, fuzzy logic techniques are used to determine the true
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.
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.
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.
Karami, Ali; Keiter, Steffen; Hollert, Henner; Courtenay, Simon C
2013-03-01
This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A "data trimming" approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies.
Fuzzy Logic in Medicine and Bioinformatics
Torres, Angela; Nieto, Juan J.
2006-01-01
The purpose of this paper is to present a general view of the current applications of fuzzy logic in medicine and bioinformatics. We particularly review the medical literature using fuzzy logic. We then recall the geometrical interpretation of fuzzy sets as points in a fuzzy hypercube and present two concrete illustrations in medicine (drug addictions) and in bioinformatics (comparison of genomes). PMID:16883057
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.
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.
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
Fuzzy Logic for Incidence Geometry.
Tserkovny, Alex
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.
Fuzzy Logic for Incidence Geometry
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 forecasting based on fuzzy-trend logical relationship groups.
Chen, Shyi-Ming; Wang, Nai-Yi
2010-10-01
In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.
Fuzzy Logic as a Tool for Assessing Students' Knowledge and Skills
ERIC Educational Resources Information Center
Voskoglou, Michael Gr.
2013-01-01
Fuzzy logic, which is based on fuzzy sets theory introduced by Zadeh in 1965, provides a rich and meaningful addition to standard logic. The applications which may be generated from or adapted to fuzzy logic are wide-ranging and provide the opportunity for modeling under conditions which are imprecisely defined. In this article we develop a fuzzy…
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.
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.
Probabilistic and fuzzy logic in clinical diagnosis.
Licata, G
2007-06-01
In this study I have compared classic and fuzzy logic and their usefulness in clinical diagnosis. The theory of probability is often considered a device to protect the classical two-valued logic from the evidence of its inadequacy to understand and show the complexity of world [1]. This can be true, but it is not possible to discard the theory of probability. I will argue that the problems and the application fields of the theory of probability are very different from those of fuzzy logic. After the introduction on the theoretical bases of fuzzy approach to logic, I have reported some diagnostic argumentations employing fuzzy logic. The state of normality and the state of disease often fight their battle on scalar quantities of biological values and it is not hard to establish a correspondence between the biological values and the percent values of fuzzy logic. Accordingly, I have suggested some applications of fuzzy logic in clinical diagnosis and in particular I have utilised a fuzzy curve to recognise subjects with diabetes mellitus, renal failure and liver disease. The comparison between classic and fuzzy logic findings seems to indicate that fuzzy logic is more adequate to study the development of biological events. In fact, fuzzy logic is useful when we have a lot of pieces of information and when we dispose to scalar quantities. In conclusion, increasingly the development of technology offers new instruments to measure pathological parameters through scalar quantities, thus it is reasonable to think that in the future fuzzy logic will be employed more in clinical diagnosis.
Adaptive hierarchical fuzzy controller
Raju, G.V.S.; Jun Zhou
1993-07-01
A methodology for designing adaptive hierarchical fuzzy controllers is presented. In order to evaluate this concept, several suitable performance indices were developed and converted to linguistic fuzzy variables. Based on those variables, a supervisory fuzzy rule set was constructed and used to change the parameters of a hierarchical fuzzy controller to accommodate the variations of system parameters. The proposed algorithm was used in feedwater flow control to a steam generator. Simulation studies are presented that illustrate the effectiveness of the approach
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.
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.
Fuzzy logic and coarse coding using programmable logic devices
NASA Astrophysics Data System (ADS)
Brooks, Geoffrey
2009-05-01
Naturally-occurring sensory signal processing algorithms, such as those that inspired fuzzy-logic control, can be integrated into non-naturally-occurring high-performance technology, such as programmable logic devices, to realize novel bio-inspired designs. Research is underway concerning an investigation into using field programmable logic devices (FPLD's) to implement fuzzy logic sensory processing. A discussion is provided concerning the commonality between bio-inspired fuzzy logic algorithms and coarse coding that is prevalent in naturally-occurring sensory systems. Undergraduate design projects using fuzzy logic for an obstacle-avoidance robot has been accomplished at our institution and other places; numerous other successful fuzzy logic applications can be found as well. The long-term goal is to leverage such biomimetic algorithms for future applications. This paper outlines a design approach for implementing fuzzy-logic algorithms into reconfigurable computing devices. This paper is presented in an effort to connect with others who may be interested in collaboration as well as to establish a starting point for future research.
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.
Nursing and fuzzy logic: an integrative review.
Jensen, Rodrigo; Lopes, Maria Helena Baena de Moraes
2011-01-01
This study conducted an integrative review investigating how fuzzy logic has been used in research with the participation of nurses. The article search was carried out in the CINAHL, EMBASE, SCOPUS, PubMed and Medline databases, with no limitation on time of publication. Articles written in Portuguese, English and Spanish with themes related to nursing and fuzzy logic with the authorship or participation of nurses were included. The final sample included 21 articles from eight countries. For the purpose of analysis, the articles were distributed into categories: theory, method and model. In nursing, fuzzy logic has significantly contributed to the understanding of subjects related to: imprecision or the need of an expert; as a research method; and in the development of models or decision support systems and hard technologies. The use of fuzzy logic in nursing has shown great potential and represents a vast field for research.
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 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 logic program at SGS-Thomson
NASA Astrophysics Data System (ADS)
Pagni, Andrea; Poluzzi, Rinaldo; Rizzotto, GianGuido
1993-12-01
From its conception by Professor Lotfi A. Zadeh in the early '60s, Fuzzy Logic has slowly won acceptance, first in the academic world, then in industry. Its success is mainly due to the different perspective with which problems are tackled. Thanks to Fuzzy Logic we have moved from a numerical/analytical description to a quantitative/qualitative one. It is important to stress that this different perspective not only allows us to solve analysis/control problems at lower costs but can also allow otherwise insoluble problems to be solved at acceptable costs. Of course, it must be stressed that Fuzzy Systems cannot match the computational precision of traditional techniques but seek, instead, to find acceptable solutions in shorter times. Recognizing the enormous importance of fuzzy logic in the markets of the future, SGS-THOMSON intends to produce devices belonging to a new class of machines: Fuzzy Computational Machines. For this purpose a major research project has been established considering the architectural aspects and system implications of fuzzy logic, the development of dedicated VLSI components and supporting software.
Adaptive parallel logic networks
NASA Technical Reports Server (NTRS)
Martinez, Tony R.; Vidal, Jacques J.
1988-01-01
Adaptive, self-organizing concurrent systems (ASOCS) that combine self-organization with massive parallelism for such applications as adaptive logic devices, robotics, process control, and system malfunction management, are presently discussed. In ASOCS, an adaptive network composed of many simple computing elements operating in combinational and asynchronous fashion is used and problems are specified by presenting if-then rules to the system in the form of Boolean conjunctions. During data processing, which is a different operational phase from adaptation, the network acts as a parallel hardware circuit.
Robust fuzzy logic stabilization with disturbance elimination.
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.
Robust Fuzzy Logic Stabilization with Disturbance Elimination
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
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.
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.
Pattern recognition using linguistic fuzzy logic predictors
NASA Astrophysics Data System (ADS)
Habiballa, Hashim
2016-06-01
The problem of pattern recognition has been solved with numerous methods in the Artificial Intelligence field. We present an unconventional method based on Lingustic Fuzzy Logic Forecaster which is primarily used for the task of time series analysis and prediction through logical deduction wtih linguistic variables. This method should be used not only to the time series prediction itself, but also for recognition of patterns in a signal with seasonal component.
Indeterminacy, linguistic semantics and fuzzy logic
Novak, V.
1996-12-31
In this paper, we discuss the indeterminacy phenomenon which has two distinguished faces, namely uncertainty modeled especially by the probability theory and vagueness, modeled by fuzzy logic. Other important mathematical model of vagueness is provided by the Alternative Set Theory. We focus on some of the basic concepts of these theories in connection with mathematical modeling of the linguistic semantics.
The Impact of Fuzzy Logic on Student Press Law.
ERIC Educational Resources Information Center
McCool, Lauralee; Plopper, Bruce L.
2001-01-01
Uses the relatively new science of fuzzy logic to review lower court and appellate court decisions from the last four decades regarding free expression in student publications. Finds pronounced effects, showing that fuzzy sets inherently favor administrators, while students show a strikingly high win/loss ratio when courts avoid fuzzy logic. (SR)
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.
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.
Automated interpretation of LIBS spectra using a fuzzy logic inference engine.
Hatch, Jeremy J; McJunkin, Timothy R; Hanson, Cynthia; Scott, Jill R
2012-03-01
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. Fuzzy logic inference rules were developed using methodology that includes data mining methods and operator expertise to differentiate between various copper-containing and stainless steel alloys as well as unknowns. Results using the fuzzy logic inference engine indicate a high degree of confidence in spectral assignment.
Fuzzy logic, neural networks, and soft computing
NASA Technical Reports Server (NTRS)
Zadeh, Lofti A.
1994-01-01
The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial
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.
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.
Fuzzy logic-based spike sorting system.
Balasubramanian, Karthikeyan; Obeid, Iyad
2011-05-15
We present a new method for autonomous real-time spike sorting using a fuzzy logic inference engine. The engine assigns each detected event a 'spikiness index' from zero to one that quantifies the extent to which the detected event is like an ideal spike. Spikes can then be sorted by simply clustering the spikiness indices. The sorter is defined in terms of natural language rules that, once defined, are static and thus require no user intervention or calibration. The sorter was tested using extracellular recordings from three animals: a macaque, an owl monkey and a rat. Simulation results show that the fuzzy sorter performed equal to or better than the benchmark principal component analysis (PCA) based sorter. Importantly, there was no degradation in fuzzy sorter performance when the spikes were not temporally aligned prior to sorting. In contrast, PCA sorter performance dropped by 27% when sorting unaligned spikes. Since the fuzzy sorter is computationally trivial and requires no spike alignment, it is suitable for scaling into large numbers of parallel channels where computational overhead and the need for operator intervention would preclude other spike sorters.
Designing a Software Tool for Fuzzy Logic Programming
NASA Astrophysics Data System (ADS)
Abietar, José M.; Morcillo, Pedro J.; Moreno, Ginés
2007-12-01
Fuzzy Logic Programming is an interesting and still growing research area that agglutinates the efforts for introducing fuzzy logic into logic programming (LP), in order to incorporate more expressive resources on such languages for dealing with uncertainty and approximated reasoning. The multi-adjoint logic programming approach is a recent and extremely flexible fuzzy logic paradigm for which, unfortunately, we have not found practical tools implemented so far. In this work, we describe a prototype system which is able to directly translate fuzzy logic programs into Prolog code in order to safely execute these residual programs inside any standard Prolog interpreter in a completely transparent way for the final user. We think that the development of such fuzzy languages and programing tools might play an important role in the design of advanced software applications for computational physics, chemistry, mathematics, medicine, industrial control and so on.
Robust adaptive control of MEMS triaxial gyroscope using fuzzy compensator.
Fei, Juntao; Zhou, Jian
2012-12-01
In this paper, a robust adaptive control strategy using a fuzzy compensator for MEMS triaxial gyroscope, which has system nonlinearities, including model uncertainties and external disturbances, is proposed. A fuzzy logic controller that could compensate for the model uncertainties and external disturbances is incorporated into the adaptive control scheme in the Lyapunov framework. The proposed adaptive fuzzy controller can guarantee the convergence and asymptotical stability of the closed-loop system. The proposed adaptive fuzzy control strategy does not depend on accurate mathematical models, which simplifies the design procedure. The innovative development of intelligent control methods incorporated with conventional control for the MEMS gyroscope is derived with the strict theoretical proof of the Lyapunov stability. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive fuzzy control scheme and demonstrate the satisfactory tracking performance and robustness against model uncertainties and external disturbances compared with conventional adaptive control method.
Intelligent Vehicle Power Management Using Machine Learning and Fuzzy Logic
2008-06-01
machine learning and fuzzy logic. A machine learning algorithm, LOPPS, has been developed to learn about optimal power source combinations with... machine learning algorithm combined with fuzzy logic is a promising technology for vehicle power management. I. INTRODUCTION ROWING...sources, and the complex configuration and operation modes, the control strategy of a hybrid vehicle is more complicated than that of a conventional
The Influence of Fuzzy Logic Theory on Students' Achievement
ERIC Educational Resources Information Center
Semerci, Çetin
2004-01-01
As science and technology develop, the use's areas of Fuzzy Logic Theory develop too. Measurement and evaluation in education is one of these areas. The purpose of this research is to explain the influence of fuzzy logic theory on students' achievement. An experimental method is employed in the research. The traditional achievement marks and The…
Terminology and concepts of control and Fuzzy Logic
NASA Technical Reports Server (NTRS)
Aldridge, Jack; Lea, Robert; Jani, Yashvant; Weiss, Jonathan
1990-01-01
Viewgraphs on terminology and concepts of control and fuzzy logic are presented. Topics covered include: control systems; issues in the design of a control system; state space control for inverted pendulum; proportional-integral-derivative (PID) controller; fuzzy controller; and fuzzy rule processing.
ANFIS optimized semi-active fuzzy logic controller for magnetorheological dampers
NASA Astrophysics Data System (ADS)
César, Manuel Braz; Barros, Rui Carneiro
2016-11-01
In this paper, we report on the development of a neuro-fuzzy controller for magnetorheological dampers using an Adaptive Neuro-Fuzzy Inference System or ANFIS. Fuzzy logic based controllers are capable to deal with non-linear or uncertain systems, which make them particularly well suited for civil engineering applications. The main objective is to develop a semi-active control system with a MR damper to reduce the response of a three degrees-of-freedom (DOFs) building structure. The control system is designed using ANFIS to optimize the fuzzy inference rule of a simple fuzzy logic controller. The results show that the proposed semi-active neuro-fuzzy based controller is effective in reducing the response of structural system.
Chen, Shyi-Ming; Chen, Shen-Wen
2015-03-01
In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy-trend logical relationships. Firstly, the proposed method fuzzifies the historical training data of the main factor and the secondary factor into fuzzy sets, respectively, to form two-factors second-order fuzzy logical relationships. Then, it groups the obtained two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, it calculates the probability of the "down-trend," the probability of the "equal-trend" and the probability of the "up-trend" of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group, respectively. Finally, it performs the forecasting based on the probabilities of the down-trend, the equal-trend, and the up-trend of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the NTD/USD exchange rates. The experimental results show that the proposed method outperforms the existing methods.
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.
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.
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.
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.
Fuzzy logic and its application in football team ranking.
Zeng, Wenyi; Li, Junhong
2014-01-01
Fuzzy set theory and fuzzy logic are a highly suitable and applicable basis for developing knowledge-based systems in physical education for tasks such as the selection for athletes, the evaluation for different training approaches, the team ranking, and the real-time monitoring of sports data. In this paper, we use fuzzy set theory and apply fuzzy clustering analysis in football team ranking. Based on some certain rules, we propose four parameters to calculate fuzzy similar matrix, obtain fuzzy equivalence matrix and the ranking result for our numerical example, T 7, T 3, T 1, T 9, T 10, T 8, T 11, T 12, T 2, T 6, T 5, T 4, and investigate four parameters sensitivity analysis. The study shows that our fuzzy logic method is reliable and stable when the parameters change in certain range.
Fruit Sorting Using Fuzzy Logic Techniques
NASA Astrophysics Data System (ADS)
Elamvazuthi, Irraivan; Sinnadurai, Rajendran; Aftab Ahmed Khan, Mohamed Khan; Vasant, Pandian
2009-08-01
Fruit and vegetables market is getting highly selective, requiring their suppliers to distribute the goods according to very strict standards of quality and presentation. In the last years, a number of fruit sorting and grading systems have appeared to fulfill the needs of the fruit processing industry. However, most of them are overly complex and too costly for the small and medium scale industry (SMIs) in Malaysia. In order to address these shortcomings, a prototype machine was developed by integrating the fruit sorting, labeling and packing processes. To realise the prototype, many design issues were dealt with. Special attention is paid to the electronic weighing sub-system for measuring weight, and the opto-electronic sub-system for determining the height and width of the fruits. Specifically, this paper discusses the application of fuzzy logic techniques in the sorting process.
Fuzzy Logic Enhanced Digital PIV Processing Software
NASA Technical Reports Server (NTRS)
Wernet, Mark P.
1999-01-01
Digital Particle Image Velocimetry (DPIV) is an instantaneous, planar velocity measurement technique that is ideally suited for studying transient flow phenomena in high speed turbomachinery. DPIV is being actively used at the NASA Glenn Research Center to study both stable and unstable operating conditions in a high speed centrifugal compressor. Commercial PIV systems are readily available which provide near real time feedback of the PIV image data quality. These commercial systems are well designed to facilitate the expedient acquisition of PIV image data. However, as with any general purpose system, these commercial PIV systems do not meet all of the data processing needs required for PIV image data reduction in our compressor research program. An in-house PIV PROCessing (PIVPROC) code has been developed for reducing PIV data. The PIVPROC software incorporates fuzzy logic data validation for maximum information recovery from PIV image data. PIVPROC enables combined cross-correlation/particle tracking wherein the highest possible spatial resolution velocity measurements are obtained.
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
Twenty-Five Years of the Fuzzy Factor: Fuzzy Logic, the Courts, and Student Press Law.
ERIC Educational Resources Information Center
Plopper, Bruce L.; McCool, Lauralee
A study applied the structure of fuzzy logic, a fairly modern development in mathematical set theory, to judicial opinions concerning non-university, public school student publications, from 1975 to 1999. The study examined case outcomes (19 cases generated 27 opinions) as a function of fuzzy logic, and it evaluated interactions between fuzzy…
Fuzzy logic color detection: Blue areas in melanoma dermoscopy images.
Lingala, Mounika; Stanley, R Joe; Rader, Ryan K; Hagerty, Jason; Rabinovitz, Harold S; Oliviero, Margaret; Choudhry, Iqra; Stoecker, William V
2014-07-01
Fuzzy logic image analysis techniques were used to analyze three shades of blue (lavender blue, light blue, and dark blue) in dermoscopic images for melanoma detection. A logistic regression model provided up to 82.7% accuracy for melanoma discrimination for 866 images. With a support vector machines (SVM) classifier, lower accuracy was obtained for individual shades (79.9-80.1%) compared with up to 81.4% accuracy with multiple shades. All fuzzy blue logic alpha cuts scored higher than the crisp case. Fuzzy logic techniques applied to multiple shades of blue can assist in melanoma detection. These vector-based fuzzy logic techniques can be extended to other image analysis problems involving multiple colors or color shades.
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)
Organizational coevolutionary classifiers with fuzzy logic used in intrusion detection
NASA Astrophysics Data System (ADS)
Chen, Zhenguo
2009-07-01
Intrusion detection is an important technique in the defense-in-depth network security framework and a hot topic in computer security in recent years. To solve the intrusion detection question, we introduce the fuzzy logic into Organization CoEvolutionary algorithm [1] and present the algorithm of Organization CoEvolutionary Classification with Fuzzy Logic. In this paper, we give an intrusion detection models based on Organization CoEvolutionary Classification with Fuzzy Logic. After illustrating our model with a representative dataset and applying it to the real-world network datasets KDD Cup 1999. The experimental result shown that the intrusion detection based on Organizational Coevolutionary Classifiers with Fuzzy Logic can give higher recognition accuracy than the general method.
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.
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.
HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.
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.
Saravanan, Vijayakumar; Lakshmi, P T V
2014-09-01
The path to personalized medicine demands the use of new and customized biopharmaceutical products containing modified proteins. Hence, assessment of these products for allergenicity becomes mandatory before they are introduced as therapeutics. Despite the availability of different tools to predict the allergenicity of proteins, it remains challenging to predict the allergens and nonallergens, when they share significant sequence similarity with known nonallergens and allergens, respectively. Hence, we propose "FuzzyApp," a novel fuzzy rule based system to evaluate the quality of the query protein to be an allergen. It measures the allergenicity of the protein based on the fuzzy IF-THEN rules derived from five different modules. On various datasets, FuzzyApp outperformed other existing methods and retained balance between sensitivity and specificity, with positive Mathew's correlation coefficient. The high specificity of allergen-like putative nonallergens (APN) revealed the FuzzyApp's capability in distinguishing the APN from allergens. In addition, the error analysis and whole proteome dataset analysis suggest the efficiency and consistency of the proposed method. Further, FuzzyApp predicted the Tropomyosin from various allergenic and nonallergenic sources accurately. The web service created allows batch sequence submission, and outputs the result as readable sentences rather than values alone, which assists the user in understanding why and what features are responsible for the prediction. FuzzyApp is implemented using PERL CGI and is freely accessible at http://fuzzyapp.bicpu.edu.in/predict.php . We suggest the use of Fuzzy logic has much potential in biomarker and personalized medicine research to enhance predictive capabilities of post-genomics diagnostics.
Development of a self-tuning fuzzy logic controller
Huang, S.H.; Nelson, R.M.
1999-07-01
To avoid the laborious task of modifying control rule sets for fuzzy logic controllers, a novel model-based self-tuning strategy has been developed. The performance of this advanced fuzzy logic controller is measured and analyzed in a linguistic plane. An optimal performance trajectory functions as the control model. The self-tuning strategy improves the performance automatically until it converges to a predetermined optimal global criterion. The experimental results indicate that the actual performance trajectory of the advanced fuzzy controller with the self-tuning strategy has reached the optimal criterion.
Astronomical pipeline processing using fuzzy logic
NASA Astrophysics Data System (ADS)
Shamir, Lior
In the past few years, pipelines providing astronomical data have been becoming increasingly important. The wide use of robotic telescopes has provided significant discoveries, and sky survey projects such as SDSS and the future LSST are now considered among the premier projects in the field astronomy. The huge amount of data produced by these pipelines raises the need for automatic processing. Astronomical pipelines introduce several well-defined problems such as astronomical image compression, cosmic-ray hit rejection, transient detection, meteor triangulation and association of point sources with their corresponding known stellar objects. We developed and applied soft computing algorithms that provide new or improved solutions to these growing problems in the field of pipeline processing of astronomical data. One new approach that we use is fuzzy logic-based algorithms, which enables the automatic analysis of the astronomical pipelines and allows mining the data for not-yet-known astronomical discoveries such as optical transients and variable stars. The developed algorithms have been tested with excellent results on the NightSkyLive sky survey, which provides a pipeline of 150 astronomical pictures per hour, and covers almost the entire global night sky.
Automated Interpretation of LIBS Spectra using a Fuzzy Logic Inference Engine
Jeremy J. Hatch; Timothy R. McJunkin; Cynthia Hanson; Jill R. Scott
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.
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.
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.
Seismic event interpretation using fuzzy logic and neural networks
Maurer, W.J.; Dowla, F.U.
1994-01-01
In the computer interpretation of seismic data, unknown sources of seismic events must be represented and reasoned about using measurements from the recorded signal. In this report, we develop the use of fuzzy logic to improve our ability to interpret weak seismic events. Processing strategies for the use of fuzzy set theory to represent vagueness and uncertainty, a phenomena common in seismic data analysis, are developed. A fuzzy-assumption based truth-maintenance-inferencing engine is also developed. Preliminary results in interpreting seismic events using the fuzzy neural network knowledge-based system are presented.
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 information 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
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.
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.
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.
Fuzzy logic control and optimization system
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.
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 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.
Completed Optimised Structure of Threonine Molecule by Fuzzy Logic Modelling
NASA Astrophysics Data System (ADS)
Sahiner, Ahmet; Ucun, Fatih; Kapusuz, Gulden; Yilmaz, Nurullah
2016-04-01
In this study we applied the fuzzy logic approach in order to model the energy depending on the two torsion angles for the threonine (C4H9NO3) molecule. The model is set up according to theoretical results obtained by the density functional theory (B3LYP) with a 6-31 G(d) basic set on a Gausian program. We aimed to determine the best torsion angle values providing the energy of the molecule minimum by a fuzzy logic approach and to compare them with the density functional theory results. It was concluded that the fuzzy logic approach gives information about the untested data and its best value which are expensive and time-consuming to obtain by other methods and experimentation.
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.
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.
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.
Coordinated signal control for arterial intersections using fuzzy logic
NASA Astrophysics Data System (ADS)
Kermanian, Davood; Zare, Assef; Balochian, Saeed
2013-09-01
Every day growth of the vehicles has become one of the biggest problems of urbanism especially in major cities. This can waste people's time, increase the fuel consumption, air pollution, and increase the density of cars and vehicles. Fuzzy controllers have been widely used in many consumer products and industrial applications with success over the past two decades. This article proposes a comprehensive model of urban traffic network using state space equations and then using Fuzzy Logic Tool Box and SIMULINK Program MATLAB a fuzzy controller in order to optimize and coordinate signal control at two intersections at an arterial road. The fuzzy controller decides to extend, early cut or terminate a signal phase and phase sequence to ensure smooth flow of traffic with minimal waiting time and length of queue. Results show that the performance of the proposed traffic controller at novel fuzzy model is better that of conventional controllers under normal and abnormal traffic conditions.
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.
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.
Autonomous Control of a Quadrotor UAV Using Fuzzy Logic
NASA Astrophysics Data System (ADS)
Sureshkumar, Vijaykumar
UAVs are being increasingly used today than ever before in both military and civil applications. They are heavily preferred in "dull, dirty or dangerous" mission scenarios. Increasingly, UAVs of all kinds are being used in policing, fire-fighting, inspection of structures, pipelines etc. Recently, the FAA gave its permission for UAVs to be used on film sets for motion capture and high definition video recording. The rapid development in MEMS and actuator technology has made possible a plethora of UAVs that are suited for commercial applications in an increasingly cost effective manner. An emerging popular rotary wing UAV platform is the Quadrotor A Quadrotor is a helicopter with four rotors, that make it more stable; but more complex to model and control. Characteristics that provide a clear advantage over other fixed wing UAVs are VTOL and hovering capabilities as well as a greater maneuverability. It is also simple in construction and design compared to a scaled single rotorcraft. Flying such UAVs using a traditional radio Transmitter-Receiver setup can be a daunting task especially in high stress situations. In order to make such platforms widely applicable, a certain level of autonomy is imperative to the future of such UAVs. This thesis paper presents a methodology for the autonomous control of a Quadrotor UAV using Fuzzy Logic. Fuzzy logic control has been chosen over conventional control methods as it can deal effectively with highly nonlinear systems, allows for imprecise data and is extremely modular. Modularity and adaptability are the key cornerstones of FLC. The objective of this thesis is to present the steps of designing, building and simulating an intelligent flight control module for a Quadrotor UAV. In the course of this research effort, a Quadrotor UAV is indigenously developed utilizing the resources of an online open source project called Aeroquad. System design is comprehensively dealt with. A math model for the Quadrotor is developed and a
PI and fuzzy logic controllers for shunt Active Power Filter--a report.
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.
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.
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.
Use of fuzzy logic in signal processing and validation
Heger, A.S.; Alang-Rashid, N.K. ); Holbert, K.E. )
1993-01-01
The advent of fuzzy logic technology has afforded another opportunity to reexamine the signal processing and validation process (SPV). The features offered by fuzzy logic can lend themselves to a more reliable and perhaps fault-tolerant approach to SPV. This is particularly attractive to complex system operations, where optimal control for safe operation depends on reliable input data. The reason for the use of fuzzy logic as the tool for SPV is its ability to transform information from the linguistic domain to a mathematical domain for processing and then transformation of its result back into the linguistic domain for presentation. To ensure the safe and optimal operation of a nuclear plant, for example, reliable and valid data must be available to the human and computer operators. Based on these input data, the operators determine the current state of the power plant and project corrective actions for future states. This determination is based on available data and the conceptual and mathematical models for the plant. A fault-tolerant SPV based on fuzzy logic can help the operators meet the objective of effective, efficient, and safe operation of the nuclear power plant. The ultimate product of this project will be a code that will assist plant operators in making informed decisions under uncertain conditions when conflicting signals may be present.
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…
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.
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic
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
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic.
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.
Application of fuzzy logic to forecast seasonal runoff
NASA Astrophysics Data System (ADS)
Mahabir, C.; Hicks, F. E.; Robinson Fayek, A.
2003-12-01
Each spring in Alberta, Canada, the potential snowmelt runoff is forecast for several basins to assess the water supply situation. Water managers need this forecast to plan water allocations for the following summer season. The Lodge Creek and Middle Creek basins, located in southeastern Alberta, are two basins that require this type of late winter forecast of potential spring runoff. Historically, the forecast has been based upon a combination of regression equations. These results are then interpreted by a forecaster and are modified based on the forecaster's heuristic knowledge of the basin. Unfortunately, this approach has had limited success in the past, in terms of the accuracy of these forecasts, and consequently an alternative methodology is needed.In this study, the applicability of fuzzy logic modelling techniques for forecasting water supply was investigated. Fuzzy logic has been applied successfully in several fields where the relationship between cause and effect (variable and results) are vague. Fuzzy variables were used to organize knowledge that is expressed linguistically into a formal analysis. For example, high snowpack, average snowpack and low snowpack became variables. By applying fuzzy logic, a water supply forecast was created that classified potential runoff into three forecast zones: low, average and high. Spring runoff forecasts from the fuzzy expert systems were found to be considerably more reliable than the regression models in forecasting the appropriate runoff zone, especially in terms of identifying low or average runoff years. Based on the modelling results in these two basins, it is concluded that fuzzy logic has a promising potential for providing reliable water supply forecasts. Copyright
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.
Prediction of Conductivity by Adaptive Neuro-Fuzzy Model
Akbarzadeh, S.; Arof, A. K.; Ramesh, S.; Khanmirzaei, M. H.; Nor, R. M.
2014-01-01
Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity. PMID:24658582
River suspended sediment modelling using a fuzzy logic approach
NASA Astrophysics Data System (ADS)
Kisi, Ozgur; Emin Karahan, M.; En, Zekai
2006-12-01
Correct estimation of sediment volume carried by a river is very important for many water resources projects. Conventional sediment rating curves, however, are not able to provide sufficiently accurate results. In this paper, a fuzzy logic approach is proposed to estimate suspended sediment concentration from streamflow. This study provides forecasting benchmarks for sediment concentration prediction in the form of a numerical and graphical comparison between fuzzy and rating-curve models. Benchmarking was based on a 5-year period of continuous streamflow and sediment concentration data of Quebrada Blanca Station operated by the United States Geological Survey. The benchmark results showed that the fuzzy model was able to produce much better results than rating-curve models. The fuzzy model proposed in the study is site specific and does not simulate the hysteresis effects. Copyright
Fuzzy Petri net-based programmable logic controller.
Andreu, D; Pascal, J C; Valette, R
1997-01-01
Programmable logic controllers (PLCs) are able to directly implement control sequences specified by means of standard languages such as Grafcet or formal models such as Petri nets. In the case of simple regulation problems between two steps it could be of great interest to introduce a notion of "fuzzy events" in order to denote a continuous evolution from one state to another. This could result from a linear interpolation between the commands attached to two control steps represented by two Petri net (PN) places. This paper is an attempt to develop fuzzy PN-based PLCs in a similar way as fuzzy controllers (regulators). Our approach is based on a combination of Petri nets with possibility theory (Petri nets with fuzzy markings).
Approach to Synchronization Control of Magnetic Bearings Using Fuzzy Logic
NASA Technical Reports Server (NTRS)
Yang, Li-Farn
1996-01-01
This paper presents a fuzzy-logic approach to the synthesis of synchronization control for magnetically suspended rotor system. The synchronization control enables a whirling rotor to undergo synchronous motion along the magnetic bearing axes; thereby avoiding the gyroscopic effect that degrade the stability of rotor systems when spinning at high speed. The control system features a fuzzy controller acting on the magnetic bearing device, in which the fuzzy inference system trained through fuzzy rules to minimize the differential errors between four bearing axes so that an error along one bearing axis can affect the overall control loop for the motion synchronization. Numerical simulations of synchronization control for the magnetically suspended rotor system are presented to show the effectiveness of the present approach.
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.
Heterogeneous fuzzy logic networks: fundamentals and development studies.
Pedrycz, Witold
2004-11-01
The recent trend in the development of neurofuzzy systems has profoundly emphasized the importance of synergy between the fundamentals of fuzzy sets and neural networks. The resulting frameworks of the neurofuzzy systems took advantage of an array of learning mechanisms primarily originating within the theory of neurocomputing and the use of fuzzy models (predominantly rule-based systems) being well established in the realm of fuzzy sets. Ideally, one can anticipate that neurofuzzy systems should fully exploit the linkages between these two technologies while strongly preserving their evident identities (plasticity or learning abilities to be shared by the transparency and full interpretability of the resulting neurofuzzy constructs). Interestingly, this synergy still becomes a target yet to be satisfied. This study is an attempt to address the fundamental interpretability challenge of neurofuzzy systems. Our underlying conjecture is that the transparency of any neurofuzzy system links directly with the logic fabric of the system so the logic fundamentals of the underlying architecture become of primordial relevance. Having this in mind the development of neurofuzzy models hinges on a collection of logic driven processing units named here fuzzy (logic) neurons. These are conceptually simple logic-oriented elements that come with a well-defined semantics and plasticity. Owing to their diversity, such neurons form essential building blocks of the networks. The study revisits the existing categories of logic neurons, provides with their taxonomy, helps understand their functional features and sheds light on their behavior when being treated as computational components of any neurofuzzy architecture. The two main categories of aggregative and reference neurons are deeply rooted in the fundamental operations encountered in the technology of fuzzy sets (including logic operations, linguistic modifiers, and logic reference operations). The developed heterogeneous networks
Modelling of Reservoir Operations using Fuzzy Logic and ANNs
NASA Astrophysics Data System (ADS)
Van De Giesen, N.; Coerver, B.; Rutten, M.
2015-12-01
Today, almost 40.000 large reservoirs, containing approximately 6.000 km3 of water and inundating an area of almost 400.000 km2, can be found on earth. Since these reservoirs have a storage capacity of almost one-sixth of the global annual river discharge they have a large impact on the timing, volume and peaks of river discharges. Global Hydrological Models (GHM) are thus significantly influenced by these anthropogenic changes in river flows. We developed a parametrically parsimonious method to extract operational rules based on historical reservoir storage and inflow time-series. Managing a reservoir is an imprecise and vague undertaking. Operators always face uncertainties about inflows, evaporation, seepage losses and various water demands to be met. They often base their decisions on experience and on available information, like reservoir storage and the previous periods inflow. We modeled this decision-making process through a combination of fuzzy logic and artificial neural networks in an Adaptive-Network-based Fuzzy Inference System (ANFIS). In a sensitivity analysis, we compared results for reservoirs in Vietnam, Central Asia and the USA. ANFIS can indeed capture reservoirs operations adequately when fed with a historical monthly time-series of inflows and storage. It was shown that using ANFIS, operational rules of existing reservoirs can be derived without much prior knowledge about the reservoirs. Their validity was tested by comparing actual and simulated releases with each other. For the eleven reservoirs modelled, the normalised outflow, <0,1>, was predicted with a MSE of 0.002 to 0.044. The rules can be incorporated into GHMs. After a network for a specific reservoir has been trained, the inflow calculated by the hydrological model can be combined with the release and initial storage to calculate the storage for the next time-step using a mass balance. Subsequently, the release can be predicted one time-step ahead using the inflow and storage.
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.
Fuzzy logic applied to prospecting for areas for installation of wood panel industries.
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
2017-02-23
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.
PI and fuzzy logic controllers for shunt Active Power Filter--a report.
Karuppanan, P; Mahapatra, Kamala Kanta
2012-09-01
The authors acknowledge certain errors in their recently published paper titled "PI and fuzzy logic controllers for shunt active power filter--A report.The ambiguity in band width calculation of adaptive hysteresis controller and control aspects of dc-link voltage issues are addressed. The shunt APF system is validated through extensive simulation and the results are support features of the proposed technique.
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.
Neural and Fuzzy Adaptive Control of Induction Motor Drives
Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.
2008-06-12
This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller.
Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model
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
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 temporal logic based railway passenger flow forecast model.
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.
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.
Rule based fuzzy logic approach for classification of fibromyalgia syndrome.
Arslan, Evren; Yildiz, Sedat; Albayrak, Yalcin; Koklukaya, Etem
2016-06-01
Fibromyalgia syndrome (FMS) is a chronic muscle and skeletal system disease observed generally in women, manifesting itself with a widespread pain and impairing the individual's quality of life. FMS diagnosis is made based on the American College of Rheumatology (ACR) criteria. However, recently the employability and sufficiency of ACR criteria are under debate. In this context, several evaluation methods, including clinical evaluation methods were proposed by researchers. Accordingly, ACR had to update their criteria announced back in 1990, 2010 and 2011. Proposed rule based fuzzy logic method aims to evaluate FMS at a different angle as well. This method contains a rule base derived from the 1990 ACR criteria and the individual experiences of specialists. The study was conducted using the data collected from 60 inpatient and 30 healthy volunteers. Several tests and physical examination were administered to the participants. The fuzzy logic rule base was structured using the parameters of tender point count, chronic widespread pain period, pain severity, fatigue severity and sleep disturbance level, which were deemed important in FMS diagnosis. It has been observed that generally fuzzy predictor was 95.56 % consistent with at least of the specialists, who are not a creator of the fuzzy rule base. Thus, in diagnosis classification where the severity of FMS was classified as well, consistent findings were obtained from the comparison of interpretations and experiences of specialists and the fuzzy logic approach. The study proposes a rule base, which could eliminate the shortcomings of 1990 ACR criteria during the FMS evaluation process. Furthermore, the proposed method presents a classification on the severity of the disease, which was not available with the ACR criteria. The study was not limited to only disease classification but at the same time the probability of occurrence and severity was classified. In addition, those who were not suffering from FMS were
Incorporating qualitative knowledge in enzyme kinetic models using fuzzy logic.
Lee, B; Yen, J; Yang, L; Liao, J C
1999-03-20
Modeling of metabolic pathway dynamics requires detailed kinetic equations at the enzyme level. In particular, the kinetic equations must account for metabolite effectors that contribute significantly to the pathway regulation in vivo. Unfortunately, most kinetic rate laws available in the literature do not consider all the effectors simultaneously, and much kinetic information exists in a qualitative or semiquantitative form. In this article, we present a strategy to incorporate such information into the kinetic equation. This strategy uses fuzzy logic-based factors to modify algebraic rate laws that account for partial kinetic characteristics. The parameters introduced by the fuzzy factors are then optimized by use of a hybrid of simplex and genetic algorithms. The resulting model provides a flexible form that can simulate various kinetic behaviors. Such kinetic models are suitable for pathway modeling without complete enzyme mechanisms. Three enzymes in Escherichia coli central metabolism are used as examples: phosphoenolpyruvate carboxylase; phosphoenolpyruvate carboxykinase; and pyruvate kinase I. Results show that, with fuzzy logic-augmented models, the kinetic data can be much better described. In particular, complex behavior, such as allosteric inhibition, can be captured using fuzzy rules. The resulting models, even though they do not provide additional physical meaning in enzyme mechanisms, allow the model to incorporate semiquantitative information in metabolic pathway models.
NASA Technical Reports Server (NTRS)
Abihana, Osama A.; Gonzalez, Oscar R.
1993-01-01
The main objectives of our research are to present a self-contained overview of fuzzy sets and fuzzy logic, develop a methodology for control system design using fuzzy logic controllers, and to design and implement a fuzzy logic controller for a real system. We first present the fundamental concepts of fuzzy sets and fuzzy logic. Fuzzy sets and basic fuzzy operations are defined. In addition, for control systems, it is important to understand the concepts of linguistic values, term sets, fuzzy rule base, inference methods, and defuzzification methods. Second, we introduce a four-step fuzzy logic control system design procedure. The design procedure is illustrated via four examples, showing the capabilities and robustness of fuzzy logic control systems. This is followed by a tuning procedure that we developed from our design experience. Third, we present two Lyapunov based techniques for stability analysis. Finally, we present our design and implementation of a fuzzy logic controller for a linear actuator to be used to control the direction of the Free Flight Rotorcraft Research Vehicle at LaRC.
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.
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.
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: A New Tool for the Analysis and Organization of International Business Communications.
ERIC Educational Resources Information Center
Sondak, Norman E.; Sondak, Eileen M.
Classical western logic, built on a foundation of true/false, yes/no, right/wrong statements, leads to many difficulties and inconsistencies in the logical analysis and organization of international business communications. This paper presents the basic principles of classical logic and of fuzzy logic, a type of logic developed to allow for…
[Purposefulness of using fuzzy logic approaches in the rare disease clinical trials].
Kiseleva, T S; Pagava, K I; Korinteli, I A; Korinteli, M G; Shonvadze, D N
2011-04-01
Different methods being used in the rare diseases clinical trials are examined. There is shown the purposefulness of using fuzzy approaches for such studies. Some advantages of Fuzzy logic methods in comparison with Baysian approach are substantiated.
Distributed traffic signal control using fuzzy logic
NASA Technical Reports Server (NTRS)
Chiu, Stephen
1992-01-01
We present a distributed approach to traffic signal control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. Thus, the signal timing parameters evolve dynamically using only local information to improve traffic flow. This distributed approach provides for a fault-tolerant, highly responsive traffic management system. The signal timing at an intersection is defined by three parameters: cycle time, phase split, and offset. We use fuzzy decision rules to adjust these three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. We show the effectiveness of this method through simulation of the traffic flow in a network of controlled intersections.
Security risk assessment: applying the concepts of fuzzy logic.
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.
The research on high speed underwater target recognition based on fuzzy logic inference
NASA Astrophysics Data System (ADS)
Jiang, Xiang-Dong; Yang, De-Sen; Shi, Sheng-Guo; Li, Si-Chun
2006-06-01
The underwater target recognition is a key technology in acoustic confrontation and underwater defence. In this article, a recognition system based of fuzzy logic inference (FLI) is set up. This system is mainly composed of three parts: the fuzzy input module, the fuzzy logic inference module with a set of inference rules and the de-fuzzy output module. The inference result shows the recognition system is effective in most conditions.
Noise-based logic: Binary, multi-valued, or fuzzy, with optional superposition of logic states
NASA Astrophysics Data System (ADS)
Kish, Laszlo B.
2009-03-01
A new type of deterministic (non-probabilistic) computer logic system inspired by the stochasticity of brain signals is shown. The distinct values are represented by independent stochastic processes: independent voltage (or current) noises. The orthogonality of these processes provides a natural way to construct binary or multi-valued logic circuitry with arbitrary number N of logic values by using analog circuitry. Moreover, the logic values on a single wire can be made a (weighted) superposition of the N distinct logic values. Fuzzy logic is also naturally represented by a two-component superposition within the binary case ( N=2). Error propagation and accumulation are suppressed. Other relevant advantages are reduced energy dissipation and leakage current problems, and robustness against circuit noise and background noises such as 1/f, Johnson, shot and crosstalk noise. Variability problems are also non-existent because the logic value is an AC signal. A similar logic system can be built with orthogonal sinusoidal signals (different frequency or orthogonal phase) however that has an extra 1/N type slowdown compared to the noise-based logic system with increasing number of N furthermore it is less robust against time delay effects than the noise-based counterpart.
Fuzzy knowledge base construction through belief networks based on Lukasiewicz logic
NASA Technical Reports Server (NTRS)
Lara-Rosano, Felipe
1992-01-01
In this paper, a procedure is proposed to build a fuzzy knowledge base founded on fuzzy belief networks and Lukasiewicz logic. Fuzzy procedures are developed to do the following: to assess the belief values of a consequent, in terms of the belief values of its logical antecedents and the belief value of the corresponding logical function; and to update belief values when new evidence is available.
Medical application of fuzzy logic: fuzzy patient state in arterial hypertension analysis
NASA Astrophysics Data System (ADS)
Blinowska, Aleksandra; Duckstein, Lucien
1993-12-01
A few existing applications of fuzzy logic in medicine are briefly described and some potential applications are reviewed. The problem of classification of patient states and medical decision making is discussed more in detail and illustrated by the example of a fuzzy rule based model developed to elicit, analyze and reproduce the opinions of multiple medical experts in the case of arterial hypertension. The goal was to reproduce the average coded answers using an adequate fuzzy procedure, here a fuzzy rule. State categories and the initial set of experimental parameters were defined according to medical practice. The fuzzy set membership functions were then assessed for each parameter in each category and a small subset of representative and pertinent parameters selected for each question. The data were split into two sets of 50 patient files each, the calibration set and the validation set. Two evaluation criteria were used: the sum of squared deviations and the sum of deviations. Fuzzy rules were then sought that reproduced the target, which was the average coded answer. Only one fuzzy rule `and' appeared to be necessary to describe the patient state in a continuous way and to approach the target as closely as the majority of experts.
On-line fuzzy logic control of tube bending
NASA Astrophysics Data System (ADS)
Lieh, Junghsen; Li, Wei Jie
2005-11-01
This paper describes the simulation and on-line fuzzy logic control of tube bending. By combining elasticity and plasticity theories, a conventional model was developed. The results from simulation were compared with those obtained from testing. The experimental data reveal that there exists certain level of uncertainty and nonlinearity in tube bending, and its variation could be significant. To overcome this, a on-line fuzzy logic controller with self-tuning capabilities was designed. The advantages of this on-line system are (1) its computational requirement is simple in comparison with more algorithmic-based controllers, and (2) the system does not need prior knowledge of material characteristics. The device includes an AC motor, a servo controller, a forming mechanism, a 3D optical sensor, and a microprocessor. This automated bending machine adopts primary and secondary errors between the actual response and desired output to conduct on-line rule reasoning. Results from testing show that the spring back angle can be effectively compensated by the self- tuning fuzzy system in a real-time fashion.
Trigueros, José Antonio; Piñero, David P; Ismail, Mahmoud M
2016-01-01
AIM 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. METHODS 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). RESULTS 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). CONCLUSION 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. PMID:27500115
Fuzzy Logic-Based Guaranteed Lifetime Protocol for Real-Time Wireless Sensor Networks.
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
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
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.
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 Control for Autonomous Mobile Robot Navigation.
Omrane, Hajer; Masmoudi, Mohamed Slim; Masmoudi, Mohamed
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.
Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation
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
Adaptive fuzzy system for 3-D vision
NASA Technical Reports Server (NTRS)
Mitra, Sunanda
1993-01-01
An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.
Advances In Infection Surveillance and Clinical Decision Support With Fuzzy Sets and Fuzzy Logic.
Koller, Walter; de Bruin, Jeroen S; Rappelsberger, Andrea; Adlassnig, Klaus-Peter
2015-01-01
By the use of extended intelligent information technology tools for fully automated healthcare-associated infection (HAI) surveillance, clinicians can be informed and alerted about the emergence of infection-related conditions in their patients. Moni--a system for monitoring nosocomial infections in intensive care units for adult and neonatal patients--employs knowledge bases that were written with extensive use of fuzzy sets and fuzzy logic, allowing the inherent un-sharpness of clinical terms and the inherent uncertainty of clinical conclusions to be a part of Moni's output. Thus, linguistic as well as propositional uncertainty became a part of Moni, which can now report retrospectively on HAIs according to traditional crisp HAI surveillance definitions, as well as support clinical bedside work by more complex crisp and fuzzy alerts and reminders. This improved approach can bridge the gap between classical retrospective surveillance of HAIs and ongoing prospective clinical-decision-oriented HAI support.
FUZZY LOGIC CONTROL OF ELECTRIC MOTORS AND MOTOR DRIVES: FEASIBILITY STUDY
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...
NASA Technical Reports Server (NTRS)
Starks, Scott; Abdel-Hafeez, Saleh; Usevitch, Bryan
1997-01-01
This paper discusses the implementation of a fuzzy logic system using an ASICs design approach. The approach is based upon combining the inherent advantages of symmetric triangular membership functions and fuzzy singleton sets to obtain a novel structure for fuzzy logic system application development. The resulting structure utilizes a fuzzy static RAM to store the rule-base and the end-points of the triangular membership functions. This provides advantages over other approaches in which all sampled values of membership functions for all universes must be stored. The fuzzy coprocessor structure implements the fuzzification and defuzzification processes through a two-stage parallel pipeline architecture which is capable of executing complex fuzzy computations in less than 0.55us with an accuracy of more than 95%, thus making it suitable for a wide range of applications. Using the approach presented in this paper, a fuzzy logic rule-base can be directly downloaded via a host processor to an onchip rule-base memory with a size of 64 words. The fuzzy coprocessor's design supports up to 49 rules for seven fuzzy membership functions associated with each of the chip's two input variables. This feature allows designers to create fuzzy logic systems without the need for additional on-board memory. Finally, the paper reports on simulation studies that were conducted for several adaptive filter applications using the least mean squared adaptive algorithm for adjusting the knowledge rule-base.
Wang, Chenhui
2016-01-01
In this paper, control of uncertain fractional-order financial chaotic system with input saturation and external disturbance is investigated. The unknown part of the input saturation as well as the system’s unknown nonlinear function is approximated by a fuzzy logic system. To handle the fuzzy approximation error and the estimation error of the unknown upper bound of the external disturbance, fractional-order adaptation laws are constructed. Based on fractional Lyapunov stability theorem, an adaptive fuzzy controller is designed, and the asymptotical stability can be guaranteed. Finally, simulation studies are given to indicate the effectiveness of the proposed method. PMID:27783648
Toward a fuzzy logic control of the infant incubator.
Reddy, Narender P; Mathur, Garima; Hariharan, S I
2009-10-01
Premature birth is a world wide problem. Thermo regulation is a major problem in premature infants. Premature infants are often kept in infant incubators providing convective heating. Currently either the incubator air temperature is sensed and used to control the heat flow, or infant's skin temperature is sensed and used in the close loop control. Skin control often leads to large fluctuations in the incubator air temperature. Air control also leads to skin temperature fluctuations. The question remains if both the infant's skin temperature and the incubator air temperature can be simultaneously used in the control. The purpose of the present study was to address this question by developing a fuzzy logic control which incorporates both incubator air temperature and infant's skin temperature to control the heating. The control was evaluated using a lumped parameter mathematical model of infant-incubator system (Simon, B. N., N. P. Reddy, and A. Kantak, J. Biomech. Eng. 116:263-266, 1994). Simulation results confirmed previous experimental results that the on-off skin control could lead to fluctuations in the incubator air temperature, and the air control could lead to too slow rise time in the core temperature. The fuzzy logic provides a smooth control with the desired rise time.
Estimating outcomes in newborn infants using fuzzy logic
Chaves, Luciano Eustáquio; Nascimento, Luiz Fernando C.
2014-01-01
OBJECTIVE: To build a linguistic model using the properties of fuzzy logic to estimate the risk of death of neonates admitted to a Neonatal Intensive Care Unit. METHODS: Computational model using fuzzy logic. The input variables of the model were birth weight, gestational age, 5th-minute Apgar score and inspired fraction of oxygen in newborn infants admitted to a Neonatal Intensive Care Unit of Taubaté, Southeast Brazil. The output variable was the risk of death, estimated as a percentage. Three membership functions related to birth weight, gestational age and 5th-minute Apgar score were built, as well as two functions related to the inspired fraction of oxygen; the risk presented five membership functions. The model was developed using the Mandani inference by means of Matlab(r) software. The model values were compared with those provided by experts and their performance was estimated by ROC curve. RESULTS: 100 newborns were included, and eight of them died. The model estimated an average possibility of death of 49.7±29.3%, and the possibility of hospital discharge was 24±17.5%. These values are different when compared by Student's t-test (p<0.001). The correlation test revealed r=0.80 and the performance of the model was 81.9%. CONCLUSIONS: This predictive, non-invasive and low cost model showed a good accuracy and can be applied in neonatal care, given the easiness of its use. PMID:25119746
Automated maneuver planning using a fuzzy logic algorithm
NASA Technical Reports Server (NTRS)
Conway, D.; Sperling, R.; Folta, D.; Richon, K.; Defazio, R.
1994-01-01
Spacecraft orbital control requires intensive interaction between the analyst and the system used to model the spacecraft trajectory. For orbits with right mission constraints and a large number of maneuvers, this interaction is difficult or expensive to accomplish in a timely manner. Some automation of maneuver planning can reduce these difficulties for maneuver-intensive missions. One approach to this automation is to use fuzzy logic in the control mechanism. Such a prototype system currently under development is discussed. The Tropical Rainfall Measurement Mission (TRMM) is one of several missions that could benefit from automated maneuver planning. TRMM is scheduled for launch in August 1997. The spacecraft is to be maintained in a 350-km circular orbit throughout the 3-year lifetime of the mission, with very small variations in this orbit allowed. Since solar maximum will occur as early as 1999, the solar activity during the TRMM mission will be increasing. The increasing solar activity will result in orbital maneuvers being performed as often as every other day. The results of automated maneuver planning for the TRMM mission will be presented to demonstrate the prototype of the fuzzy logic tool.
Fuzzy logic anti-skid control for commercial trucks
NASA Astrophysics Data System (ADS)
Akey, Mark L.
1995-06-01
A fuzzy logic (FL) anti-skid brake controller (ABS) is proposed as the next generation ABS replacing current generation finite state (FS) control. The FL controller is part of a commercial truck braking system, encompassing reverse front-back braking proportions on an articulated vehicle as compared to that found on fixed, passenger car systems. In this early research, the FL controller must satisfy three goals. The first goal is to produce superior braking distances over that of the finite state controller, specifically under low (mu) conditions. The second goal is to provide superior braking under varying system conditions (road surface conditions, physical brake parameters, wheel velocity sensor parameters). The third goal is to provide a convenient, flexible, and tractable ABS solution which is amenable to redevelopemnt to different vehicular platforms. Monte Carlo simulation results illustrate stopping distance improvements of 5 to 10 % averaged over all (mu) surfaces for varying wheel loads. On low (mu) surfaces, the improvement increases to 15% (up to a full tractor-trailer length). These results are obtained while varying other system parameters demonstrating robustness. Finally, the fuzzy logic rule sets and the overall configuration illustrate a straight-forward design and maturation process for the rule sets.
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.
Landslide Susceptibility Assessment Through Fuzzy Logic Inference System (flis)
NASA Astrophysics Data System (ADS)
Bibi, T.; Gul, Y.; Rahman, A. Abdul; Riaz, M.
2016-09-01
Landslide is among one of the most important natural hazards that lead to modification of the environment. It is a regular feature of a rapidly growing district Mansehra, Pakistan. This caused extensive loss of life and property in the district located at the foothills of Himalaya. Keeping in view the situation it is concluded that besides structural approaches the non-structural approaches such as hazard and risk assessment maps are effective tools to reduce the intensity of damage. A landslide susceptibility map is base for engineering geologists and geomorphologists. However, it is not easy to produce a reliable susceptibility map due to complex nature of landslides. Since 1980s, several mathematical models have been developed to map landslide susceptibility and hazard. Among various models this paper is discussing the effectiveness of fuzzy logic approach for landslide susceptibility mapping in District Mansehra, Pakistan. The factor maps were modified as landslide susceptibility and fuzzy membership functions were assessed for each class. Likelihood ratios are obtained for each class of contributing factors by considering the expert opinion. The fuzzy operators are applied to generate landslide susceptibility maps. According to this map, 17% of the study area is classified as high susceptibility, 32% as moderate susceptibility, 51% as low susceptibility and areas. From the results it is found that the fuzzy model can integrate effectively with various spatial data for landslide hazard mapping, suggestions in this study are hope to be helpful to improve the applications including interpretation, and integration phases in order to obtain an accurate decision supporting layer.
Adaptive Neuro-fuzzy approach in friction identification
NASA Astrophysics Data System (ADS)
Zaiyad Muda @ Ismail, Muhammad
2016-05-01
Friction is known to affect the performance of motion control system, especially in terms of its accuracy. Therefore, a number of techniques or methods have been explored and implemented to alleviate the effects of friction. In this project, the Artificial Intelligent (AI) approach is used to model the friction which will be then used to compensate the friction. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is chosen among several other AI methods because of its reliability and capabilities of solving complex computation. ANFIS is a hybrid AI-paradigm that combines the best features of neural network and fuzzy logic. This AI method (ANFIS) is effective for nonlinear system identification and compensation and thus, being used in this project.
Adaptive fuzzy modeling of the hypnotic process in anesthesia.
Marrero, A; Méndez, J A; Reboso, J A; Martín, I; Calvo, J L
2017-04-01
This paper addresses the problem of patient model synthesis in anesthesia. Recent advanced drug infusion mechanisms use a patient model to establish the proper drug dose. However, due to the inherent complexity and variability of the patient dynamics, difficulty obtaining a good model is high. In this paper, a method based on fuzzy logic and genetic algorithms is proposed as an alternative to standard compartmental models. The model uses a Mamdani type fuzzy inference system developed in a two-step procedure. First, an offline model is obtained using information from real patients. Then, an adaptive strategy that uses genetic algorithms is implemented. The validation of the modeling technique was done using real data obtained from real patients in the operating room. Results show that the proposed method based on artificial intelligence appears to be an improved alternative to existing compartmental methodologies.
Application of fuzzy logic to feature extraction from images of agricultural material
NASA Astrophysics Data System (ADS)
Thompson, Bruce T.
1999-11-01
Imaging technology has extended itself from performing gauging on machined parts, to verifying labeling on consumer products, to quality inspection of a variety of man-made and natural materials. Much of this has been made possible by faster computers and algorithms used to extract useful information from the image. In the application of agricultural material, specifically tobacco leaves, the tremendous amount of natural variability in color and texture creates new challenges to image feature extraction. As with many imaging applications, the problem can be expressed as `I see it in the image, how can I get the computer to recognize it?' In this application, the goal is to measure the amount of thick stem pieces in an image of tobacco leaves. By backlighting the leaf, the stems appear dark on a lighter background. The difference in lightness of leaf versus darkness of stem is dependent on the orientation of the leaf and the amount of folding. Because of this, any image thresholding approach must be adaptive. Another factor that allows us to identify the stem from the leaf is shape. The stem is long and narrow, while dark folded leaf is larger and more oblate. These criteria under the image collection limitations create a good application for fuzzy logic. Several generalized classification algorithms, such as fuzzy c-means and fuzzy learning vector quantization, are evaluated and compared. In addition, fuzzy thresholding based on image shape and compactness are applied to this application.
Force control of a tri-layer conducting polymer actuator using optimized fuzzy logic control
NASA Astrophysics Data System (ADS)
Itik, Mehmet; Sabetghadam, Mohammadreza; Alici, Gursel
2014-12-01
Conducting polymers actuators (CPAs) are potential candidates for replacing conventional actuators in various fields, such as robotics and biomedical engineering, due to their advantageous properties, which includes their low cost, light weight, low actuation voltage and biocompatibility. As these actuators are very suitable for use in micro-nano manipulation and in injection devices in which the magnitude of the force applied to the target is of crucial importance, the force generated by CPAs needs to be accurately controlled. In this paper, a fuzzy logic (FL) controller with a Mamdani inference system is designed to control the blocking force of a trilayer CPA with polypyrrole electrodes, which operates in air. The particle swarm optimization (PSO) method is employed to optimize the controller’s membership function parameters and therefore enhance the performance of the FL controller. An adaptive neuro-fuzzy inference system model, which can capture the nonlinear dynamics of the actuator, is utilized in the optimization process. The optimized Mamdani FL controller is then implemented on the CPA experimentally, and its performance is compared with a non-optimized fuzzy controller as well as with those obtained from a conventional PID controller. The results presented indicate that the blocking force at the tip of the CPA can be effectively controlled by the optimized FL controller, which shows excellent transient and steady state characteristics but increases the control voltage compared to the non-optimized fuzzy controllers.
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.
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.
Qualitative information modeling: The role of fuzzy logic in project economic evaluations
Warnken, P.G.
1995-12-31
Conventional models rely on a precise mathematical formalism to express the quantitative essentials of the system being modeled. In contrast, decisionmakers in the real world employ cognitive skills to process information and arrive at decisions based on judgement and experience. Bridging the gap between the two analytic approaches -- that is, formulating intelligent models -- has met with very limited success using traditional computational methods. The difficulty stems from two problems. First, imprecision, which is the distinguishing feature of qualitative factors, is an information attribute that is not easily computable using the rules of traditional set theory and Boolean (bivalent) logic. Second, cognitive information processing is cumbersome using the numerical rule-based approaches common in today`s expert systems. Fuzzy models overcome these problems by employing new mathematical rules for expressing and processing knowledge. These rules are based on fuzzy logic. Fuzzy logic is the formal symbolic language used to represent linguistic terms and verbal rules for computational and modeling purposes. This language provides model builders with the means to incorporate subjective judgements, imprecise information, and human reasoning capabilities as part of a model`s framework. This paper outlines the concepts needed to understand fuzzy modeling systems. The key concepts discussed include fuzzy sets, fuzzy logical operators, linguistic variables, and verbal rules. A simple fuzzy economic rating model for project investments is presented to demonstrate the fuzzy modeling technique. Finally, the paper discusses the role of fuzzy logic in the economic modeling process.
A Modern Syllogistic Method in Intuitionistic Fuzzy Logic with Realistic Tautology.
Rushdi, Ali Muhammad; Zarouan, Mohamed; Alshehri, Taleb Mansour; Rushdi, Muhammad Ali
2015-01-01
The Modern Syllogistic Method (MSM) of propositional logic ferrets out from a set of premises all that can be concluded from it in the most compact form. The MSM combines the premises into a single function equated to 1 and then produces the complete product of this function. Two fuzzy versions of MSM are developed in Ordinary Fuzzy Logic (OFL) and in Intuitionistic Fuzzy Logic (IFL) with these logics augmented by the concept of Realistic Fuzzy Tautology (RFT) which is a variable whose truth exceeds 0.5. The paper formally proves each of the steps needed in the conversion of the ordinary MSM into a fuzzy one. The proofs rely mainly on the successful replacement of logic 1 (or ordinary tautology) by an RFT. An improved version of Blake-Tison algorithm for generating the complete product of a logical function is also presented and shown to be applicable to both crisp and fuzzy versions of the MSM. The fuzzy MSM methodology is illustrated by three specific examples, which delineate differences with the crisp MSM, address the question of validity values of consequences, tackle the problem of inconsistency when it arises, and demonstrate the utility of the concept of Realistic Fuzzy Tautology.
A Modern Syllogistic Method in Intuitionistic Fuzzy Logic with Realistic Tautology
Rushdi, Ali Muhammad; Zarouan, Mohamed; Alshehri, Taleb Mansour; Rushdi, Muhammad Ali
2015-01-01
The Modern Syllogistic Method (MSM) of propositional logic ferrets out from a set of premises all that can be concluded from it in the most compact form. The MSM combines the premises into a single function equated to 1 and then produces the complete product of this function. Two fuzzy versions of MSM are developed in Ordinary Fuzzy Logic (OFL) and in Intuitionistic Fuzzy Logic (IFL) with these logics augmented by the concept of Realistic Fuzzy Tautology (RFT) which is a variable whose truth exceeds 0.5. The paper formally proves each of the steps needed in the conversion of the ordinary MSM into a fuzzy one. The proofs rely mainly on the successful replacement of logic 1 (or ordinary tautology) by an RFT. An improved version of Blake-Tison algorithm for generating the complete product of a logical function is also presented and shown to be applicable to both crisp and fuzzy versions of the MSM. The fuzzy MSM methodology is illustrated by three specific examples, which delineate differences with the crisp MSM, address the question of validity values of consequences, tackle the problem of inconsistency when it arises, and demonstrate the utility of the concept of Realistic Fuzzy Tautology. PMID:26380357
Fuzzy Adaptive Control for Intelligent Autonomous Space Exploration Problems
NASA Technical Reports Server (NTRS)
Esogbue, Augustine O.
1998-01-01
The principal objective of the research reported here is the re-design, analysis and optimization of our newly developed neural network fuzzy adaptive controller model for complex processes capable of learning fuzzy control rules using process data and improving its control through on-line adaption. The learned improvement is according to a performance objective function that provides evaluative feedback; this performance objective is broadly defined to meet long-range goals over time. Although fuzzy control had proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are either inefficient, impractical or cannot be derived, the state of the art prior to our work showed that procedures for deriving fuzzy control, however, were mostly ad hoc heuristics. The learning ability of neural networks was exploited to systematically derive fuzzy control and permit on-line adaption and in the process optimize control. The operation of neural networks integrates very naturally with fuzzy logic. The neural networks which were designed and tested using simulation software and simulated data, followed by realistic industrial data were reconfigured for application on several platforms as well as for the employment of improved algorithms. The statistical procedures of the learning process were investigated and evaluated with standard statistical procedures (such as ANOVA, graphical analysis of residuals, etc.). The computational advantage of dynamic programming-like methods of optimal control was used to permit on-line fuzzy adaptive control. Tests for the consistency, completeness and interaction of the control rules were applied. Comparisons to other methods and controllers were made so as to identify the major advantages of the resulting controller model. Several specific modifications and extensions were made to the original controller. Additional modifications and explorations have been proposed for further study. Some of
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.
Wastewater neutralization control based on fuzzy logic: Experimental results
Adroer, M.; Alsina, A.; Aumatell, J.; Poch, M.
1999-07-01
Many industrial wastes contain acidic or alkaline materials that require neutralization of previous discharge into receiving waters or to chemical and biological treatment plants. The control of the wastewater neutralization process is subjected to several difficulties, such as the highly nonlinear titration curve (with special sensitivity around neutrality), the unknown water composition, the variable buffering capacity of the system, and the changes in input loading. To deal with these problems, this study proposes a fixed fuzzy logic controller (FLC) structure coupled with a tuning factor. The versatility and robustness of this controller has been proved when faced with solutions of variable buffering capacity, with acids that cover a wide pK range and with switches between acids throughout the course of a test. Laboratory experiments and simulation runs using the proposed controller were successful in a wide operational range.
A fuzzy logic approach to marine spatial management.
Teh, Lydia C L; Teh, Louise S L
2011-04-01
Marine spatial planning tends to prioritise biological conservation targets over socio-economic considerations, which may incur lower user compliance and ultimately compromise management success. We argue for more inclusion of human dimensions in spatial management, so that outcomes not only fulfill biodiversity and conservation objectives, but are also acceptable to resource users. We propose a fuzzy logic framework that will facilitate this task- The protected area suitability index (PASI) combines fishers' spatial preferences with biological criteria to assess site suitability for protection from fishing. We apply the PASI in a spatial evaluation of a small-scale reef fishery in Sabah, Malaysia. While our results pertain to fishers specifically, the PASI can also be customized to include the interests of other stakeholders and resource users, as well as incorporate varying levels of protection.
Controlling of grid connected photovoltaic lighting system with fuzzy logic
Saglam, Safak; Ekren, Nazmi; Erdal, Hasan
2010-02-15
In this study, DC electrical energy produced by photovoltaic panels is converted to AC electrical energy and an indoor area is illuminated using this energy. System is controlled by fuzzy logic algorithm controller designed with 16 rules. Energy is supplied from accumulator which is charged by photovoltaic panels if its energy would be sufficient otherwise it is supplied from grid. During the 1-week usage period at the semester time, 1.968 kWh energy is used from grid but designed system used 0.542 kWh energy from photovoltaic panels at the experiments. Energy saving is determined by calculations and measurements for one education year period (9 months) 70.848 kWh. (author)
Fuzzy Logic Based Rotor Health Index of Induction Motor
NASA Astrophysics Data System (ADS)
Misra, Rajul; Pahuja, G. L.
2015-10-01
This paper presents an experimental study on detection and diagnosis of broken rotor bars in Squirrel Cage Induction Motor (SQIM). The proposed scheme is based on Motor Current Signature Analysis (MCSA) which uses amplitude difference of supply frequency to upper and lower side bands. Initially traditional MCSA has been used for rotor fault detection. It provides rotor health index on full load conditions. However in real practice if a fault occurs motor can not run at full load. To overcome the issue of reduced load condition a Fuzzy Logic based MCSA has been designed, implemented, tested and compared with traditional MCSA. A simulation result shows that proposed scheme is not only capable of detecting the severity of rotor fault but also provides remarkable performance at reduced load conditions.
Severity classification for idiopathic pulmonary fibrosis by using fuzzy logic
Lopes, Agnaldo José; Capone, Domenico; Mogami, Roberto; Lanzillotti, Regina Serrão; de Melo, Pedro Lopes; Jansen, José Manoel
2011-01-01
OBJECTIVE: To set out a severity classification for idiopathic pulmonary fibrosis (IPF) based on the interaction of pulmonary function parameters with high resolution computed tomography (CT) findings. INTRODUCTION: Despite the contribution of functional and radiological methods in the study of IPF, there are few classification proposals for the disease based on these examinations. METHODS: A cross-sectional study was carried out, in which 41 non-smoking patients with IPF were evaluated. The following high resolution CT findings were quantified using a semi-quantitative scoring system: reticular abnormality, honeycombing and ground-glass opacity. The functional variables were measured by spirometry, forced oscillation technique, helium dilution method, as well as the single-breath method of diffusing capacity of carbon monoxide. With the interaction between functional indexes and high resolution CT scores through fuzzy logic, a classification for IPF has been built. RESULTS: Out of 41 patients studied, 26 were male and 15 female, with a mean age of 70.8 years. Volume measurements were the variables which showed the best interaction with the disease extension on high resolution CT, while the forced vital capacity showed the lowest estimative errors in comparison to total lung capacity. A classification for IPF was suggested based on the 95% confidence interval of the forced vital capacity %: mild group (≥92.7); moderately mild (76.9–92.6); moderate (64.3–76.8%); moderately severe (47.1–64.2); severe (24.3–47.0); and very severe (<24.3). CONCLUSION: Through fuzzy logic, an IPF classification was built based on forced vital capacity measurement with a simple practical application. PMID:21808868
A Fuzzy Logic Framework for Integrating Multiple Learned Models
Hartog, Bobi Kai Den
1999-03-01
The Artificial Intelligence field of Integrating Multiple Learned Models (IMLM) explores ways to combine results from sets of trained programs. Aroclor Interpretation is an ill-conditioned problem in which trained programs must operate in scenarios outside their training ranges because it is intractable to train them completely. Consequently, they fail in ways related to the scenarios. We developed a general-purpose IMLM solution, the Combiner, and applied it to Aroclor Interpretation. The Combiner's first step, Scenario Identification (M), learns rules from very sparse, synthetic training data consisting of results from a suite of trained programs called Methods. S1 produces fuzzy belief weights for each scenario by approximately matching the rules. The Combiner's second step, Aroclor Presence Detection (AP), classifies each of three Aroclors as present or absent in a sample. The third step, Aroclor Quantification (AQ), produces quantitative values for the concentration of each Aroclor in a sample. AP and AQ use automatically learned empirical biases for each of the Methods in each scenario. Through fuzzy logic, AP and AQ combine scenario weights, automatically learned biases for each of the Methods in each scenario, and Methods' results to determine results for a sample.
Robust observer-based adaptive fuzzy sliding mode controller
NASA Astrophysics Data System (ADS)
Oveisi, Atta; Nestorović, Tamara
2016-08-01
In this paper, a new observer-based adaptive fuzzy integral sliding mode controller is proposed based on the Lyapunov stability theorem. The plant is subjected to a square-integrable disturbance and is assumed to have mismatch uncertainties both in state- and input-matrices. Based on the classical sliding mode controller, the equivalent control effort is obtained to satisfy the sufficient requirement of sliding mode controller and then the control law is modified to guarantee the reachability of the system trajectory to the sliding manifold. In order to relax the norm-bounded constrains on the control law and solve the chattering problem of sliding mode controller, a fuzzy logic inference mechanism is combined with the controller. An adaptive law is then introduced to tune the parameters of the fuzzy system on-line. Finally, for evaluating the controller and the robust performance of the closed-loop system, the proposed regulator is implemented on a real-time mechanical vibrating system.
NASA Astrophysics Data System (ADS)
Huo, Baoyu; Tong, Shaocheng; Li, Yongming
2013-12-01
This article develops an adaptive fuzzy control method for accommodating actuator faults in a class of unknown nonlinear systems with unmeasured states. The considered faults are modelled as both loss of effectiveness and lock-in-place (stuck at unknown place). With the help of fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy adaptive observer is developed for estimating the unmeasured states. Combining the backstepping technique with the nonlinear tolerant-fault control theory, a novel adaptive fuzzy faults-tolerant control approach is constructed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded and the tracking error between the system output and the reference signal converges to a small neighbourhood of zero by appropriate choice of the design parameters. Simulation results are provided to show the effectiveness of the control approach.
Decomposed fuzzy systems and their application in direct adaptive fuzzy control.
Hsueh, Yao-Chu; Su, Shun-Feng; Chen, Ming-Chang
2014-10-01
In this paper, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator for adaptive fuzzy control systems. The proposed structure is to decompose each fuzzy variable into layers of fuzzy systems, and each layer is to characterize one traditional fuzzy set. Similar to forming fuzzy rules in traditional fuzzy systems, layers from different variables form the so-called component fuzzy systems. DFS is proposed to provide more adjustable parameters to facilitate possible adaptation in fuzzy rules, but without introducing a learning burden. It is because those component fuzzy systems are independent so that it can facilitate minimum distribution learning effects among component fuzzy systems. It can be seen from our experiments that even when the rule number increases, the learning time in terms of cycles is still almost constant. It can also be found that the function approximation capability and learning efficiency of the DFS are much better than that of the traditional fuzzy systems when employed in adaptive fuzzy control systems. Besides, in order to further reduce the computational burden, a simplified DFS is proposed in this paper to satisfy possible real time constraints required in many applications. From our simulation results, it can be seen that the simplified DFS can perform fairly with a more concise decomposition structure.
Preventive Maintenance Prioritization by Fuzzy Logic for Seamless Hydro Power Generation
NASA Astrophysics Data System (ADS)
Roy, P. K.; Adhikary, P.; Mazumdar, A.
2014-06-01
Preventive maintenance prioritization is one of the most important criteria for the electricity generation planners to minimize the down time and production costs. Break down of equipments increases costs and plant down time results in loss of business. This work focuses on prioritizing the preventive maintenance for seamless hydro power generation considering (24 × 7) client's power demand using fuzzy logic. The main task involves prioritizing the maintenance work considering constraints of varied power demand and hydro turbine plant breakdown. Fuzzy logic is used to optimize the preventive maintenance prioritization under the main constraints. Manual fuzzy arithmetic is used to develop the model and MATLAB Fuzzy Inference System editor used to validate the same. This novel fuzzy logic approach of preventive maintenance prioritizing for hydro power generation is absent in renewable power generation and industrial engineering literatures due to its assessment complexity.
A fuzzy logic sliding mode controlled electronic differential for a direct wheel drive EV
NASA Astrophysics Data System (ADS)
Ozkop, Emre; Altas, Ismail H.; Okumus, H. Ibrahim; Sharaf, Adel M.
2015-11-01
In this study, a direct wheel drive electric vehicle based on an electronic differential system with a fuzzy logic sliding mode controller (FLSMC) is studied. The conventional sliding surface is modified using a fuzzy rule base to obtain fuzzy dynamic sliding surfaces by changing its slopes using the global error and its derivative in a fuzzy logic inference system. The controller is compared with proportional-integral-derivative (PID) and sliding mode controllers (SMCs), which are usually preferred to be used in industry. The proposed controller provides robustness and flexibility to direct wheel drive electric vehicles. The fuzzy logic sliding mode controller, electronic differential system and the overall electrical vehicle mechanism are modelled and digitally simulated by using the Matlab software. Simulation results show that the system with FLSMC has better efficiency and performance compared to those of PID and SMCs.
Fuzzy logic feedback control for fed-batch enzymatic hydrolysis of lignocellulosic biomass.
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 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.
Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.
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.
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.
Use of fuzzy logic in lignite inventory estimation
Tutmez, B.; Dag, A.
2007-07-01
Seam thickness is one of the most important parameters for reserve estimation of a lignite deposit. This paper addresses a case study on fuzzy estimation of lignite seam thickness from spatial coordinates. From the relationships between input (Cartesian coordinates) and output (thickness) parameters, fuzzy clustering and a fuzzy rule-based inference system were designed. Data-driven fuzzy model parameters were derived from numerical values directly. In addition, estimations of the fuzzy model were compared with kriging estimations. It was concluded that the performance ofthe fuzzy model was more satisfactory. The results indicated that the fuzzy modeling approach is very reliable for the estimation of lignite reserves.
Neural Network and Fuzzy Logic Technology for Naval Flight Control Systems
1991-08-06
it is still uncertain what neural network and fuzzy logic functions are both technologically feasible and suitable for flight control system...this program is focused on the development of a neural network FCS design tool, a neural network flight control law emulator, a fuzzy logic automatic...carrier landing system and a neural network flight control configuration management system. For each project, some initial results are given. Also
MRI and PET image fusion using fuzzy logic and image local features.
Javed, Umer; Riaz, Muhammad Mohsin; Ghafoor, Abdul; Ali, Syed Sohaib; Cheema, Tanveer Ahmed
2014-01-01
An image fusion technique for magnetic resonance imaging (MRI) and positron emission tomography (PET) using local features and fuzzy logic is presented. The aim of proposed technique is to maximally combine useful information present in MRI and PET images. Image local features are extracted and combined with fuzzy logic to compute weights for each pixel. Simulation results show that the proposed scheme produces significantly better results compared to state-of-art schemes.
Depth Control of Sevofluorane Anesthesia with Microcontroller Based Fuzzy Logic System
2007-11-02
sevoflurane in humans”, Anesthesiology, 66:301-303, 1987 [10].YARDIMCI, A., ONURAL A.,”Fuzzy Logic Control of Child Blood Pressure During Anaesthesia...microcontroller-based fuzzy logic control system according to the blood pressure and heart rate taken from the patient. The potential benefits of the... blood pressure and hearth rate. The main reason for automating the control of depth anesthesia is to release the anesthesiologist so that he or
Barbosa, A Márcia; Real, Raimundo
2012-01-01
We modelled the distributions of two toads (Bufo bufo and Epidalea calamita) in the Iberian Peninsula using the favourability function, which makes predictions directly comparable for different species and allows fuzzy logic operations to relate different models. The fuzzy intersection between individual models, representing favourability for the presence of both species simultaneously, was compared with another favourability model built on the presences shared by both species. The fuzzy union between individual models, representing favourability for the presence of any of the two species, was compared with another favourability model based on the presences of either or both of them. The fuzzy intersections between favourability for each species and the complementary of favourability for the other (corresponding to the logical operation "A and not B") were compared with models of exclusive presence of one species versus the exclusive presence of the other. The results of modelling combined species data were highly similar to those of fuzzy logic operations between individual models, proving fuzzy logic and the favourability function valuable for comparative distribution modelling. We highlight several advantages of fuzzy logic over other forms of combining distribution models, including the possibility to combine multiple species models for management and conservation planning.
Barbosa, A. Márcia; Real, Raimundo
2012-01-01
We modelled the distributions of two toads (Bufo bufo and Epidalea calamita) in the Iberian Peninsula using the favourability function, which makes predictions directly comparable for different species and allows fuzzy logic operations to relate different models. The fuzzy intersection between individual models, representing favourability for the presence of both species simultaneously, was compared with another favourability model built on the presences shared by both species. The fuzzy union between individual models, representing favourability for the presence of any of the two species, was compared with another favourability model based on the presences of either or both of them. The fuzzy intersections between favourability for each species and the complementary of favourability for the other (corresponding to the logical operation “A and not B”) were compared with models of exclusive presence of one species versus the exclusive presence of the other. The results of modelling combined species data were highly similar to those of fuzzy logic operations between individual models, proving fuzzy logic and the favourability function valuable for comparative distribution modelling. We highlight several advantages of fuzzy logic over other forms of combining distribution models, including the possibility to combine multiple species models for management and conservation planning. PMID:22629142
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.
Neural-network-based fuzzy logic control system with applications on compliant robot control
NASA Astrophysics Data System (ADS)
Hor, MawKae; Lu, Hui L.
1994-10-01
In view of the success of neural network applications in inverted pendulum control, speech recognition, and other problem solving, we believe that one could inject the noise removing concepts and learning spirits into the algorithm in constructing the neural networks and apply it to the various tasks such as compliant coordinated motion using multiple robots. Based on the fuzzy logic, a fuzzy logical control system is a logical system which is much closer to human thinking than any other logical systems. During recent years, fuzzy logic control has emerged as a fruitful area in applications, especially the applications lacking quantitative data regarding the input-output relations. Whereas, the connectionist model injects the learning ability to the fuzzy logic system. This model, proposed by Lin and Lee, is a connected neural network that embedded the fuzzy rules in the architecture. Since this model is general enough and we expect the embedded fuzzy concepts can solve the problems caused by the defective training data, it is chosen as our base structure. Appropriate modifications have been made to this model to reflect the real situations encountered in the robot applications. Our goal is to control two different types of robots for coordinated motion using sensory feedback information.
NASA Astrophysics Data System (ADS)
Tong, Shaocheng; Xu, Yinyin; Li, Yongming
2015-06-01
This paper is concerned with the problem of adaptive fuzzy decentralised output-feedback control for a class of uncertain stochastic nonlinear pure-feedback large-scale systems with completely unknown functions, the mismatched interconnections and without requiring the states being available for controller design. With the help of fuzzy logic systems approximating the unknown nonlinear functions, a fuzzy state observer is designed estimating the unmeasured states. Therefore, the nonlinear filtered signals are incorporated into the backstepping recursive design, and an adaptive fuzzy decentralised output-feedback control scheme is developed. It is proved that the filter system converges to a small neighbourhood of the origin based on appropriate choice of the design parameters. Simulation studies are included illustrating the effectiveness of the proposed approach.
NASA Astrophysics Data System (ADS)
Derrouazin, A.; Aillerie, M.; Mekkakia-Maaza, N.; Charles, J. P.
2016-07-01
Several researches for management of diverse hybrid energy systems and many techniques have been proposed for robustness, savings and environmental purpose. In this work we aim to make a comparative study between two supervision and control techniques: fuzzy and classic logics to manage the hybrid energy system applied for typical housing fed by solar and wind power, with rack of batteries for storage. The system is assisted by the electric grid during energy drop moments. A hydrogen production device is integrated into the system to retrieve surplus energy production from renewable sources for the household purposes, intending the maximum exploitation of these sources over years. The models have been achieved and generated signals for electronic switches command of proposed both techniques are presented and discussed in this paper.
NASA Astrophysics Data System (ADS)
Li, Yongming; Tong, Shaocheng
2016-10-01
In this paper, a fuzzy adaptive switched control approach is proposed for a class of uncertain nonholonomic chained systems with input nonsmooth constraint. In the control design, an auxiliary dynamic system is designed to address the input nonsmooth constraint, and an adaptive switched control strategy is constructed to overcome the uncontrollability problem associated with x0(t0) = 0. By using fuzzy logic systems to tackle unknown nonlinear functions, a fuzzy adaptive control approach is explored based on the adaptive backstepping technique. By constructing the combination approximation technique and using Young's inequality scaling technique, the number of the online learning parameters is reduced to n and the 'explosion of complexity' problem is avoid. It is proved that the proposed method can guarantee that all variables of the closed-loop system converge to a small neighbourhood of zero. Two simulation examples are provided to illustrate the effectiveness of the proposed control approach.
Chen, Shyi-Ming; Manalu, Gandhi Maruli Tua; Pan, Jeng-Shyang; Liu, Hsiang-Chuan
2013-06-01
In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization (PSO) techniques. First, we fuzzify the historical training data of the main factor and the secondary factor, respectively, to form two-factors second-order fuzzy logical relationships. Then, we group the two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, we obtain the optimal weighting vector for each fuzzy-trend logical relationship group by using PSO techniques to perform the forecasting. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index and the NTD/USD exchange rates. The experimental results show that the proposed method gets better forecasting performance than the existing methods.
Sliding mode control of wind-induced vibrations using fuzzy sliding surface and gain adaptation
NASA Astrophysics Data System (ADS)
Thenozhi, Suresh; Yu, Wen
2016-04-01
Although fuzzy/adaptive sliding mode control can reduce the chattering problem in structural vibration control applications, they require the equivalent control and the upper bounds of the system uncertainties. In this paper, we used fuzzy logic to approximate the standard sliding surface and designed a dead-zone adaptive law for tuning the switching gain of the sliding mode control. The stability of the proposed controller is established using Lyapunov stability theory. A six-storey building prototype equipped with an active mass damper has been used to demonstrate the effectiveness of the proposed controller towards the wind-induced vibrations.
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.
Automated mango fruit assessment using fuzzy logic approach
NASA Astrophysics Data System (ADS)
Hasan, Suzanawati Abu; Kin, Teoh Yeong; Sauddin@Sa'duddin, Suraiya; Aziz, Azlan Abdul; Othman, Mahmod; Mansor, Ab Razak; Parnabas, Vincent
2014-06-01
In term of value and volume of production, mango is the third most important fruit product next to pineapple and banana. Accurate size assessment of mango fruits during harvesting is vital to ensure that they are classified to the grade accordingly. However, the current practice in mango industry is grading the mango fruit manually using human graders. This method is inconsistent, inefficient and labor intensive. In this project, a new method of automated mango size and grade assessment is developed using RGB fiber optic sensor and fuzzy logic approach. The calculation of maximum, minimum and mean values based on RGB fiber optic sensor and the decision making development using minimum entropy formulation to analyse the data and make the classification for the mango fruit. This proposed method is capable to differentiate three different grades of mango fruit automatically with 77.78% of overall accuracy compared to human graders sorting. This method was found to be helpful for the application in the current agricultural industry.
Mobile Health in Maternal and Newborn Care: Fuzzy Logic
Premji, Shahirose
2014-01-01
Whether mHealth improves maternal and newborn health outcomes remains uncertain as the response is perhaps not true or false but lies somewhere in between when considering unintended harmful consequences. Fuzzy logic, a mathematical approach to computing, extends the traditional binary “true or false” (one or zero) to exemplify this notion of partial truths that lies between completely true and false. The commentary explores health, socio-ecological and environmental consequences–positive, neutral or negative. Of particular significance is the negative influence of mHealth on maternal care-behaviors, which can increase stress reactivity and vulnerability to stress-induced illness across the lifespan of the child and establish pathways for intergenerational transmission of behaviors. A mHealth “fingerprinting” approach is essential to monitor psychosocial, economic, cultural, environmental and physical impact of mHealth intervention and make evidence-informed decision(s) about use of mHealth in maternal and newborn care. PMID:25003177
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.
Fuzzy logic controller for the electric motor driving the astronomical telescope
NASA Astrophysics Data System (ADS)
Soliman, Hussein F.; Attia, Abdel-Fattah A.; Badr, Mohammed A.; Osman, Anas M.; Gamaleldin, Abdul A.
1998-05-01
The paper presents an application of fuzzy logic controller to regulate the DC motor driver system of astronomical telescope. The mathematical model of such a telescope is highly nonlinear coupled equations. However, the accuracy requirement in telescope system exceed those of other industrial plants. Fuzzy logic controller provides means to deal with nonlinear functions. A fuzzy logic controller (FLC) was designed to enhance the performance of a two-link model of astronomical telescope. The proposed FLC utilizes the position deviation for the desired value, and its rate of change to regulate the armature voltage of the DC motor drive of each link. The final action of FLC is equivalent to PD controller with a variable gain by using an expert look- up table. This work presents the derivation of the mathematical model of 14 inch Celestron telescope and computer simulation of its motion. The FLC contains two groups of fuzzy sets.
Fuzzy-Logic Based Vibration Suppression Control Experiments on Active Structures
NASA Astrophysics Data System (ADS)
Kwak, M. K.; Sciulli, D.
1996-03-01
This paper is concerned with the fuzzy-logic based vibration suppression control of active structures equipped with piezoelectric sensors and actuators. The control methodology is based on the fuzzy logic control of the variable structures system type. The sufficient condition for the closed-loop stability of the decentralized fuzzy control for the system equipped with collocated sensors and actuators is derived from the sufficient condition of the decentralized collocated variable system control. Hence, it is concluded that the fuzzy control is in fact the variation of the variable structure system control in this case. Comparison of the variable structure system to the fuzzy control leads to a new fuzzy rule of the vibration suppression of the active structure equipped with collocated sensors and actuators. It is shown that the fuzzy-logic control can be designed for the collocated system without any knowledge of the system to be controlled. However, this may not be true in the case of multi-input and multi-output non-collocated systems. All the developments are demonstrated by means of a real-time fuzzy control experiment on the cantilever beam with surface-bonded piezoceramic sensors and actuators.
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.
Fuzzy logic path planning system for collision avoidance by an autonomous rover vehicle
NASA Technical Reports Server (NTRS)
Murphy, Michael G.
1993-01-01
The Space Exploration Initiative of the United States will make great demands upon NASA and its limited resources. One aspect of great importance will be providing for autonomous (unmanned) operation of vehicles and/or subsystems in space flight and surface exploration. An additional, complicating factor is that much of the need for autonomy of operation will take place under conditions of great uncertainty or ambiguity. Issues in developing an autonomous collision avoidance subsystem within a path planning system for application in a remote, hostile environment that does not lend itself well to remote manipulation by Earth-based telecommunications is addressed. A good focus is unmanned surface exploration of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. Four major issues addressed are (1) avoidance of a fuzzy moving obstacle; (2) backoff from a deadend in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system. Examples of the need for collision avoidance by an autonomous rover vehicle on the surface of Mars with a moving obstacle would be wind-blown debris, surface flow or anomalies due to subsurface disturbances, another vehicle, etc. The other issues of backoff, sensor fusion, and adaptive learning are important in the overall path planning system.
Adaptive fuzzy segmentation of magnetic resonance images.
Pham, D L; Prince, J L
1999-09-01
An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, we fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, we also describe a new faster multigrid-based algorithm for its implementation. We show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.
Fuzzy Adaptive Control System of a Non-Stationary Plant
NASA Astrophysics Data System (ADS)
Nadezhdin, Igor S.; Goryunov, Alexey G.; Manenti, Flavio
2016-08-01
This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller. In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-07-26
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-01-01
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches. PMID:27472336
Development of Fuzzy Logic and Soft Computing Methodologies
NASA Technical Reports Server (NTRS)
Zadeh, L. A.; Yager, R.
1999-01-01
Our earlier research on computing with words (CW) has led to a new direction in fuzzy logic which points to a major enlargement of the role of natural languages in information processing, decision analysis and control. This direction is based on the methodology of computing with words and embodies a new theory which is referred to as the computational theory of perceptions (CTP). An important feature of this theory is that it can be added to any existing theory - especially to probability theory, decision analysis, and control - and enhance the ability of the theory to deal with real-world problems in which the decision-relevant information is a mixture of measurements and perceptions. The new direction is centered on an old concept - the concept of a perception - a concept which plays a central role in human cognition. The ability to reason with perceptions perceptions of time, distance, force, direction, shape, intent, likelihood, truth and other attributes of physical and mental objects - underlies the remarkable human capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Everyday examples of such tasks are parking a car, driving in city traffic, cooking a meal, playing golf and summarizing a story. Perceptions are intrinsically imprecise. Imprecision of perceptions reflects the finite ability of sensory organs and ultimately, the brain, to resolve detail and store information. More concretely, perceptions are both fuzzy and granular, or, for short, f-granular. Perceptions are f-granular in the sense that: (a) the boundaries of perceived classes are not sharply defined; and (b) the elements of classes are grouped into granules, with a granule being a clump of elements drawn together by indistinguishability, similarity. proximity or functionality. F-granularity of perceptions may be viewed as a human way of achieving data compression. In large measure, scientific progress has been, and continues to be
Fuzzy logic-based prognostic score for outcome prediction in esophageal cancer.
Wang, Chang-Yu; Lee, Tsair-Fwu; Fang, Chun-Hsiung; Chou, Jyh-Horng
2012-11-01
Given the poor prognosis of esophageal cancer and the invasiveness of combined modality treatment, improved prognostic scoring systems are needed. We developed a fuzzy logic-based system to improve the predictive performance of a risk score based on the serum concentrations of C-reactive protein (CRP) and albumin in a cohort of 271 patients with esophageal cancer before radiotherapy. Univariate and multivariate survival analyses were employed to validate the independent prognostic value of the fuzzy risk score. To further compare the predictive performance of the fuzzy risk score with other prognostic scoring systems, time-dependent receiver operating characteristic curve (ROC) analysis was used. Application of fuzzy logic to the serum values of CRP and albumin increased predictive performance for 1-year overall survival (AUC=0.773) compared with that of a single marker (AUC=0.743 and 0.700 for CRP and albumin, respectively), where the AUC denotes the area under curve. This fuzzy logic-based approach also performed consistently better than the Glasgow Prognostic Score (GPS) (AUC=0.745). Thus, application of fuzzy logic to the analysis of serum markers can more accurately predict the outcome for patients with esophageal cancer.
Methodological development of fuzzy-logic controllers from multivariable linear control.
Tso, S K; Fung, Y H
1997-01-01
It is the function of the design of a fuzzy-logic controller to determine the universes of discourse of the antecedents and the consequents, number of membership labels, distribution and shape of membership functions, rule formulation, etc. Much of the information is usually extracted from expert knowledge, operator experience, or heuristic thinking. It is hence difficult to mechanize the first-stage design of fuzzy-logic controllers using linguistic labels whose performance is no worse than that of conventional multivariable linear controllers such as state-feedback controllers, PID controllers, etc. In this paper, an original systematic seven-step linear-to-fuzzy (LIN2FUZ) algorithm is proposed for generating the labels, universes of discourse of the antecedents and the consequents, and fuzzy rules of ;basically linear' fuzzy-logic controllers, given the reference design of available conventional multivariable linear controllers. The functionally equivalent fuzzy-logic controllers can thus provide the sound basis for the further development to achieve performance beyond the capability or the conventional controllers. The validity and effectiveness of the proposed LIN2FUZ algorithm are demonstrated by a four-input one-output inverted pendulum system.
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.
Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models.
Nadiri, Ata Allah; Gharekhani, Maryam; Khatibi, Rahman; Moghaddam, Asghar Asghari
2017-02-13
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.
Nadiri, Ata Allah; Sedghi, Zahra; Khatibi, Rahman; Gharekhani, Maryam
2017-03-22
Driven by contamination risks, mapping Vulnerability Indices (VI) of multiple aquifers (both unconfined and confined) is investigated by integrating the basic DRASTIC framework with multiple models overarched by Artificial Neural Networks (ANN). The DRASTIC framework is a proactive tool to assess VI values using the data from the hydrosphere, lithosphere and anthroposphere. However, a research case arises for the application of multiple models on the ground of poor determination coefficients between the VI values and non-point anthropogenic contaminants. The paper formulates SCFL models, which are derived from the multiple model philosophy of Supervised Committee (SC) machines and Fuzzy Logic (FL) and hence SCFL as their integration. The Fuzzy Logic-based (FL) models include: Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Larsen Fuzzy Logic (LFL) models. The basic DRASTIC framework uses prescribed rating and weighting values based on expert judgment but the four FL-based models (SFL, MFL, LFL and SCFL) derive their values as per internal strategy within these models. The paper reports that FL and multiple models improve considerably on the correlation between the modeled vulnerability indices and observed nitrate-N values and as such it provides evidence that the SCFL multiple models can be an alternative to the basic framework even for multiple aquifers. The study area with multiple aquifers is in Varzeqan plain, East Azerbaijan, northwest Iran.
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.
Design and Implementation of Takagi-Sugeno Fuzzy Logic Controller for Shunt Compensator
NASA Astrophysics Data System (ADS)
Singh, Alka; Badoni, Manoj
2016-12-01
This paper describes the application of Takagi-Sugeno (TS) type fuzzy logic controller to a three-phase shunt compensator in power distribution system. The shunt compensator is used for power quality improvement and has the ability to provide reactive power compensation, reduce the level of harmonics in supply currents, power factor correction and load balancing. Additionally, it can also be used to regulate voltage at the point of common coupling (PCC). The paper discusses the design of TS fuzzy logic controller and its implementation based on only four rules. The smaller number of rules makes it suitable for experimental verification as compared to Mamdani fuzzy controller. A small laboratory prototype of the system is developed and the control algorithm is verified experimentally. The TS fuzzy controller is compared with the proportional integral based industrial controller and their performance is compared under a wide variation of dynamic load changes.
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.
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 logic based anaesthesia monitoring systems for the detection of absolute hypovolaemia.
Mansoor Baig, Mirza; Gholamhosseini, Hamid; Harrison, Michael J
2013-07-01
Anaesthesia monitoring involves critical diagnostic tasks carried out amongst lots of distractions. Computers are capable of handling large amounts of data at high speed and therefore decision support systems and expert systems are now capable of processing many signals simultaneously in real time. We have developed two fuzzy logic based anaesthesia monitoring systems; a real time smart anaesthesia alarm system (RT-SAAM) and fuzzy logic monitoring system-2 (FLMS-2), an updated version of FLMS for the detection of absolute hypovolaemia. This paper presents the design aspects of these two systems which employ fuzzy logic techniques to detect absolute hypovolaemia, and compares their performances in terms of usability and acceptability. The interpretation of these two systems of absolute hypovolaemia was compared with clinicians' assessments using Kappa analysis, RT-SAAM K=0.62, FLMS-2 K=0.75; an improvement in performance by FLMS-2.
Liu, Kevin F R
2007-05-01
While pursuing economic development, countries around the world have become aware of the importance of environmental sustainability; therefore, the evaluation of environmental sustainability has become a significant issue. Traditionally, multiple-criteria decision-making (MCDM) was widely used as a way of evaluating environmental sustainability, Recently, several researchers have attempted to implement this evaluation with fuzzy logic since they recognized the assessment of environmental sustainability as a subjective judgment Intuition. This paper outlines a new evaluation-framework of environmental sustainability, which integrates fuzzy logic into MCDM. This evaluation-framework consists of 36 structured and 5 unstructured decision-points, wherein MCDM is used to handle the former and fuzzy logic serves for the latter, With the integrated evaluation-framework, the evaluations of environmental sustainability in 146 countries are calculated, ranked and clustered, and the evaluation results are very helpful to these countries, as they identify their obstacles towards environmental sustainability.
Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling
Aldridge, Bree B.; Saez-Rodriguez, Julio; Muhlich, Jeremy L.; Sorger, Peter K.; Lauffenburger, Douglas A.
2009-01-01
When modeling cell signaling networks, a balance must be struck between mechanistic detail and ease of interpretation. In this paper we apply a fuzzy logic framework to the analysis of a large, systematic dataset describing the dynamics of cell signaling downstream of TNF, EGF, and insulin receptors in human colon carcinoma cells. Simulations based on fuzzy logic recapitulate most features of the data and generate several predictions involving pathway crosstalk and regulation. We uncover a relationship between MK2 and ERK pathways that might account for the previously identified pro-survival influence of MK2. We also find unexpected inhibition of IKK following EGF treatment, possibly due to down-regulation of autocrine signaling. More generally, fuzzy logic models are flexible, able to incorporate qualitative and noisy data, and powerful enough to produce quantitative predictions and new biological insights about the operation of signaling networks. PMID:19343194
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
NASA Astrophysics Data System (ADS)
Tahmasebi, Pejman; Hezarkhani, Ardeshir
2012-05-01
The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
Tahmasebi, Pejman; Hezarkhani, Ardeshir
2012-01-01
The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called “Coactive Neuro-Fuzzy Inference System” (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) – as a well-known technique to solve the complex optimization problems – is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS–GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS–GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems. PMID:25540468
Assessment of Benefits and Drawbacks of Using Fuzzy Logic, Especially in Fire Control Systems
1994-03-01
classitied by :W. Pelt classitication date :September 20, 1993 jp:op:ved ci.’ :::: - - - - title :ongerubriceerd manage mentuittreksel :Ongerubriceerd...kanonsysteem het gebruik van "fuzzy reasoning" technieken voorkwam. Dit voorstel is gedaan door SIGNAAL, in een opdracht van de KM. Het TNO-FEL had een...van bet gebruik van fuzzy logic in vergelijking met ’conventionele’ methoden. Geconcludeerd wordt dat er in principe goede mogelijkhieden voor
Convergent method of and apparatus for distributed control of robotic systems using fuzzy logic
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.
Fuzzy Logic: Toward Measuring Gottfredson's Concept of Occupational Social Space.
ERIC Educational Resources Information Center
Hesketh, Beryl; And Others
1989-01-01
Investigated the application of fuzzy graphic rating scale to measurement of preferences for occupational sex type, prestige, and interests using Gottfredson's concept of occupational social space. Reported reliability and validity data with illustrative examples of respondents' interpretations of their own fuzzy ratings. Outlined counseling and…
2010-05-01
cognitive map. Three examples illustrate fuzzy cognitive maps‘ potential for understanding a non -state actor‘s decision-making calculus and...of the Cold War, the United States has wrestled with how rational deterrence applies to non -state actors in today’s complex security environment...Fuzzy logic’s themes of flexibility, adaptability, and ambiguity lay the foundation for applying fuzzy logic to non -state actor deterrence. Because
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.
Adaptive fuzzy backstepping control for a class of switched nonlinear systems with actuator faults
NASA Astrophysics Data System (ADS)
Hou, Yingxue; Tong, Shaocheng; Li, Yongming
2016-11-01
This paper investigates the problem of fault-tolerant control (FTC) for a class of switched nonlinear systems. These systems are under arbitrary switchings and are subject to both lock-in-place and loss-of-effectiveness actuator faults. In the control design, fuzzy logic systems are used to identify the unknown switched nonlinear systems. Under the framework of the backstepping control design, FTC, fuzzy adaptive control and common Lyapunov function stability theory, an adaptive fuzzy control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop switched system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error remains an adjustable neighbourhood of the origin. Two simulation examples are provided to illustrate the effectiveness of the proposed approach.
A PI-fuzzy logic controller for the regulation of blood glucose level in diabetic patients.
Ibbini, M
2006-01-01
This manuscript investigates different fuzzy logic controllers for the regulation of blood glucose level in diabetic patients. While fuzzy logic control is still intuitive and at a very early stage, it has already been implemented in many industrial plants and reported results are very promising. A fuzzy logic control (FLC) scheme was recently proposed for maintaining blood glucose level in diabetics within acceptable limits, and was shown to be more effective with better transient characteristics than conventional techniques. In fact, FLC is based on human expertise and on desired output characteristics, and hence does not require precise mathematical models. This observation makes fuzzy rule-based technique very suitable for biomedical systems where models are, in general, either very complicated or over-simplistic. Another attractive feature of fuzzy techniques is their insensitivity to system parameter variations, as numerical values of physiological parameters are often not precise and usually vary from patient to another. PI and PID controllers are very popular and are efficiently used in many industrial plants. Fuzzy PI and PID controllers behave in a similar fashion to those classical controllers with the obvious advantage that the controller parameters are time dependant on the range of the control variables and consequently, result in a better performance. In this manuscript, a fuzzy PI controller is designed using a simplified design scheme and then subjected to simulations of the two common diabetes disturbances--sudden glucose meal and system parameter variations. The performance of the proposed fuzzy PI controller is compared to that of the conventional PID and optimal techniques and is shown to be superior. Moreover, the proposed fuzzy PI controller is shown to be more effective than the previously proposed FLC, especially with respect to the overshoot and settling time.
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.
PC based speed control of dc motor using fuzzy logic controller
Mandal, S.K.; Kanphade, R.D.; Lavekar, K.P.
1998-07-01
The dc motor is extensively used as constant speed drive in textile mills, paper mills, printing press, etc.. If the load and supply voltage are time varying, the speed will be changed. Since last few decades the conventional PID controllers are used to maintain the constant speed by controlling the duty ratio of Chopper. Generally, four quadrant chopper is used for regenerative braking and reverse motoring operation. Fuzzy Logic is newly introduced in control system. Fuzzy Control is based on Fuzzy Logic, a logical system which is too much closer in spirit to human thinking and natural language. The Fuzzy Logic Controller (FLC) provides a linguistic control strategy based on knowledge base of the system. Firstly, the machine is started very smoothly from zero to reference speed in the proposed scheme by increasing the duty ratio. Then change and rate of change of speed (dN, dN/dt), change and rate of change input voltage (dV, dV/dt) and load current are input to FLC. The new value of duty ratio is determined from the Fuzzy rule base and defuzzification method. The chopper will be 'ON' according to new duty ratio to maintain the constant speed. The dynamic and steady state performance of the proposed system is better than conventional control system. In this paper mathematical simulation and experimental implementation are carried out to investigate the drive performance.
NASA Astrophysics Data System (ADS)
Kitowski, J.; Ksiażek, E.
1985-10-01
The paper presents some preliminary results of diagnostic analysis of the stimulated primary circuit of the THTR-300 nuclear power plant, based on the fuzzy sets theory. Two methods are used: the solution of the fuzzy relational equations and its extension to fuzzy logic. The first stage of the diagnostic system is the identification of the plant in the sense of fuzzy relations and the second stage is the failure or disturbance recognition. According to the fuzzy logic, lower and upper bounds of the failure vectors are obtained. Such an expert system can supply the operator in a correct recognition of the current state of the complex plant.
Fuzzy logic and a risk-based graded approach for developing S/RIDs: An introduction
Wayland, J.R.
1996-01-01
A Standards/Requirements Identification Document (S/RID) is the set of expressed performance expectations, or standards, for a facility. Critical to the development of an integrated standards-based management is the identification of a set of necessary and sufficient standards from a selected set of standards/requirements. There is a need for a formal, rigorous selection process for the S/RIDs. This is the first of three reports that develop a fuzzy logic selection process. In this report the fundamentals of fuzzy logic are discussed as they apply to a risk-based graded approach.
Fuzzy-logic-based safety verification framework for nuclear power plants.
Rastogi, Achint; Gabbar, Hossam A
2013-06-01
This article presents a practical implementation of a safety verification framework for nuclear power plants (NPPs) based on fuzzy logic where hazard scenarios are identified in view of safety and control limits in different plant process values. Risk is estimated quantitatively and compared with safety limits in real time so that safety verification can be achieved. Fuzzy logic is used to define safety rules that map hazard condition with required safety protection in view of risk estimate. Case studies are analyzed from NPP to realize the proposed real-time safety verification framework. An automated system is developed to demonstrate the safety limit for different hazard scenarios.
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.
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.
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection.
Thounaojam, Dalton Meitei; Khelchandra, Thongam; Manglem Singh, Kh; 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.
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.
Adaptive Filter Design Using Type-2 Fuzzy Cerebellar Model Articulation Controller.
Lin, Chih-Min; Yang, Ming-Shu; Chao, Fei; Hu, Xiao-Min; Zhang, Jun
2016-10-01
This paper aims to propose an efficient network and applies it as an adaptive filter for the signal processing problems. An adaptive filter is proposed using a novel interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC). The T2FCMAC realizes an interval type-2 fuzzy logic system based on the structure of the CMAC. Due to the better ability of handling uncertainties, type-2 fuzzy sets can solve some complicated problems with outstanding effectiveness than type-1 fuzzy sets. In addition, the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so that the convergence of the filtering error can be guaranteed. In order to demonstrate the performance of the proposed adaptive T2FCMAC filter, it is tested in signal processing applications, including a nonlinear channel equalization system, a time-varying channel equalization system, and an adaptive noise cancellation system. The advantages of the proposed filter over the other adaptive filters are verified through simulations.
Using fuzzy logic analysis for siting decisions of infiltration trenches for highway runoff control.
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
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.
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.
A Measurement-Theoretic Analysis of the Fuzzy Logic Model of Perception.
ERIC Educational Resources Information Center
Crowther, Court S.; And Others
1995-01-01
The fuzzy logic model of perception (FLMP) is analyzed from a measurement-theoretic perspective. The choice rule of FLMP is shown to be equivalent to a version of the Rasch model. In fact, FLMP can be reparameterized as a simple two-category logit model. (SLD)
A "fuzzy"-logic language for encoding multiple physical traits in biomolecules.
Warszawski, Shira; Netzer, Ravit; Tawfik, Dan S; Fleishman, Sarel J
2014-12-12
To carry out their activities, biological macromolecules balance different physical traits, such as stability, interaction affinity, and selectivity. How such often opposing traits are encoded in a macromolecular system is critical to our understanding of evolutionary processes and ability to design new molecules with desired functions. We present a framework for constraining design simulations to balance different physical characteristics. Each trait is represented by the equilibrium fractional occupancy of the desired state relative to its alternatives, ranging from none to full occupancy, and the different traits are combined using Boolean operators to effect a "fuzzy"-logic language for encoding any combination of traits. In another paper, we presented a new combinatorial backbone design algorithm AbDesign where the fuzzy-logic framework was used to optimize protein backbones and sequences for both stability and binding affinity in antibody-design simulation. We now extend this framework and find that fuzzy-logic design simulations reproduce sequence and structure design principles seen in nature to underlie exquisite specificity on the one hand and multispecificity on the other hand. The fuzzy-logic language is broadly applicable and could help define the space of tolerated and beneficial mutations in natural biomolecular systems and design artificial molecules that encode complex characteristics.
FUZZY-LOGIC-BASED CONTROLLERS FOR EFFICIENCY OPTIMIZATION OF INVERTER-FED INDUCTION MOTOR DRIVES
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 INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM
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
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...
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.
A fuzzy logic approach to assess groundwater pollution levels below agricultural fields.
Muhammetoglu, Ayse; Yardimci, Ahmet
2006-07-01
A fuzzy logic approach has been developed to assess the groundwater pollution levels below agricultural fields. The data collected for Kumluca Plain of Turkey have been utilized to develop the approach. The plain is known with its intensive agricultural activities, which imply excessive application of fertilizers. The characteristics of the soils and underlying groundwater for this plain were monitored during the years 1999 and 2000. Additionally, an extensive field survey related to the types and yields of crops, fertilizer application and irrigation water was carried out. Both the soil and groundwater have exhibited high levels of nitrogen, phosphorus and salinity with considerable spatial and temporal variations. The pollution level of groundwater at several established stations within the plain were assessed using Fuzzy Logic. Water Pollution Index (WPI) values are calculated by Fuzzy Logic utilizing the most significant groundwater pollutants in the area namely nitrite, nitrate and orthophosphate together with the groundwater vulnerability to pollution. The results of the calculated WPI and the monitoring study have yielded good agreement. WPI indicated high to moderate water pollution levels at Kumluca plain depending on factors such as agricultural age, depth to groundwater, soil characteristics and vulnerability of groundwater to pollution. Fuzzy Logic approach has shown to be a practical, simple and useful tool to assess groundwater pollution levels.
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.
Improvements to the adaptive maneuvering logic program
NASA Technical Reports Server (NTRS)
Burgin, George H.
1986-01-01
The Adaptive Maneuvering Logic (AML) computer program simulates close-in, one-on-one air-to-air combat between two fighter aircraft. Three important improvements are described. First, the previously available versions of AML were examined for their suitability as a baseline program. The selected program was then revised to eliminate some programming bugs which were uncovered over the years. A listing of this baseline program is included. Second, the equations governing the motion of the aircraft were completely revised. This resulted in a model with substantially higher fidelity than the original equations of motion provided. It also completely eliminated the over-the-top problem, which occurred in the older versions when the AML-driven aircraft attempted a vertical or near vertical loop. Third, the requirements for a versatile generic, yet realistic, aircraft model were studied and implemented in the program. The report contains detailed tables which make the generic aircraft to be either a modern, high performance aircraft, an older high performance aircraft, or a previous generation jet fighter.
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.
A fuzzy-logic antiswing controller for three-dimensional overhead cranes.
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.
Fuzzy Logic-Supported Detection of Complex Geospatial Features in a Web Service Environment
NASA Astrophysics Data System (ADS)
He, L. L.; Di, L. P.; Yue, P.; Zhang, M. D.
2013-10-01
Spatial relations among simple features can be used to characterize complex geospatial features. These spatial relations are often represented using linguistic terms such as near, which have inherent vagueness and imprecision. Fuzzy logic can be used to modeling fuzziness of the terms. Once simple features are extracted from remote sensing imagery, degree of satisfaction of spatial relations among these simple features can be derived to detect complex features. The derivation process can be performed in a distributed service environment, which benefits Earth science society in the last decade. Workflow-based service can provide ondemand uncertainty-aware discovery of complex features in a distributed environment. A use case on the complex facility detection illustrates the applicability of the fuzzy logic-supported service-oriented approach.
VANET Broadcast Protocol Based on Fuzzy Logic and Lightweight Retransmission Mechanism
NASA Astrophysics Data System (ADS)
Wu, Celimuge; Ohzahata, Satoshi; Kato, Toshihiko
Vehicular ad hoc networks have been attracting the interest of both academic and industrial communities on account of their potential role in Intelligent Transportation Systems (ITS). However, due to vehicle movement and fading in wireless communications, providing a reliable and efficient multi-hop broadcast service in vehicular ad hoc networks is still an open research topic. In this paper, we propose FUZZBR (FUZZy BRoadcast), a fuzzy logic based multi-hop broadcast protocol for information dissemination in vehicular ad hoc networks. FUZZBR has low message overhead since it uses only a subset of neighbor nodes to relay data messages. In the relay node selection, FUZZBR jointly considers multiple metrics of inter-vehicle distance, node mobility and signal strength by employing the fuzzy logic. FUZZBR also uses a lightweight retransmission mechanism to retransmit a packet when a relay fails. We use computer simulations to evaluate the performance of FUZZBR.
Apalit, Nathan
2010-01-01
The world of musculoskeletal disorders (MSDs) is complicated and fuzzy. Fuzzy logic provides a precise framework for complex problems characterized by uncertainty, vagueness and imprecision. Although fuzzy logic would appear to be an ideal modeling language to help address the complexity of MSDs, little research has been done in this regard. The Work Ratio is a novel mathematical model that uses fuzzy logic to provide a numerical and linguistic valuation of the likelihood of return to work and remaining at work. It can be used for a worker with any MSD at any point in time. Basic mathematical concepts from set theory and fuzzy logic are reviewed. A case study is then used to illustrate the use of the Work Ratio. Its potential strengths and limitations are discussed. Further research of its use with a variety of MSDs, settings and multidisciplinary teams is needed to confirm its universal value.
The Fuzzy Logic of MicroRNA Regulation: A Key to Control Cell Complexity
Ripoli, Andrea; Rainaldi, Giuseppe; Rizzo, Milena; Mercatanti, Alberto; Pitto, Letizia
2010-01-01
Genomic and clinical evidence suggest a major role of microRNAs (miRNAs) in the regulatory mechanisms of gene expression, with a clear impact on development and physiology; miRNAs are a class of endogenous 22-25 nt single-stranded RNA molecules, that negatively regulate gene expression post-transcriptionally, by imperfect base pairing with the 3’ UTR of the corresponding mRNA target. Because of this imperfection, each miRNA can bind multiple targets, and multiple miRNAs can bind the same mRNA target; although digital, the miRNAs control mechanism is characterized by an imprecise action, naturally understandable in the theoretical framework of fuzzy logic. A major practical application of fuzzy logic is represented by the design and the realization of efficient and robust control systems, even when the processes to be controlled show chaotic, deterministic as well unpredictable, behaviours. The vagueness of miRNA action, when considered together with the controlled and chaotic gene expression, is a hint of a cellular fuzzy control system. As a demonstration of the possibility and the effectiveness of miRNA based fuzzy mechanism, a fuzzy cognitive map -a mathematical formalism combining neural network and fuzzy logic- has been developed to study the apoptosis/proliferation control performed by the miRNA-17-92 cluster/E2F1/cMYC circuitry. When experimentally demonstrated, the concept of fuzzy control could modify the way we analyse and model gene expression, with a possible impact on the way we imagine and design therapeutic intervention based on miRNA silencing. PMID:21286312
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.
Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic.
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.
Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic
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. PMID:28058046
Construction of a fuzzy and Boolean logic gates based on DNA.
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.
Implementation of Adaptive Digital Controllers on Programmable Logic Devices
NASA Technical Reports Server (NTRS)
Gwaltney, David A.; King, Kenneth D.; Smith, Keary J.; Montenegro, Justino (Technical Monitor)
2002-01-01
Much has been made of the capabilities of Field Programmable Gate Arrays (FPGA's) in the hardware implementation of fast digital signal processing functions. Such capability also makes an FPGA a suitable platform for the digital implementation of closed loop controllers. Other researchers have implemented a variety of closed-loop digital controllers on FPGA's. Some of these controllers include the widely used Proportional-Integral-Derivative (PID) controller, state space controllers, neural network and fuzzy logic based controllers. There are myriad advantages to utilizing an FPGA for discrete-time control functions which include the capability for reconfiguration when SRAM- based FPGA's are employed, fast parallel implementation of multiple control loops and implementations that can meet space level radiation tolerance requirements in a compact form-factor. Generally, a software implementation on a Digital Signal Processor (DSP) device or microcontroller is used to implement digital controllers. At Marshall Space Flight Center, the Control Electronics Group has been studying adaptive discrete-time control of motor driven actuator systems using DSP devices. While small form factor, commercial DSP devices are now available with event capture, data conversion, Pulse Width Modulated (PWM) outputs and communication peripherals, these devices are not currently available in designs and packages which meet space level radiation requirements. In general, very few DSP devices are produced that are designed to meet any level of radiation tolerance or hardness. An alternative is required for compact implementation of such functionality to withstand the harsh environment encountered on spacemap. The goal of this effort is to create a fully digital, flight ready controller design that utilizes an FPGA for implementation of signal conditioning for control feedback signals, generation of commands to the controlled system, and hardware insertion of adaptive-control algorithm
Implementation of Adaptive Digital Controllers on Programmable Logic Devices
NASA Technical Reports Server (NTRS)
Gwaltney, David A.; King, Kenneth D.; Smith, Keary J.; Monenegro, Justino (Technical Monitor)
2002-01-01
Much has been made of the capabilities of FPGA's (Field Programmable Gate Arrays) in the hardware implementation of fast digital signal processing. Such capability also makes an FPGA a suitable platform for the digital implementation of closed loop controllers. Other researchers have implemented a variety of closed-loop digital controllers on FPGA's. Some of these controllers include the widely used proportional-integral-derivative (PID) controller, state space controllers, neural network and fuzzy logic based controllers. There are myriad advantages to utilizing an FPGA for discrete-time control functions which include the capability for reconfiguration when SRAM-based FPGA's are employed, fast parallel implementation of multiple control loops and implementations that can meet space level radiation tolerance requirements in a compact form-factor. Generally, a software implementation on a DSP (Digital Signal Processor) or microcontroller is used to implement digital controllers. At Marshall Space Flight Center, the Control Electronics Group has been studying adaptive discrete-time control of motor driven actuator systems using digital signal processor (DSP) devices. While small form factor, commercial DSP devices are now available with event capture, data conversion, pulse width modulated (PWM) outputs and communication peripherals, these devices are not currently available in designs and packages which meet space level radiation requirements. In general, very few DSP devices are produced that are designed to meet any level of radiation tolerance or hardness. The goal of this effort is to create a fully digital, flight ready controller design that utilizes an FPGA for implementation of signal conditioning for control feedback signals, generation of commands to the controlled system, and hardware insertion of adaptive control algorithm approaches. An alternative is required for compact implementation of such functionality to withstand the harsh environment
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.
NASA Technical Reports Server (NTRS)
Cheatham, John B., Jr.; Magee, Kevin N.
1991-01-01
The Rice University Department of Mechanical Engineering and Materials Sciences' Robotics Group designed and built an eight degree of freedom redundant manipulator. Fuzzy logic was proposed as a control scheme for tasks not directly controlled by a human operator. In preliminary work, fuzzy logic control was implemented for a camera tracking system and a six degree of freedom manipulator. Both preliminary systems use real time vision data as input to fuzzy controllers. Related projects include integration of tactile sensing and fuzzy control of a redundant snake-like arm that is under construction.
Application of fuzzy logic to target tracking in a cluttered environment
NASA Astrophysics Data System (ADS)
Gad, Ahmed S.; Farooq, Mohamad; Midwood, S.
2001-08-01
Most of the real world engineering problems are imprecise and they carry a certain degree of fuzziness in the description of their nature. Fuzzy logic is a design methodology that can be used to solve real life problems. It has the advantage of lower development costs, superior features, and better end product performance. Fuzzy logic makes it possible to describe complex systems using expert experience and knowledge in English-like rules, which are easy to learn and use, even by non-experts. Fuzzy technique does not require system modeling or complex mathematical equations. The design methodology is to first understand and characterize the system behavior by using our basic knowledge and experience and then design the algorithm using the fuzzy rules that describe the relationship between its input and output. This is done by debugging the design through simulations and if the performance is not satisfactory we only need to modify or add some fuzzy rules. There exists considerable literature on target tracking based on the Kalman filtering and probabilistic data association (PDA) techniques. A few of these techniques can yield acceptable results in a high-density clutter environment due to the complexity of combined target and measurement to track association or due to the simplification assumed in these techniques. This paper presents the use of fuzzy association rules involved in data association of target measurements under a high-density clutter. The fuzzy tracker is used to track a target and its performance is compared with a standard PDA filter for various signal-to-noise ratios (SNR).
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…
Fuzzy adaptive interacting multiple model nonlinear filter for integrated navigation sensor fusion.
Tseng, Chien-Hao; Chang, Chih-Wen; Jwo, Dah-Jing
2011-01-01
In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF.
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.
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.
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.
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.
Machining analysis of natural fibre reinforced composites using fuzzy logic
NASA Astrophysics Data System (ADS)
Balasubramanian, K.; Sultan, M. T. H.; Cardona, F.; Rajeswari, N.
2016-10-01
In this work, a new composite plate with natural jute fibre as the reinforcement fibres and isophthalic polyester as the resin was manufactured and subjected to a series of end milling operation by changing three input factors namely speed, feed rate and depth of cut. During each operation, the output responses namely thrust force and torque were measured. The responses were analyzed using Taguchi method to examine the relation between the input factors and output responses, and also to know the most influencing factors on the responses. The data was also analyzed using fuzzy rule model for prediction of responses for a range of input factors. The results showed that all three factors chosen have significant effect on the responses. The fuzzy model data in comparison with the experimental values shows only a marginal error and hence the prediction was highly satisfactory.
An adaptive neural fuzzy filter and its applications.
Lin, C T; Juang, C F
1997-01-01
A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper. The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules. The adaptation here includes the construction of fuzzy if-then rules (structure learning), and the tuning of the free parameters of membership functions (parameter learning). In the structure learning phase, fuzzy rules are found based on the matching of input-output clusters. In the parameter learning phase, a backpropagation-like adaptation algorithm is developed to minimize the output error. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially, and both the structure learning and parameter learning are performed concurrently as the adaptation proceeds. However, if some linguistic information about the design of the filter is available, such knowledge can be put into the ANFF to form an initial structure with hidden nodes. Two major advantages of the ANFF can thus be seen: 1) a priori knowledge can be incorporated into the ANFF which makes the fusion of numerical data and linguistic information in the filter possible; and 2) no predetermination, like the number of hidden nodes, must be given, since the ANFF can find its optimal structure and parameters automatically.
Using Fuzzy Logic for Performance Evaluation in Reinforcement Learning
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.; Khedkar, Pratap S.
1992-01-01
Current reinforcement learning algorithms require long training periods which generally limit their applicability to small size problems. A new architecture is described which uses fuzzy rules to initialize its two neural networks: a neural network for performance evaluation and another for action selection. This architecture is applied to control of dynamic systems and it is demonstrated that it is possible to start with an approximate prior knowledge and learn to refine it through experiments using reinforcement learning.
Feasibility of using adaptive logic networks to predict compressor unit failure
Armstrong, W.W.; Chungying Chu; Thomas, M.M.
1995-12-31
In this feasibility study, an adaptive logic network (ALN) was trained to predict failures of turbine-driven compressor units using a large database of measurements. No expert knowledge about compressor systems was involved. The predictions used only the statistical properties of the measurements and the indications of failure types. A fuzzy set was used to model measurements typical of normal operation. It was constrained by a requirement imposed during ALN training, that it should have a shape similar to a Gaussian density, more precisely, that its logarithm should be convex-up. Initial results obtained using this approach to knowledge discovery in the database were encouraging.
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
Amador-Angulo, Leticia; Mendoza, Olivia; Castro, Juan R; Rodríguez-Díaz, Antonio; Melin, Patricia; Castillo, Oscar
2016-09-09
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.
A fuzzy logic system for seizure onset detection in intracranial EEG.
Rabbi, Ahmed Fazle; Fazel-Rezai, Reza
2012-01-01
We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved.
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.
Olivero, Jesús; Márquez, Ana L; Real, Raimundo
2013-01-01
This study uses the amphibian species of the Mediterranean basin to develop a consistent procedure based on fuzzy sets with which biogeographic regions and biotic transition zones can be objectively detected and reliably mapped. Biogeographical regionalizations are abstractions of the geographical organization of life on Earth that provide frameworks for cataloguing species and ecosystems, for answering basic questions in biogeography, evolutionary biology, and systematics, and for assessing priorities for conservation. On the other hand, limits between regions may form sharply defined boundaries along some parts of their borders, whereas elsewhere they may consist of broad transition zones. The fuzzy set approach provides a heuristic way to analyse the complexity of the biota within an area; significantly different regions are detected whose mutual limits are sometimes fuzzy, sometimes clearly crisp. Most of the regionalizations described in the literature for the Mediterranean biogeographical area present a certain degree of convergence when they are compared within the context of fuzzy interpretation, as many of the differences found between regionalizations are located in transition zones, according to our case study. Compared with other classification procedures based on fuzzy sets, the novelty of our method is that both fuzzy logic and statistics are used together in a synergy in order to avoid arbitrary decisions in the definition of biogeographic regions and transition zones.
Fuzzy-rule-based Adaptive Resource Control for Information Sharing in P2P Networks
NASA Astrophysics Data System (ADS)
Wu, Zhengping; Wu, Hao
With more and more peer-to-peer (P2P) technologies available for online collaboration and information sharing, people can launch more and more collaborative work in online social networks with friends, colleagues, and even strangers. Without face-to-face interactions, the question of who can be trusted and then share information with becomes a big concern of a user in these online social networks. This paper introduces an adaptive control service using fuzzy logic in preference definition for P2P information sharing control, and designs a novel decision-making mechanism using formal fuzzy rules and reasoning mechanisms adjusting P2P information sharing status following individual users' preferences. Applications of this adaptive control service into different information sharing environments show that this service can provide a convenient and accurate P2P information sharing control for individual users in P2P networks.
Distributed Adaptive Fuzzy Control for Nonlinear Multiagent Systems Via Sliding Mode Observers.
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 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.
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.
A new approach to diabetic control: fuzzy logic and insulin pump technology.
Grant, Paul
2007-09-01
Diabetes is a major health problem. Since the utilisation of insulin in the 1920s there have been myriad problems in developing suitable technologies to formulate and administer correct dosages to temper this metabolic disease. From multiple daily injections, nasal inhalations and enzymatic supplementation these artificial shortcuts still do not have the ability to fully replicate a 'normoglycaemic' state of being. In this paper, we sought to explore the use of insulin pumps and the application of fuzzy logic technology to act as an 'artificial pancreas' in diabetic patients. This paper builds on our previous work [Grant P, Naesh, O. Fuzzy logic and decision-making in anaesthetics. J Roy Soc Med 2005;98(1):7-9 [review
NASA Astrophysics Data System (ADS)
Hasanien, Hany M.; Al-Ammar, Essam A.
2012-11-01
Doubly fed induction generator (DFIG) based wind farm is today the most widely used concept. This paper presents dynamic response enhancement of DFIG based wind farm under remote fault conditions using the fuzzy logic controller. The goal of the work is to improve the dynamic response of DFIG based wind farm during and after the clearance of fault using the proposed controller. The stability of wind farm during and after the clearance of fault is investigated. The effectiveness of the fuzzy logic controller is then compared with that of a PI controller. The validity of the controllers in restoring the wind farms normal operation after the clearance of fault is illustrated by the simulation results which are carried out using MATLAB/SIMULINK. Simulation results are analyzed under different fault conditions.
A Novel Strain-Based Method to Estimate Tire Conditions Using Fuzzy Logic for Intelligent Tires.
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.
Yuan, Kebin; Parri, Andrea; Yan, Tingfang; Wang, Long; Munih, Marko; Vitiello, Nicola; Wang, Qining
2015-01-01
In this paper, we present a fuzzy-logic-based hybrid locomotion mode classification method for an active pelvis orthosis. Locomotion information measured by the onboard hip joint angle sensors and the pressure insoles is used to classify five locomotion modes, including two static modes (sitting, standing still), and three dynamic modes (level-ground walking, ascending stairs, and descending stairs). The proposed method classifies these two kinds of modes first by monitoring the variation of the relative hip joint angle between the two legs within a specific period. Static states are then classified by the time-based absolute hip joint angle. As for dynamic modes, a fuzzy-logic based method is proposed for the classification. Preliminary experimental results with three able-bodied subjects achieve an off-line classification accuracy higher than 99.49%.
Fuzzy logic merger of spectral and ecological information for improved montane forest mapping.
White, Joseph D.; Running, Steven W.; Ryan, Kevin C.; Key, Carl H.
2002-01-01
Environmental data are often utilized to guide interpretation of spectral information based on context, however, these are also important in deriving vegetation maps themselves, especially where ecological information can be mapped spatially. A vegetation classification procedure is presented which combines a classification of spectral data from Landsat‐5 Thematic Mapper (TM) and environmental data based on topography and fire history. These data were combined utilizing fuzzy logic where assignment of each pixel to a single vegetation category was derived comparing the partial membership of each vegetation category within spectral and environmental classes. Partial membership was assigned from canopy cover for forest types measured from field sampling. Initial classification of spectral and ecological data produced map accuracies of less than 50% due to overlap between spectrally similar vegetation and limited spatial precision for predicting local vegetation types solely from the ecological information. Combination of environmental data through fuzzy logic increased overall mapping accuracy (70%) in coniferous forest communities of northwestern Montana, USA.
Fuzzy logic electric vehicle regenerative antiskid braking and traction control system
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 logic electric vehicle regenerative antiskid braking and traction control system
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.
Adaptive Fuzzy Systems in Computational Intelligence
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1996-01-01
In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.
The stock-flow model of spatial data infrastructure development refined by fuzzy logic.
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.
Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier.
Ibrahim, Sulaimon; Chowriappa, Pradeep; Dua, Sumeet; Acharya, U Rajendra; Noronha, Kevin; Bhandary, Sulatha; Mugasa, Hatwib
2015-12-01
Prolonged diabetes retinopathy leads to diabetes maculopathy, which causes gradual and irreversible loss of vision. It is important for physicians to have a decision system that detects the early symptoms of the disease. This can be achieved by building a classification model using machine learning algorithms. Fuzzy logic classifiers group data elements with a degree of membership in multiple classes by defining membership functions for each attribute. Various methods have been proposed to determine the partitioning of membership functions in a fuzzy logic inference system. A clustering method partitions the membership functions by grouping data that have high similarity into clusters, while an equalized universe method partitions data into predefined equal clusters. The distribution of each attribute determines its partitioning as fine or coarse. A simple grid partitioning partitions each attribute equally and is therefore not effective in handling varying distribution amongst the attributes. A data-adaptive method uses a data frequency-driven approach to partition each attribute based on the distribution of data in that attribute. A data-adaptive neuro-fuzzy inference system creates corresponding rules for both finely distributed and coarsely distributed attributes. This method produced more useful rules and a more effective classification system. We obtained an overall accuracy of 98.55%.
Modeling coastal environmental changes by fuzzy logic approach
NASA Astrophysics Data System (ADS)
Zoran, Maria A.; Zoran, Liviu Florin V.
2004-10-01
The coastal zone contains that unique environmental triple point where the water, land and atmospheric components of the terrestrial surface converge and interact. This paper is an application of remotely sensed images in marine coastal land cover classification for change detection assessment. The nature of the gradients in coastal region land cover composition among the map classes can therefore be identified.A supervised approach uses the prior knowledge about the area and thus it is very useful in getting better results than an unsupervised classification. The study test area was North-Western Black Sea coastal region, characterized by no so fast drastic changes,as it is a slow and continuous process. Satellite images (Landsat MSS, TM, ETM, SAR ERS, ASTER, MODIS) over a period of time between 1975 and 2003 were chosen for change detection analysis.In the fuzzy approach, it is possible to describe change as a degree, this being the main reason for fuzzy approach using for classification and change detection of major land cover classes in a marine coastal area.The results can be utilized as a temporal land-use change model for a region to quantify the extent and nature of change, and aid in future prediction studies, which helps in planning environmental agencies to develop sustainable land-use practices .
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.
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.
Fuzzy logic based risk assessment of effluents from waste-water treatment plants.
Cabanillas, Julián; Ginebreda, Antoni; Guillén, Daniel; Martínez, Elena; Barceló, Damià; Moragas, Lucas; Robusté, Jordi; Darbra, Rosa Ma
2012-11-15
This paper presents a new methodology to assess the risk of water effluents from waste-water treatment plants (WWTPs) based on fuzzy logic, a well-known theory to deal with uncertainty, especially in the environmental field where data are often lacking. The method has been tested using the effluent's pollution data coming from 22 waste-water treatment plants (WWTPs) located in Catalonia (NE Spain). Thirty-eight pollutants were analyzed along three campaigns performed yearly from 2008 to 2010. Whereas 9 compounds have been detected in more than 70% of the samples analyzed, 7 compounds have been found at levels equal or higher than the river Environmental Quality Standards set by the Water Framework Directive. Upon combination of both criteria (presence and concentration), compounds of greatest environmental concern in the WWTP studied are nickel, the herbicide diuron, and the endocrine disruptors nonyl and octylphenol. It is remarkable the low variability of the pollutant concentration just differing for the case of nickel and zinc. These low values of exposure together with other pollutants' characteristics provide a medium or low risk assessment for all the WWTPs. The results of this new method have been compared with COMMPS procedure, a solid method developed in the context of the Water Framework Directive, and they show that the fuzzy model is more conservative than COMMPS. This is due to different reasons: the fuzzy model takes into account the persistence of chemical compounds whereas COMMPS does not; the fuzzy model includes the weights provided by an expert group inquired in previous works and also considers the uncertainty of the environmental data, avoiding the crisp values and offering a range of overlapping between the different fuzzy sets. However, the results even if being more conservative with fuzzy logic, are in good agreement with a solid methodology such as the COMMPS procedure.
Fuzzy-logic based strategy for validation of multiplex methods: example with qualitative GMO assays.
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.
Ondrej Linda; Todd Vollmer; Jim Alves-Foss; Milos Manic
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 provides 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.
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.
Qazi, Abroon Jamal; de Silva, Clarence W; Khan, Afzal; Khan, Muhammad Tahir
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.
Fuzzy Logic-based expert system for evaluating cake quality of freeze-dried formulations.
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.
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.
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
Risk management and the precautionary principle: a fuzzy logic model.
Cameron, Enrico; Peloso, Gian Francesco
2005-08-01
The aim of this article is to illustrate a procedure for applying the precautionary principle within a strategy for reducing the possibility of underestimating the effective risk caused by a phenomenon, product, or process, and of adopting insufficient risk reduction measures or overlooking their need. We start by simply defining risk as the product between the numerical expression of the adverse consequences of an event and the likelihood of its occurrence or the likelihood that such consequences will occur. Uncertainty in likelihood estimates and several key concepts inherent to the precautionary principle, such as sufficient certainty, prevention, and desired level of protection, are represented as fuzzy sets. The strategy described may be viewed as a simplified example of a precautionary decision process that has been chiefly conceived as a theoretical contribution to the debate concerning the precautionary principle, the quantification of its application, and the formal approach to such problems.
NASA Astrophysics Data System (ADS)
Zhang, Nannan; Zhou, Kefa; Du, Xishihui
2017-04-01
Mineral prospectivity mapping (MPM) is a multi-step process that ranks promising target areas for further exploration. Fuzzy logic and fuzzy analytical hierarchy process (AHP) are knowledge-driven MPM approaches. In this study, both approaches were used for data processing, based on which MPM was performed for porphyry and hydrothermal vein copper deposits in the Dananhu-Tousuquan island arc, Xinjiang. The results of the two methods were then compared. The two methods combined expert experience and the Studentized contrast (S(C)) values of the weights-of-evidence approach to calculate the weights of 15 layers, and these layers were then integrated by the gamma operator (γ). Through prediction-area (P-A) plot analysis, the optimal γ for fuzzy logic and fuzzy AHP was determined as 0.95 and 0.93, respectively. The thresholds corresponding to different levels of metallogenic probability were defined via concentration-area (C-A) fractal analysis. The prediction performances of the two methods were compared on this basis. The results showed that in MPM based on fuzzy logic, the area under the receiver operating characteristic (ROC) curve was 0.806 and 81.48% of the known deposits were predicted, whereas in MPM based on fuzzy AHP, the area under the ROC curve was 0.862 and 92.59% of the known deposits were predicted. Therefore, prediction based on fuzzy AHP is more accurate and can provide directions for future prospecting.
Adaptive Fuzzy Control of a Direct Drive Motor
NASA Technical Reports Server (NTRS)
Medina, E.; Kim, Y. T.; Akbaradeh-T., M. -R.
1997-01-01
This paper presents a state feedback adaptive control method for position and velocity control of a direct drive motor. The proposed control scheme allows for integrating heuristic knowledge with mathematical knowledge of a system. It performs well even when mathematical model of the system is poorly understood. The controller consists of an adaptive fuzzy controller and a supervisory controller. The supervisory controller requires only knowledge of the upper bound and lower bound of the system parameters. The fuzzy controller is based on fuzzy basis functions and states of the system. The adaptation law is derived based on the Lyapunov function which ensures that the state of the system asymptotically approaches zero. The proposed controller is applied to a direct drive motor with payload and parameter uncertainty, and the effectiveness is verified by simulation results.
Modelling and Control of the Qball X4 Quadrotor System based on Pid and Fuzzy Logic Structure
NASA Astrophysics Data System (ADS)
Bodrumlu, Tolga; Turan Soylemez, Mehmet; Mutlu, Ilhan
2017-01-01
This work focuses on a quadrocopter model, which was developed by QuanserTM and named as Qball X4. First, mathematical model of the Qball X4 is obtained. Then, a conventional PID control technique is presented. This PID control parameters come from Qball user manual. After the presentation of conventional PID control, as an extension of the conventional PID control theory, a different fuzzy controller structure is given. The proposed fuzzy controller structure is based on fuzzy logic and its name is PID type fuzzy controller. All of the simulations are done in MATLABTM environment.
Sari, Hanife; Yetilmezsoy, Kaan; Ilhan, Fatih; Yazici, Senem; Kurt, Ugur; Apaydin, Omer
2013-06-01
Three multiple input and multiple output-type fuzzy-logic-based models were developed as an artificial intelligence-based approach to model a novel integrated process (UF-IER-EDBM-FO) consisted of ultrafiltration (UF), ion exchange resins (IER), electrodialysis with bipolar membrane (EDBM), and Fenton's oxidation (FO) units treating young, middle-aged, and stabilized landfill leachates. The FO unit was considered as the key process for implementation of the proposed modeling scheme. Four input components such as H(2)O(2)/chemical oxygen demand ratio, H(2)O(2)/Fe(2+) ratio, reaction pH, and reaction time were fuzzified in a Mamdani-type fuzzy inference system to predict the removal efficiencies of chemical oxygen demand, total organic carbon, color, and ammonia nitrogen. A total of 200 rules in the IF-THEN format were established within the framework of a graphical user interface for each fuzzy-logic model. The product (prod) and the center of gravity (centroid) methods were performed as the inference operator and defuzzification methods, respectively, for the proposed prognostic models. Fuzzy-logic predicted results were compared to the outputs of multiple regression models by means of various descriptive statistical indicators, and the proposed methodology was tested against the experimental data. The testing results clearly revealed that the proposed prognostic models showed a superior predictive performance with very high determination coefficients (R (2)) between 0.930 and 0.991. This study indicated a simple means of modeling and potential of a knowledge-based approach for capturing complicated inter-relationships in a highly non-linear problem. Clearly, it was shown that the proposed prognostic models provided a well-suited and cost-effective method to predict removal efficiencies of wastewater parameters prior to discharge to receiving streams.
Plasma position control in the STOR-M tokamak: A fuzzy logic approach
NASA Astrophysics Data System (ADS)
Morelli, Jordan Edwin
Adequate control of the position of the plasma column within the STOR-M tokamak is a chief requirement in order for experimental quality discharges to be obtained. Optimal control over tokamak discharge parameters, including the plasma position, is very difficult to achieve. This is due in large part to the difficulty in modelling the tokamak discharge parameters, as they are highly nonlinear and time varying in nature. The difficulty of modelling the tokamak discharge parameters suggests that a control system, such as a fuzzy logic based controller, which does not require a system model may be well suited to the control of fusion plasma. In order to improve the quality of control over the plasma position within the STOR-M tokamak, the existing analog PID controller was modified. These modifications facilitate the application of a digital controller by a personal computer via the Advantech PCL-711B data acquisition card. The performance of the modified plasma position controller and an Arbitrary Signal Generator developed by the author was evaluated. This modified plasma position controller was applied successfully to the STOR-M tokamak during both normal mode and A.C. mode operation. In both cases, the modified controller provided adequate control over the position of the plasma column within the discharge chamber. Furthermore, the modified controller was more convenient to optimize than the original, existing analog PID controller. By taking advantage of the modifications that were made to the plasma position controller, a fuzzy logic controller was developed by the author. The fuzzy logic based plasma position controller was also successfully applied to the STOR-M tokamak during both normal mode and A.C. operation. The fuzzy controller was demonstrated to reliably provide a higher degree of control over the position of the plasma column within the STOR-M tokamak than the modified PID controller.
Olliver, S; Davis, G M; Hatzakis, G E
2003-01-01
We have previously developed a fuzzy logic controller for weaning adults with chronic obstructive pulmonary disease using pressure support ventilation (PSV). We used the core of our fuzzy logic-based weaning platform and further developed parametrizable components for weaning newborns of differing body size and disease-state. The controller was validated on neonates recovering from congenital heart disease (CHD) while receiving synchronous intermittent mandatory ventilation (SIMV). We wished to compare the efficacy of this controller versus the bedside weaning protocol in children with respiratory syncytial virus pneumonitis/bronchiolitis (RSV) in the pediatric intensive care unit (PICU). The fuzzy controller evaluated the "current" and "trend" weaning status of the newborn to quantitatively determine the change in the SIMV integrated ventilatory setting. For the "current" status it used heart rate (HR), respiratory rate (RR), tidal volume (VT) and oxygen saturation (SaO2), while for the "trend" status the differences of deltaRR/ deltat, deltaHR/ deltat, and deltaSaO2/ deltat recorded between two subsequent time points were utilized. The enumerated vital signs were fuzzified and then probability levels of occurrence were assigned. Individualized "golden" goals for SaO2 were set for each newborn. We retrospectively assessed the charts of 19 newborns, 113+/-128 days old, 5,546+/-2,321 gr body weight, weaning for 99+/-46 days, at 2-hour intervals. The SIMV levels proposed by the fuzzy controller were matched to those levels actually applied. In 60% of the time both values coincided. For the remaining 40%, the controller was more aggressive suggesting lower values of SIMV than the applied ones. The Area under the SIMV curves over time was 1,969+/-1,044 for the applied vs 1,886+/-978 for the suggested levels, respectively. The fuzzy controller adjusted for body size and disease-pattern can approximate the actual weaning course of newborns with RSV.
Luo, Shaohua
2014-09-01
This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.
Luo, Shaohua
2014-09-01
This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.
Fuzzy-logic modeling of land suitability for hybrid poplar across the Prairie Provinces of Canada.
Joss, B N; Hall, R J; Sidders, D M; Keddy, T J
2008-06-01
Determining the feasibility of a large-scale afforestation program is one approach being investigated by the Government of Canada to increase Canada's potential to sequester carbon from the atmosphere. Large-scale afforestation, however, requires knowledge of where it is suitable to establish and grow trees. Spatial models based on Boolean logic and/or statistical models within a geographic information system may be used for this purpose, but empirical environmental data are often lacking, and the association of these data to land suitability is most often a subjective process. As a solution to this problem, this paper presents a fuzzy-logic modeling approach to assess land suitability for afforestation of hybrid poplar (Populus spp.) over the Prairie Provinces of Canada. Expert knowledge regarding the selection and magnitudes of environmental variables were integrated into fuzzy rule sets from which estimates of land suitability were generated and presented in map form. The environmental variables selected included growing season precipitation, climate moisture index, growing degree days, and Canada Land Inventory capability for agriculture and elevation. Approximately 150,000 km2, or 28% of the eligible land base within the Prairie Provinces was found to be suitable for afforestation. Accuracy assessments conducted with fuzzy accuracy methods provided a more descriptive assessment of the resulting land suitability map than figures generated from a more conventional Boolean-based accuracy measure. Modeling, mapping and accuracy assessment issues were identified for future extension of this work to map hybrid poplar land suitability over Canada.
Fuzzy logic model to describe anesthetic effect and muscular influence on EEG Cerebral State Index.
Brás, S; Gouveia, S; Ribeiro, L; Ferreira, D A; Antunes, L; Nunes, C S
2013-06-01
The well-known Cerebral State Index (CSI) quantifies depth of anesthesia and is traditionally modeled with Hill equation and propofol effect-site concentration (Ce). This work brings out two novelties: introduction of electromyogram (EMG) and use of fuzzy logic models with ANFIS optimized parameters. The data were collected from dogs (n=27) during routine surgery considering two propofol administration protocols: constant infusion (G1, n=14) and bolus (G2, n=13). The median modeling error of the fuzzy logic model with Ce and EMG was lower or similar than that of the Hill with Ce (p=0.012-G1, p=0.522-G2). Furthermore, there was no significant performance impact due to model structure alteration (p=0.288-G1, p=0.330-G2) and EMG introduction increased or maintained the performance (p=0.036-G1, p=0.798-G2). Therefore, the new model can achieve higher performance than Hill model, mostly due to EMG information and not due to changes in the model structure. In conclusion, the fuzzy models adequately describe CSI data with advantages over traditional Hill models.
Assessment of safety and health in the tea industry of Barak valley, Assam: a fuzzy logic approach.
Gupta, Rajat; Dey, Sanjoy Kumar
2013-01-01
Traditional safety and health system measurement procedures, practiced in various industries produce qualitative results with a degree of uncertainty. This paper presents a fuzzy-logic-based approach to developing a fuzzy model for assessing the safety and health status in the tea industry. For this, the overall safety and health status at a tea estate has been considered as a function of 4 inputs: occupational safety, occupational health, behavioral safety and competency. A set of fuzzy rules based on expert human judgment has been used to correlate different fuzzy inputs and output. Fuzzy set operations are used to calculate the safety and health status of the tea industry. Application of the developed model at a tea estate showed that the safety and health status belongs to the fuzzy class of good with a crisp value of 7.2.
NASA Astrophysics Data System (ADS)
Bosch, David; Ledo, Juanjo; Queralt, Pilar
2013-07-01
Fuzzy logic has been used for lithology prediction with remarkable success. Several techniques such as fuzzy clustering or linguistic reasoning have proven to be useful for lithofacies determination. In this paper, a fuzzy inference methodology has been implemented as a MATLAB routine and applied for the first time to well log data from the German Continental Deep Drilling Program (KTB). The training of the fuzzy inference system is based on the analysis of the multi-class Matthews correlation coefficient computed for the classification matrix. For this particular data set, we have found that the best suited membership function type is the piecewise linear interpolation of the normalized histograms; that the best combination operator for obtaining the final lithology degrees of membership is the fuzzy gamma operator; and that all the available properties are relevant in the classification process. Results show that this fuzzy logic-based method is suited for rapidly and reasonably suggesting a lithology column from well log data, neatly identifying the main units and in some cases refining the classification, which can lead to a better interpretation. We have tested the trained system with synthetic data generated from property value distributions of the training data set to find that the differences in data distributions between both wells are significant enough to misdirect the inference process. However, a cross-validation analysis has revealed that, even with differences between data distributions and missing lithologies in the training data set, this fuzzy logic inference system is able to output a coherent classification.
Rezaei, Farshad; Safavi, Hamid R; Ahmadi, Azadeh
2013-01-01
Groundwater is an important source of water, especially in arid and semi-arid regions where surface water is scarce. Groundwater pollution in these regions is consequently a major concern, especially as pollution control and removal in these resources are not only expensive but at times impossible. It is, therefore, essential to prevent their contamination in the first place by properly identifying vulnerable zones. One method most commonly used for evaluating groundwater pollution is the DRASTIC method, in which the Boolean logic is used to rank and classify the parameters involved. Problems arise, however, in the application of the Boolean logic. In this paper, the fuzzy logic has been used to avoid the problems. For this purpose, three critical cases of minimum, maximum, and mean values have been considered for the net recharge parameter. The process has been performed on the Zayandehrood river basin aquifers. The fuzzy-DRASTIC vulnerability map thus obtained indicates that the western areas of the basin generally have the maximum pollution potential followed by the areas located in the east. The central parts of the study area are found to have a low pollution potential. Finally, two sensitivity analyses are performed to show the significance of each value of the net recharge parameter in the calculation of vulnerability index.
Fuzzy logic control of steam generator water level in pressurized water reactors
Kuan, C.C.; Lin, C.; Hsu, C.C. . Dept. of Nuclear Engineering)
1992-10-01
In this paper a fuzzy logic controller is applied to control the steam generator water level in a pressurized water reactor. The method does not require a detailed mathematical mode of the object to be controlled. The design is based on a set of linguistic rules that were adopted from the human operator's experience. After off-line fuzzy computation, the controller is a lookup table, and thus, real-time control is achieved. Shrink-and-swell phenomena are considered in the linguistic rules, and the simulation results show that their effect is dramatically reduced. The performance of the control system can also be improved by changing the input and output scaling factors, which is convenient for on-line tuning.
Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor
Ondrej Linda; Todd Vollmer; Jason Wright; Milos Manic
2011-04-01
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 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 test-bed mimicking the environment of a critical infrastructure control system.
NASA Astrophysics Data System (ADS)
Pandey, Arun Kumar; Dubey, Avanish Kumar
2012-03-01
Capability of laser cutting mainly depends on optical and thermal properties of work material. Highly reflective and thermally conductive Duralumin sheets are difficult-to-laser-cut. Application of Duralumin sheets in aeronautic and automotive industries due to its high strength to weight ratio demand narrow and complex cuts with high geometrical accuracy. The present paper experimentally investigates the laser cutting of Duralumin sheet with the aim to improve geometrical accuracy by simultaneously minimizing the kerf width and kerf deviations at top and bottom sides. A hybrid approach, obtained by combining robust parameter design methodology and Fuzzy logic theory has been applied to compute the fuzzy multi-response performance index. This performance index is further used for multi-objective optimization. The predicted optimum results have been verified by performing the confirmation tests. The confirmation tests show considerable reduction in kerf deviations at top and bottom sides.
Applying fuzzy logic to estimate the parameters of the length-weight relationship.
Bitar, S D; Campos, C P; Freitas, C E C
2016-05-03
We evaluated three mathematical procedures to estimate the parameters of the relationship between weight and length for Cichla monoculus: least squares ordinary regression on log-transformed data, non-linear estimation using raw data and a mix of multivariate analysis and fuzzy logic. Our goal was to find an alternative approach that considers the uncertainties inherent to this biological model. We found that non-linear estimation generated more consistent estimates than least squares regression. Our results also indicate that it is possible to find consistent estimates of the parameters directly from the centers of mass of each cluster. However, the most important result is the intervals obtained with the fuzzy inference system.
A modular diagnosis system based on fuzzy logic for UASB reactors treating sewage.
Borges, R M; Mattedi, A; Munaro, C J; Franci Gonçalves, R
A modular diagnosis system (MDS), based on the framework of fuzzy logic, is proposed for upflow anaerobic sludge blanket (UASB) reactors treating sewage. In module 1, turbidity and rainfall information are used to estimate the influent organic content. In module 2, a dynamic fuzzy model is used to estimate the current biogas production from on-line measured variables, such as daily average temperature and the previous biogas flow rate, as well as the organic load. Finally, in module 3, all the information above and the residual value between the measured and estimated biogas production are used to provide diagnostic information about the operation status of the plant. The MDS was validated through its application to two pilot UASB reactors and the results showed that the tool can provide useful diagnoses to avoid plant failures.
Mehri, M
2013-04-01
Application of appropriate models to approximate the performance function warrants more precise prediction and helps to make the best decisions in the poultry industry. This study reevaluated the factors affecting hatchability in laying hens from 29 to 56 wk of age. Twenty-eight data lines representing 4 inputs consisting of egg weight, eggshell thickness, egg sphericity, and yolk/albumin ratio and 1 output, hatchability, were obtained from the literature and used to train an artificial neural network (ANN). The prediction ability of ANN was compared with that of fuzzy logic to evaluate the fitness of these 2 methods. The models were compared using R(2), mean absolute deviation (MAD), mean squared error (MSE), mean absolute percentage error (MAPE), and bias. The developed model was used to assess the relative importance of each variable on the hatchability by calculating the variable sensitivity ratio. The statistical evaluations showed that the ANN-based model predicted hatchability more accurately than fuzzy logic. The ANN-based model had a higher determination of coefficient (R(2) = 0.99) and lower residual distribution (MAD = 0.005; MSE = 0.00004; MAPE = 0.732; bias = 0.0012) than fuzzy logic (R(2) = 0.87; MAD = 0.014; MSE = 0.0004; MAPE = 2.095; bias = 0.0046). The sensitivity analysis revealed that the most important variable in the ANN-based model of hatchability was egg weight (variable sensitivity ratio, VSR = 283.11), followed by yolk/albumin ratio (VSR = 113.16), eggshell thickness (VSR = 16.23), and egg sphericity (VSR = 3.63). The results of this research showed that the universal approximation capability of ANN made it a powerful tool to approximate complex functions such as hatchability in the incubation process.
Fuzzy logic enhanced speed control of an indirect field-oriented induction machine drive
Heber, B.; Xu, L.; Tang, Y.
1997-09-01
Field orientation control (FOC) of induction machines has permitted fast transient response by decoupled torque and flux control. However, field orientation detuning caused by parameter variations is a major difficulty for indirect FOC methods. Traditional probability density function (PID) controllers have trouble meeting a wide range of speed tracking performance even when proper field orientation is achieved. PID controller performance is severely degraded when detuning occurs. This paper presents a fuzzy logic design approach that can meet the speed tracking requirements even when detuning occurs. Computer simulations and experimental results obtained via a general-purpose digital signal processor (DSP) system are presented.
NASA Astrophysics Data System (ADS)
Minihan, T. P.; Lei, S.; Sun, G.; Palazzolo, A.; Kascak, A. F.; Calvert, T.
2003-06-01
Conventional use of magnetic bearings relies on a zero reference to keep the rotor centered in the radial and axial axes. This paper compares different control methods developed for the alternate control task of tracking an axial dynamic target. Controllers based on fuzzy logic, sliding mode, and direct linearization were designed to meet this task. Performance criteria, such as maximum axial displacement, minimum phase lag and I2R power losses were compared for each controller. The large motion, tracking problem for a rotor has utility in applications where dynamic seal clearances are required. This has a variety of potential applications in turbo-machinery, such as active stall control.
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.
Fuzzy Logic Based Controller for a Grid-Connected Solid Oxide Fuel Cell Power Plant.
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.
Chaotic queue-based genetic algorithm for design of a self-tuning fuzzy logic controller
NASA Astrophysics Data System (ADS)
Saini, Sanju; Saini, J. S.
2012-11-01
This paper employs a chaotic queue-based method using logistic equation in a non-canonical genetic algorithm for optimizing the performance of a self-tuning Fuzzy Logic Controller, used for controlling a nonlinear double-coupled system. A comparison has been made with a standard canonical genetic algorithm implemented on the same plant. It has been shown that chaotic queue-method brings an improvement in the performance of the FLC for wide range of set point changes by a more profound initial population spread in the search space.
Fuzzy Adaptive Quantized Control for a Class of Stochastic Nonlinear Uncertain Systems.
Liu, Zhi; Wang, Fang; Zhang, Yun; Chen, C L Philip
2016-02-01
In this paper, a fuzzy adaptive approach for stochastic strict-feedback nonlinear systems with quantized input signal is developed. Compared with the existing research on quantized input problem, the existing works focus on quantized stabilization, while this paper considers the quantized tracking problem, which recovers stabilization as a special case. In addition, uncertain nonlinearity and the unknown stochastic disturbances are simultaneously considered in the quantized feedback control systems. By putting forward a new nonlinear decomposition of the quantized input, the relationship between the control signal and the quantized signal is established, as a result, the major technique difficulty arising from the piece-wise quantized input is overcome. Based on fuzzy logic systems' universal approximation capability, a novel fuzzy adaptive tracking controller is constructed via backstepping technique. The proposed controller guarantees that the tracking error converges to a neighborhood of the origin in the sense of probability and all the signals in the closed-loop system remain bounded in probability. Finally, an example illustrates the effectiveness of the proposed control approach.
NASA Astrophysics Data System (ADS)
Hongesombut, Komsan; Mitani, Yasunori; Tsuji, Kiichiro
Fuzzy logic control has been applied to various applications in power systems. Its control rules and membership functions are typically obtained by trial and error methods or experience knowledge. Proposed here is the application of a micro-genetic algorithm (micro-GA) to simultaneously design optimal membership functions and control rules for STATCOM. First, we propose a simple approach to extract membership functions and fuzzy logic control rules based on observed signals. Then a proposed GA will be applied to optimize membership functions and its control rules. To validate the effectiveness of the proposed approach, several simulation studies have been performed on a multimachine power system. Simulation results show that the proposed fuzzy logic controller with STATCOM can effectively and robustly enhance the damping of oscillations.
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.
Huq, Rajibul; Wang, Rosalie; Lu, Elaine; Hebert, Debbie; Lacheray, Hervé; Mihailidis, Alex
2013-06-01
This paper presents preliminary studies in developing a fuzzy logic based intelligent system for autonomous post-stroke upper-limb rehabilitation exercise. The intelligent system autonomously varies control parameters to generate different haptic effects on the robotic device. The robotic device is able to apply both resistive and assistive forces for guiding the patient during the exercise. The fuzzy logic based decision-making system estimates muscle fatigue of the patient using exercise performance and generates a combination of resistive and assistive forces so that the stroke survivor can exercise for longer durations with increasing control. The fuzzy logic based system is initially developed using a study with healthy subjects and preliminary results are also presented to validate the developed system with healthy subjects. The next stage of this work will collect data from stroke survivors for further development of the system.
Gentili, Pier Luigi; Giubila, Maria Sole; Heron, B Mark
2017-02-03
This work demonstrates the computational power of a hydrodynamic photochemical oscillator based on a photochromic naphthopyran, generating aperiodic time series. The chaotic character of the time series is tested by calculating its largest Lyapunov exponent and the correlation dimension of its attractor after building its phase space through the Takens' theorem. Then, the chaotic dynamic is shown to be suitable to implement all the fundamental Boolean two-inputs-one-output logic gates. Finally, the strategy to implement Fuzzy logic systems (FLSs) based on the time series is described. Such FLSs promise to be useful in the field of Computational Linguistics that is concerned with the development of artificial intelligent systems able to transform collections of numerical data in natural language texts.
Autonomous navigation system using a fuzzy adaptive nonlinear H∞ filter.
Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim
2014-09-19
Although nonlinear H∞ (NH∞) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H∞ (FANH∞) filter is proposed for the Unmanned Aerial Vehicle (UAV) localization problem. Based on a real-time Fuzzy Inference System (FIS), the FANH∞ filter continually adjusts the higher order of the Taylor development thorough adaptive bounds and adaptive disturbance attenuation , which significantly increases the UAV localization performance. The results obtained using the FANH∞ navigation filter are compared to the NH∞ navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH∞ filter.
Peche, Roberto Rodriguez, Esther
2011-03-15
This study shows the practical application of the EIA method based on fuzzy logic proposed by the authors (Peche and Rodriguez, 2009) to a simplified case of study-the activity of a petrol station throughout its exploitation. The intensity (p{sub 1}), the extent (p{sub 2}) and the persistence (p{sub 3}) were the properties selected to describe the impacts and their respective assessment functions v-bar{sub i}=f(p-bar{sub i}) were determined. The main actions (A) and potentially affected environmental factors (F) were selected. Every impact was identified by a pair A-F and the values of the three impact properties were estimated for each of them by means of triangular fuzzy numbers. Subsequently, the fuzzy estimation of every impact was carried out, the estimation of the impact A{sub 1}-F{sub 2} (V-bar{sub 1}) being explained in detail. Every impact was simultaneously represented by its corresponding generalised confidence interval and membership function. Since the membership functions of all impacts were similar to triangular fuzzy numbers, a triangular approach (TA) was used to describe every impact. A triangular approach coefficient (TAC) was introduced to quantify the similarity of each fuzzy number and its corresponding triangular approach, where TAC (V-bar) element of (0, 1] and TAC being 1 when the fuzzy number is triangular. The TACs-ranging from 0.96 to 0.99-proved that TAs were valid in all cases. Next, the total positive and negative impacts-TV-bar{sup +} and TV-bar{sup -} were calculated and later, the fuzzy value of the total environmental impact TV-bar was determined from them. Finally, the defuzzification of TV-bar led to the punctual impact estimator TV{sup (1)} = -88.50 and its corresponding uncertainty interval [{delta}{sub l}(TV-bar),{delta}{sub r}(TV-bar)]=[6.52,6.96], which represent the total value of the EI. In conclusion, the EIA method enabled the integration of heterogeneous impacts, which exerted influence on environmental factors of a
NASA Astrophysics Data System (ADS)
Andujar, J. M.; Aroba, J.; de Torre, M. L. La; Grande, J. A.
2006-01-01
This work aims at contrasting, by means of a set of fuzzy logic- and data mining-based algorithms, the functioning model of a detritic aquifer undergoing overexploitation and nitrate excess input coming from strawberry and citrus intensive crops in its recharge zone. To provide researchers unskilled in data mining techniques with an easy and intuitive interpretation, the authors have developed a computer tool based on fuzzy logic that allows immediate qualitative analysis of the data contained in a data mass from the water chemical analyses, and serves as a contrast to functioning models previously proposed with classical statistics.
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.
Hybrid intelligent control scheme for air heating system using fuzzy logic and genetic algorithm
Thyagarajan, T.; Shanmugam, J.; Ponnavaikko, M.; Panda, R.C.
2000-01-01
Fuzzy logic provides a means for converting a linguistic control strategy, based on expert knowledge, into an automatic control strategy. Its performance depends on membership function and rule sets. In the traditional Fuzzy Logic Control (FLC) approach, the optimal membership is formed by trial-and-error method. In this paper, Genetic Algorithm (GA) is applied to generate the optimal membership function of FLC. The membership function thus obtained is utilized in the design of the Hybrid Intelligent Control (HIC) scheme. The investigation is carried out for an Air Heat System (AHS), an important component of drying process. The knowledge of the optimum PID controller designed, is used to develop the traditional FLC scheme. The computational difficulties in finding optimal membership function of traditional FLC is alleviated using GA In the design of HIC scheme. The qualitative performance indices are evaluated for the three control strategies, namely, PID, FLC and HIC. The comparison reveals that the HIC scheme designed based on the hybridization of FLC with GA performs better. Moreover, GA is found to be an effective tool for designing the FLC, eliminating the human interface required to generate the membership functions.
A Novel Strain-Based Method to Estimate Tire Conditions Using Fuzzy Logic for Intelligent Tires
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
Gómez, Antonio; Cedano, Juan; Espadaler, Jordi; Hermoso, Antonio; Piñol, Jaume; Querol, Enrique
2008-02-01
The functional annotation of the new protein sequences represents a major drawback for genomic science. The best way to suggest the function of a protein from its sequence is by finding a related one for which biological information is available. Current alignment algorithms display a list of protein sequence stretches presenting significant similarity to different protein targets, ordered by their respective mathematical scores. However, statistical and biological significance do not always coincide, therefore, the rearrangement of the program output according to more biological characteristics than the mathematical scoring would help functional annotation. A new method that predicts the putative function for the protein integrating the results from the PSI-BLAST program and a fuzzy logic algorithm is described. Several protein sequence characteristics have been checked in their ability to rearrange a PSI-BLAST profile according more to their biological functions. Four of them: amino acid content, matched segment length and hydropathic and flexibility profiles positively contributed, upon being integrated by a fuzzy logic algorithm into a program, BYPASS, to the accurate prediction of the function of a protein from its sequence.
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.
Using fuzzy logic in test case prioritization for regression testing programs with assertions.
Alakeel, Ali M
2014-01-01
Program assertions have been recognized as a supporting tool during software development, testing, and maintenance. Therefore, software developers place assertions within their code in positions that are considered to be error prone or that have the potential to lead to a software crash or failure. Similar to any other software, programs with assertions must be maintained. Depending on the type of modification applied to the modified program, assertions also might have to undergo some modifications. New assertions may also be introduced in the new version of the program, while some assertions can be kept the same. This paper presents a novel approach for test case prioritization during regression testing of programs that have assertions using fuzzy logic. The main objective of this approach is to prioritize the test cases according to their estimated potential in violating a given program assertion. To develop the proposed approach, we utilize fuzzy logic techniques to estimate the effectiveness of a given test case in violating an assertion based on the history of the test cases in previous testing operations. We have conducted a case study in which the proposed approach is applied to various programs, and the results are promising compared to untreated and randomly ordered test cases.
Upadhyay, Rohit; Sehwag, Sneha; Mishra, Hari Niwas
2017-04-15
An electronic nose (e-nose), having 18 metal oxide semiconductor (MOS) sensors, guided determination of frying disposal time of sunflower oil is reported. The ranking and screening of MOS sensors, specific for volatile organic compounds, was performed using fuzzy logic. A correlation was examined between rancidity indices of fried oil (total polar compounds (TPC), and triglyceride dimers-polymers (TGDP), among others) and e-nose based odor index. Fuzzy logic screened 6 MOS sensors (LY2/G, LY2/AA, LY2/GH, LY2/gCT1, T30/1, and P30/1) to deconvolute the rancid fried oils using hierarchical clustering on principal component space. A good relationship was noted between rancidity indices and odor index (R(2)>0.85). Based on maximum discard limits of rancidity indices (25% TPC and 10% TGDP), the frying disposal time of 15.2h (TPC) vs. 15.8h (e-nose) and 15.5h (TGDP) vs. 16.3h (e-nose) was determined. The demonstrated methodology holds a potential extension to different fried oils and products.
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.
Fuzzy Logic Controller Stability Analysis Using a Satisfiability Modulo Theories Approach
NASA Technical Reports Server (NTRS)
Arnett, Timothy; Cook, Brandon; Clark, Matthew A.; Rattan, Kuldip
2017-01-01
While many widely accepted methods and techniques exist for validation and verification of traditional controllers, at this time no solutions have been accepted for Fuzzy Logic Controllers (FLCs). Due to the highly nonlinear nature of such systems, and the fact that developing a valid FLC does not require a mathematical model of the system, it is quite difficult to use conventional techniques to prove controller stability. Since safety-critical systems must be tested and verified to work as expected for all possible circumstances, the fact that FLC controllers cannot be tested to achieve such requirements poses limitations on the applications for such technology. Therefore, alternative methods for verification and validation of FLCs needs to be explored. In this study, a novel approach using formal verification methods to ensure the stability of a FLC is proposed. Main research challenges include specification of requirements for a complex system, conversion of a traditional FLC to a piecewise polynomial representation, and using a formal verification tool in a nonlinear solution space. Using the proposed architecture, the Fuzzy Logic Controller was found to always generate negative feedback, but inconclusive for Lyapunov stability.
Zhang, Xian-Xia; Jiang, Ye; Li, Han-Xiong; Li, Shao-Yuan
2013-10-01
A data-driven 3-D fuzzy-logic controller (3-D FLC) design methodology based on support vector regression (SVR) learning is developed for nonlinear spatially distributed dynamic systems. Initially, the spatial information expression and processing as well as the fuzzy linguistic expression and rule inference of a 3-D FLC are integrated into spatial fuzzy basis functions (SFBFs), and then the 3-D FLC can be depicted by a three-layer network structure. By relating SFBFs of the 3-D FLC directly to spatial kernel functions of an SVR, an equivalence relationship of the 3-D FLC and the SVR is established, which means that the 3-D FLC can be designed with the help of the SVR learning. Subsequently, for an easy implementation, a systematic SVR learning-based 3-D FLC design scheme is formulated. In addition, the universal approximation capability of the proposed 3-D FLC is presented. Finally, the control of a nonlinear catalytic packed-bed reactor is considered as an application to demonstrate the effectiveness of the proposed 3-D FLC.
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
Hilloowala, R.M.; Sharaf, A.M.
1996-01-01
The paper presents a rule-based fuzzy logic controller to control the output power of a pulse width modulated (PWM) inverter used in a stand alone wind energy conversion scheme (SAWECS). The self-excited induction generator used in SAWECS has the inherent problem of fluctuations in the magnitude and frequency of its terminal voltage with changes in wind velocity and load. To overcome this drawback the variable magnitude, variable frequency voltage at the generator terminals is rectified and the dc power is transferred to the load through a PWM inverter. The objective is to track and extract maximum power from the wind energy system (WES) and transfer this power to the local isolated load. This is achieved by using the fuzzy logic controller which regulates the modulation index of the PWM inverter based on the input signals: the power error e = (P{sub ref} {minus} P{sub o}) and its rate of change {dot e}. These input signals are fuzzified, that is defined by a set of linguistic labels characterized by their membership functions predefined for each class. Using a set of 49 rules which relate the fuzzified input signals (e, {dot e}) to the fuzzy controller output U, fuzzy set theory and associated fuzzy logic operations, the fuzzy controller`s output (in terms of linguistic labels) is defuzzified to obtain the actual analog (numerical) output signal which is then used to control the PWM inverter and ensure complete utilization of the available wind energy. The proposed rule-based fuzzy logic controller is simulated and the results are experimentally verified on a scaled down laboratory prototype of the SAWECS.
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.
Fuzzy Logic Classification of Imaging Laser Desorption Fourier Transform Mass Spectrometry Data
Timothy R. McJunkin; Jill R. Scott
2008-06-01
The fuzzy logic method is applied to classification of mass spectra obtained with an imaging internal source Fourier transform mass spectrometer (I2LD-FTMS). Traditionally, an operator uses the relative abundance of ions with specific mass-to-charge (m/z) ratios to categorize spectra. An operator does this by comparing the spectrum of m/z versus abundance of an unknown sample against a library of spectra from known samples. Automated positioning and acquisition allow the I2LD-FTMS to acquire data from very large grids, which would require classification of up to 3600 spectra per hour to keep pace with the acquisition. The tedious job of classifying numerous spectra generated in an I2LD-FTMS imaging application can be replaced by a fuzzy rule base if the cues an operator uses can be encapsulated. Appropriate methods for assigning fuzzy membership values for inputs (e.g., mass spectrum abundances) and choice of fuzzy inference operators to translate linguistic antecedent into confidence values for the consequence (or in this case the classification) is followed by using the maximum confidence and a necessary minimum threshold for making a crisp decision. This paper also describes a method for gathering statistics on ions, which are not currently used in the rule base, but which may be candidates for making the rule base more accurate and complete or to form new rule bases based on data obtained from known samples. A spatial method for classifying spectra with low membership values, based on neighboring sample classifications, is also presented.
A Robot Manipulator with Adaptive Fuzzy Controller in Obstacle Avoidance
NASA Astrophysics Data System (ADS)
Sreekumar, Muthuswamy
2016-07-01
Building robots and machines to act within a fuzzy environment is a problem featuring complexity and ambiguity. In order to avoid obstacles, or move away from it, the robot has to perform functions such as obstacle identification, finding the location of the obstacle, its velocity, direction of movement, size, shape, and so on. This paper presents about the design, and implementation of an adaptive fuzzy controller designed for a 3 degree of freedom spherical coordinate robotic manipulator interfaced with a microcontroller and an ultrasonic sensor. Distance between the obstacle and the sensor and its time rate are considered as inputs to the controller and how the manipulator to take diversion from its planned trajectory, in order to avoid collision with the obstacle, is treated as output from the controller. The obstacles are identified as stationary or moving objects and accordingly adaptive self tuning is accomplished with three set of linguistic rules. The prototype of the manipulator has been fabricated and tested for collision avoidance by placing stationary and moving obstacles in its planned trajectory. The performance of the adaptive control algorithm is analyzed in MATLAB by generating 3D fuzzy control surfaces.
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,…
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,…
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.
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.
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
Fuzzy Logic and Glacier Dynamics Assessment: New Paradigms for Operational Hazard Detection Systems
NASA Astrophysics Data System (ADS)
Furfaro, R.; Kargel, J. S.; Fink, W.; Bishop, M. P.
2008-12-01
Satellite-based remote sensing is critical for monitoring highly dynamic environments that include rapidly changing alpine glaciers, melt-water production, and a variety of natural hazards. Multi-spectral and multi- temporal satellite data in conjunction with digital elevation models can be utilized to assess supraglacial and proglacial lakes, valley impoundment water volumes, and the potential for flood and debris-flow hazards. Advanced remote sensing and GIS-based methodologies represent the only effective approach for periodic assessment and detection of glacier hazards using spatio-temporal data and analysis. Such approaches, however, do not address all of the requirements needed for the development of hazard/disaster warning systems and the generation of unique information to help establish mitigation strategies. Consequently, our objectives are to introduce the methods of fuzzy logic as an additional level of analysis and interpretation to demonstrate how intelligent, knowledge-driven algorithms can be used to assess glacier dynamics and glacier-induced hazards. Operational monitoring of dynamic environments and natural hazards will require multiple levels of analysis and information production using on-board automation. These systems must autonomously assess the hazard potential related to surface processes and the topography, while being able to identify disaster conditions. Such systems should (1) include analytical capabilities to permit automated and comprehensive identification, characterization, and quantification of terrain features (e.g., via Automated Global Feature Analyzer "AGFA"); (2) permit operational multi-scale hazard potential assessment (e.g., automatic global, regional and local assessment capabilities); and (3) permit data integration that fuses existing data and real-time data acquisition into a spatio-temporal framework that facilitates intelligent assessment and monitoring. The fuzzy logic framework may be an ideal approach that serves
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.
Adaptive fuzzy control with smooth inverse for nonlinear systems preceded by non-symmetric dead-zone
NASA Astrophysics Data System (ADS)
Wang, Xingjian; Wang, Shaoping
2016-07-01
In this study, the adaptive output feedback control problem of a class of nonlinear systems preceded by non-symmetric dead-zone is considered. To cope with the possible control signal chattering phenomenon which is caused by non-smooth dead-zone inverse, a new smooth inverse is proposed for non-symmetric dead-zone compensation. For the systematic design procedure of the adaptive fuzzy control algorithm, we combine the backstepping technique and small-gain approach. The Takagi-Sugeno fuzzy logic systems are used to approximate unknown system nonlinearities. The closed-loop stability is studied by using small gain theorem and the closed-loop system is proved to be semi-globally uniformly ultimately bounded. Simulation results indicate that, compared to the algorithm with the non-smooth inverse, the proposed control strategy can achieve better tracking performance and the chattering phenomenon can be avoided effectively.
Pipelined recurrent fuzzy neural networks for nonlinear adaptive speech prediction.
Stavrakoudis, Dimitris G; Theocharis, John B
2007-10-01
A class of pipelined recurrent fuzzy neural networks (PRFNNs) is proposed in this paper for nonlinear adaptive speech prediction. The PRFNNs are modular structures comprising a number of modules that are interconnected in a chained form. Each module is implemented by a small-scale recurrent fuzzy neural network (RFNN) with internal dynamics. Due to module nesting, the PRFNNs offer a number of desirable attributes, including decomposition of the modeling task, enhanced temporal processing capabilities, and multistage dynamic fuzzy inference. Tuning of the PRFNN adaptable parameters is accomplished by a series of gradient descent methods with different weighting of the modules and the decoupled extended Kalman filter (DEKF) algorithm, based on weight grouping. Extensive experimentation is carried out to evaluate the performance of the PRFNNs on the speech prediction platform. Comparative analysis shows that the PRFNNs outperform the single-RFNN models in terms of the prediction gains that are obtained and computational efficiency. Furthermore, PRFNNs provide considerably better performance compared to pipelined recurrent neural networks, for models with similar model complexity.
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.
A comparison of fuzzy logic-PID control strategies for PWR pressurizer control
Kavaklioglu, K.; Ikonomopoulos, A. )
1993-01-01
This paper describes the results obtained from a comparison performed between classical proportional-integral-derivative (PID) and fuzzy logic (FL) controlling the pressure in a pressurized water reactor (PWR). The two methodologies have been tested under various transient scenarios, and their performances are evaluated with respect to robustness and on-time response to external stimuli. One of the main concerns in the safe operation of PWR is the pressure control in the primary side of the system. In order to maintain the pressure in a PWR at the desired level, the pressurizer component equipped with sprayers, heaters, and safety relief valves is used. The control strategy in a Westinghouse PWR is implemented with a PID controller that initiates either the electric heaters or the sprayers, depending on the direction of the coolant pressure deviation from the setpoint.
Fuzzy logic based on-line fault detection and classification in transmission line.
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.
2013-01-01
This study proposes the promotion of environmental protection education among communities as a solution to the serious problems of high energy consumption and carbon emissions around the world. Environmental protection education has direct and lasting influences on everyone in society; therefore, it is helpful in our fight against many serious problems caused by high energy consumption. In this study, the Delphi method and the fuzzy logic theory are used to develop a quantizing assessment model based on qualitative analysis. This model can be used to assess the results and influences of community residents' participation in environmental protection education on green community development. In addition, it can be used to provide references for governing authorities in their decision making of green community development policies. PMID:24363614
Assessing the quality of sardine based on biogenic amines using a fuzzy logic model.
Zare, Davood; Ghazali, H M
2017-04-15
There is an increasing concern about the quality and quality assessment procedures of seafood. In the present study, a model to assess fish quality based on biogenic amine contents using fuzzy logic model (FLM) is proposed. The fish used was sardine (Sardinella sp.) where the production of eight biogenic amines was monitored over fifteen days of storage at 0, 3 and 10°C. Based on the results, histamine, putrescine and cadaverine were selected as input variables and twelve quality grades were considered for quality of fish as output variables for the FLM. Input data were processed by rules established in the model and were then defuzzified according to defined output variables. Finally, the quality of fish was evaluated using the designed model and Pearson correlation between storage times with quality of fish showed r=0.97, 0.95 and 1 for fish stored at 0, 3 and 10°C, respectively.
The 0/0 problem in the fuzzy-logical model of perception.
Schwartz, Jean-Luc
2006-10-01
The "Fuzzy-Logical Model of Perception" (FLMP) has often been questioned for its presumed ability to fit any data, but no clear-cut evidence has been presented yet. This paper demonstrates the ability of the FLMP to fit random data in the "McGurk region," that is in conditions involving conflicting stimuli. This is due to the so-called "0/0 problem," consisting in the fact that any audio-visual response can be fitted by the FLMP if the audio and visual stimuli provide at least one null probability in each possible category. The consequence is a high instability of the root mean square error in the region of the best fit.
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.
Bordon, Jure; Moskon, Miha; Zimic, Nikolaj; Mraz, Miha
2015-01-01
Quantitative modelling of biological systems has become an indispensable computational approach in the design of novel and analysis of existing biological systems. However, kinetic data that describe the system's dynamics need to be known in order to obtain relevant results with the conventional modelling techniques. These data are often hard or even impossible to obtain. Here, we present a quantitative fuzzy logic modelling approach that is able to cope with unknown kinetic data and thus produce relevant results even though kinetic data are incomplete or only vaguely defined. Moreover, the approach can be used in the combination with the existing state-of-the-art quantitative modelling techniques only in certain parts of the system, i.e., where kinetic data are missing. The case study of the approach proposed here is performed on the model of three-gene repressilator.
[Research on the Application of Fuzzy Logic to Systems Analysis and Control
NASA Technical Reports Server (NTRS)
1998-01-01
Research conducted with the support of NASA Grant NCC2-275 has been focused in the main on the development of fuzzy logic and soft computing methodologies and their applications to systems analysis and control. with emphasis 011 problem areas which are of relevance to NASA's missions. One of the principal results of our research has been the development of a new methodology called Computing with Words (CW). Basically, in CW words drawn from a natural language are employed in place of numbers for computing and reasoning. There are two major imperatives for computing with words. First, computing with words is a necessity when the available information is too imprecise to justify the use of numbers, and second, when there is a tolerance for imprecision which can be exploited to achieve tractability, robustness, low solution cost, and better rapport with reality. Exploitation of the tolerance for imprecision is an issue of central importance in CW.
A fuzzy-logic-based controller for methane production in anaerobic fixed-film reactors.
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.
A study of fuzzy logic ensemble system performance on face recognition problem
NASA Astrophysics Data System (ADS)
Polyakova, A.; Lipinskiy, L.
2017-02-01
Some problems are difficult to solve by using a single intelligent information technology (IIT). The ensemble of the various data mining (DM) techniques is a set of models which are able to solve the problem by itself, but the combination of which allows increasing the efficiency of the system as a whole. Using the IIT ensembles can improve the reliability and efficiency of the final decision, since it emphasizes on the diversity of its components. The new method of the intellectual informational technology ensemble design is considered in this paper. It is based on the fuzzy logic and is designed to solve the classification and regression problems. The ensemble consists of several data mining algorithms: artificial neural network, support vector machine and decision trees. These algorithms and their ensemble have been tested by solving the face recognition problems. Principal components analysis (PCA) is used for feature selection.
Shunt hybrid active power filter under nonideal voltage based on fuzzy logic controller
NASA Astrophysics Data System (ADS)
Dey, Papan; Mekhilef, Saad
2016-09-01
In this paper, a synchronous reference frame (SRF) method based on a modified phase lock loop (PLL) circuit is developed for a three-phase four-wire shunt hybrid active power filter (APF). Its performance is analysed under unbalanced grid conditions. The dominant lower order harmonics as well as reactive power can be compensated by the passive elements, whereas the active part mitigates the remaining distortions and improves the power quality. As different control methods show contradictory performance, fuzzy logic controller is considered here for DC-link voltage regulation of the inverter. Extensive simulations of the proposed technique are carried out in a MATLAB-SIMULINK environment. A laboratory prototype has been built on dSPACE1104 platform to verify the feasibility of the suggested SHAPF controller. The simulation and experimental results validate the effectiveness of the proposed technique.
Hsueh, Sung-Lin
2013-01-01
This study proposes the promotion of environmental protection education among communities as a solution to the serious problems of high energy consumption and carbon emissions around the world. Environmental protection education has direct and lasting influences on everyone in society; therefore, it is helpful in our fight against many serious problems caused by high energy consumption. In this study, the Delphi method and the fuzzy logic theory are used to develop a quantitizing assessment model based on qualitative analysis. This model can be used to assess the results and influences of community residents' participation in environmental protection education on green community development. In addition, it can be used to provide references for governing authorities in their decision making of green community development policies.
SOM guided fuzzy logic prospectivity model for gold in the Häme Belt, southwestern Finland
NASA Astrophysics Data System (ADS)
Leväniemi, Hanna; Hulkki, Helena; Tiainen, Markku
2017-04-01
This study investigated gold prospectivity in the Paleoproterozoic Häme Belt, located in southwestern Finland. The Häme Belt comprises calc-alkaline and tholeitic volcanic rocks, migmatites, granitoids, and mafic to ultramafic intrusions. Mineral exploration in the region has resulted in the discovery of several gold occurrences during recent decades; however, no prospectivity modeling for gold has yet been conducted. This study integrated till geochemical and geophysical data to examine and extract data characteristics critical for gold occurrences. Modeling was guided by self-organizing map (SOM) analysis to define essential data associations and to aid in model input data selection and generation. The final fuzzy logic prospectivity model map yielded high predictability values for most known Au or Cu-Au occurrences, but also highlighted new targets for exploration.
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.
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.
Aggregation of sensory data using fuzzy logic for sensory quality evaluation of food.
Debjani, Chakraborty; Das, Shrilekha; Das, H
2013-12-01
A method of sensory evaluation using fuzzy logic has been proposed in this paper. The method was applied for evaluation of sensory quality of tea liquor made out of dried CTC tea. Linguistic data (e.g., excellent, very good, good, satisfactory, fair, not-satisfactory, etc.) on individual tea liquor's quality attributes and the perception of the evaluators (e.g., extremely important, highly important, important, somewhat important, not-at-all important, etc.) for relative importance of these quality attributes were obtained. Sensory score between 0 and 100 for (i) Judges' preference for different quality attributes of tea liquor in general, (ii) Quality attributes ranking of tea liquor and the (ii) Overall quality of tea liquor were estimated. The last one can be utilized for the ranking of the different tea liquors.
PID self tuning control based on Mamdani fuzzy logic control for quadrotor stabilization
Priyambodo, Tri Kuntoro Putra, Agfianto Eko; Dharmawan, Andi
2016-02-01
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 constants 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.
A spatial fuzzy logic approach to urban multi-hazard impact assessment in Concepción, Chile.
Araya-Muñoz, Dahyann; Metzger, Marc J; Stuart, Neil; Wilson, A Meriwether W; Carvajal, Danilo
2017-01-15
Even though most cities are exposed to more than one hazard, local planners and decision-makers still have a limited understanding of the exposure and sensitivity to and the spatial distribution of hazards. We examine the impact of multiple hazards in the Concepción Metropolitan Area (CMA), Chile. A flexible methodology based on spatial fuzzy logic modelling was developed to explore the impact of weather-related hazards, including coastal flooding, fluvial flooding, water scarcity, heat stress, and wildfire. 32 indicators were standardised and then aggregated through a stepwise approach into a multi-hazard impact index. We find that all the municipalities in the CMA increased their level of impact between 1992 and 2002, due to a larger increase in the exposure rather than the modest decrease in sensitivity. Municipal sensitivity was driven mostly by changes in the population's age structure. Wildfires and water scarcity appeared to have the largest impact on all municipalities. Fuzzy modelling offered high flexibility in the standardisation and aggregation of indicators with diverse characteristics, while also providing a means to explore how the interaction of numerous indicators influenced the index. The resulting maps can help identify indicators, components, and hazards or combinations of hazards that most influence the impact on municipalities. The results can be used to improve and promote dialogue among policy-makers and stakeholders regarding prioritisation of resources for urban development in ways that can also reduce exposure and sensitivity and lower vulnerability to climate change. The methods presented can be adapted to other cities.
Validation of Fuzzy Logic Method for Automated Mass Spectral Classification for Mineral Imaging
B. Yan; B. Yan; T. R. McJunkiin; D. L. Stoner
2006-12-01
Imaging mass spectrometry requires the acquisition and interpretation of hundreds to thousands of individual spectra in order to map the mineral phases within heterogeneous geomatrices. A fuzzy logic inference engine (FLIE) was developed to automate data interpretation. To evaluate the strengths and limitations of FLIE, the chemical images obtained using FLIE were compared with those developed using two chemometric methods: principle component analysis (PCA) and cluster analysis (K-Means). Two heterogeneous geomatrices, a low-grade chalcopyrite ore and basalt, were imaged using a laser-desorption Fourier transform mass spectrometer. Similar mineral distribution patterns in the chalcopyrite ore sample were obtained by the three data analysis methods with most of the differences occurring at the interfaces between mineral phases. PCA missed one minor mineral phase in the chalcopyrite ore sample and did not clearly differentiate among the mineral classes of the basalt. K-Means cluster analysis differentiated among the various mineral phases in both samples, but improperly grouped some spectra in the chalcopyrite sample that only contained unanticipated high mass peaks. Unlike the chemometric methods, FLIE was able to classify spectra as unknowns for those spectra that fell below the confidence level threshold. A nearest neighbor approach, included in FLIE, was used to classify the unknowns to form a visually complete image; however, the unknowns identified by FLIE can be informative because they highlight potential problems or overlooked results. In conclusion, this study validated the fuzzy logic-based approach used in our laboratory and reveald some limitations in the three techniques that were evaluated.
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 Logic Based Edge Detection in Smooth and Noisy Clinical Images.
Haq, Izhar; Anwar, Shahzad; Shah, Kamran; Khan, Muhammad Tahir; Shah, Shaukat Ali
2015-01-01
Edge detection has beneficial applications in the fields such as machine vision, pattern recognition and biomedical imaging etc. Edge detection highlights high frequency components in the image. Edge detection is a challenging task. It becomes more arduous when it comes to noisy images. This study focuses on fuzzy logic based edge detection in smooth and noisy clinical images. The proposed method (in noisy images) employs a 3 × 3 mask guided by fuzzy rule set. Moreover, in case of smooth clinical images, an extra mask of contrast adjustment is integrated with edge detection mask to intensify the smooth images. The developed method was tested on noise-free, smooth and noisy images. The results were compared with other established edge detection techniques like Sobel, Prewitt, Laplacian of Gaussian (LOG), Roberts and Canny. When the developed edge detection technique was applied to a smooth clinical image of size 270 × 290 pixels having 24 dB 'salt and pepper' noise, it detected very few (22) false edge pixels, compared to Sobel (1931), Prewitt (2741), LOG (3102), Roberts (1451) and Canny (1045) false edge pixels. Therefore it is evident that the developed method offers improved solution to the edge detection problem in smooth and noisy clinical images.
Towards the Application of Fuzzy Logic for Developing a Novel Indoor Air Quality Index (FIAQI)
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
Fuzzy Logic Analysis of Alumina-Titania Deposition Yield During APS Process
NASA Astrophysics Data System (ADS)
Kanta, Abdoul-Fatah; Montavon, Ghislain; Planche, Marie-Pierre; Coddet, Christian
2007-12-01
The behavior modeling of Atmospheric Plasma Spray (APS) process requires a global approach which considers interrelated non-linear relationships between coating characteristics/properties in-service and process parameters (power, feedstock injection, kinematics, etc.). Such an approach would permit to reduce the development costs. To reach this objective, the knowledge of the interactions between process parameters plays a relevant role in the optimization. This work intends to develop a behavior model based on fuzzy logic concepts. Here, the model considers the deposition yield as the result of the process and it establishes relationships with power process parameter (arc current intensity, plasma gas total flow rate, hydrogen content) on the basis of fuzzy rules. The model hence permits to discriminate the role and the effects of each power process parameters. The modeling results are compared to experimental data. The specific case of the deposition of alumina-titania (Al2O3-TiO2, 13% by weight) by Atmospheric Plasma Spraying (APS) is considered.
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
Burn control of an ITER-like fusion reactor using fuzzy logic
NASA Astrophysics Data System (ADS)
Garcia-Amador, A. Sair; Martinell, Julio J.
2016-10-01
The fuel burn in a fusion reactor has to be kept at a nearly constant rate in order to have a steady power exhaust. Here, we develop a control system based on a fuzzy logic controller in order that adjusts external parameters to keep the plasma temperature and density at the design values of a reactor of the characteristics of ITER. The control parameters chosen are the D-T refueling rate, the auxiliary heating power and a neutral helium beam. We use a fuzzy controller of the Mamdani type that uses a number of membership functions appropriate to produce a response to parameter deviations that minimizes the response time. The inference rules are determined in a way to provide stabilization to all perturbations of the temperature, density and alpha particle fraction. The dynamical response of the reactor is simulated with a 0D model that uses confinement times provided by the ITER scaling. We show that the system is feedback stabilized for a large range of parameters around the nominal values. The recovery time after a departure from the steady values is of the order of one second. We compare the results with another control system based on neural networks that was developed previously. Funded by projects PAPIIT IN109115 and Conacyt 152905.
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.
Medical diagnosis imaging systems: image and signal processing applications aided by fuzzy logic
NASA Astrophysics Data System (ADS)
Hata, Yutaka
2010-04-01
First, we describe an automated procedure for segmenting an MR image of a human brain based on fuzzy logic for diagnosing Alzheimer's disease. The intensity thresholds for segmenting the whole brain of a subject are automatically determined by finding the peaks of the intensity histogram. After these thresholds are evaluated in a region growing, the whole brain can be identified. Next, we describe a procedure for decomposing the obtained whole brain into the left and right cerebral hemispheres, the cerebellum and the brain stem. Our method then identified the whole brain, the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem. Secondly, we describe a transskull sonography system that can visualize the shape of the skull and brain surface from any point to examine skull fracture and some brain diseases. We employ fuzzy signal processing to determine the skull and brain surface. The phantom model, the animal model with soft tissue, the animal model with brain tissue, and a human subjects' forehead is applied in our system. The all shapes of the skin surface, skull surface, skull bottom, and brain tissue surface are successfully determined.
Fuzzy Logic Based Edge Detection in Smooth and Noisy Clinical Images
Haq, Izhar
2015-01-01
Edge detection has beneficial applications in the fields such as machine vision, pattern recognition and biomedical imaging etc. Edge detection highlights high frequency components in the image. Edge detection is a challenging task. It becomes more arduous when it comes to noisy images. This study focuses on fuzzy logic based edge detection in smooth and noisy clinical images. The proposed method (in noisy images) employs a 3×3 mask guided by fuzzy rule set. Moreover, in case of smooth clinical images, an extra mask of contrast adjustment is integrated with edge detection mask to intensify the smooth images. The developed method was tested on noise-free, smooth and noisy images. The results were compared with other established edge detection techniques like Sobel, Prewitt, Laplacian of Gaussian (LOG), Roberts and Canny. When the developed edge detection technique was applied to a smooth clinical image of size 270×290 pixels having 24 dB ‘salt and pepper’ noise, it detected very few (22) false edge pixels, compared to Sobel (1931), Prewitt (2741), LOG (3102), Roberts (1451) and Canny (1045) false edge pixels. Therefore it is evident that the developed method offers improved solution to the edge detection problem in smooth and noisy clinical images. PMID:26407133
Fuzzy Multicriteria Decision Analysis for Adaptive Watershed Management
NASA Astrophysics Data System (ADS)
Chang, N.
2006-12-01
The dramatic changes of societal complexity due to intensive interactions among agricultural, industrial, and municipal sectors have resulted in acute issues of water resources redistribution and water quality management in many river basins. Given the fact that integrated watershed management is more a political and societal than a technical challenge, there is a need for developing a compelling method leading to justify a water-based land use program in some critical regions. Adaptive watershed management is viewed as an indispensable tool nowadays for providing step-wise constructive decision support that is concerned with all related aspects of the water consumption cycle and those facilities affecting water quality and quantity temporally and spatially. Yet the greatest challenge that decision makers face today is to consider how to leverage ambiguity, paradox, and uncertainty to their competitive advantage of management policy quantitatively. This paper explores a fuzzy multicriteria evaluation method for water resources redistribution and subsequent water quality management with respect to a multipurpose channel-reservoir system--the Tseng- Wen River Basin, South Taiwan. Four fuzzy operators tailored for this fuzzy multicriteria decision analysis depict greater flexibility in representing the complexity of various possible trade-offs among management alternatives constrained by physical, economic, and technical factors essential for adaptive watershed management. The management strategies derived may enable decision makers to integrate a vast number of internal weirs, water intakes, reservoirs, drainage ditches, transfer pipelines, and wastewater treatment facilities within the basin and bring up the permitting issue for transboundary diversion from a neighboring river basin. Experience gained indicates that the use of different types of fuzzy operators is highly instructive, which also provide unique guidance collectively for achieving the overarching goals
NASA Astrophysics Data System (ADS)
Aliouane, Leila; Ouadfeul, Sid-Ali; Boudella, Amar
2015-04-01
The main goal of the proposed idea is to use the artificial intelligence such as the neural network and fuzzy logic to predict the pore pressure in shale gas reservoirs. Pore pressure is a very important parameter that will be used or estimation of effective stress. This last is used to resolve well-bore stability problems, failure plan identification from Mohr-Coulomb circle and sweet spots identification. Many models have been proposed to estimate the pore pressure from well-logs data; we can cite for example the equivalent depth model, the horizontal model for undercompaction called the Eaton's model…etc. All these models require a continuous measurement of the slowness of the primary wave, some thing that is not easy during well-logs data acquisition in shale gas formtions. Here, we suggest the use the fuzzy logic and the multilayer perceptron neural network to predict the pore pressure in two horizontal wells drilled in the lower Barnett shale formation. The first horizontal well is used for the training of the fuzzy set and the multilayer perecptron, the input is the natural gamma ray, the neutron porosity, the slowness of the compression and shear wave, however the desired output is the estimated pore pressure using Eaton's model. Data of another horizontal well are used for generalization. Obtained results clearly show the power of the fuzzy logic system than the multilayer perceptron neural network machine to predict the pore pressure in shale gas reservoirs. Keywords: artificial intelligence, fuzzy logic, pore pressure, multilayer perecptron, Barnett shale.
Design of fuzzy system by NNs and realization of adaptability
NASA Technical Reports Server (NTRS)
Takagi, Hideyuki
1993-01-01
The issue of designing and tuning fuzzy membership functions by neural networks (NN's) was started by NN-driven Fuzzy Reasoning in 1988. NN-driven fuzzy reasoning involves a NN embedded in the fuzzy system which generates membership values. In conventional fuzzy system design, the membership functions are hand-crafted by trial and error for each input variable. In contrast, NN-driven fuzzy reasoning considers several variables simultaneously and can design a multidimensional, nonlinear membership function for the entire subspace.
Holakooie, Mohammad Hosein; Ojaghi, Mansour; Taheri, Asghar
2016-01-01
This paper investigates sensorless indirect field oriented control (IFOC) of SLIM with full-order Luenberger observer. The dynamic equations of SLIM are first elaborated to draw full-order Luenberger observer with some simplifying assumption. The observer gain matrix is derived from conventional procedure so that observer poles are proportional to SLIM poles to ensure the stability of system for wide range of linear speed. The operation of observer is significantly impressed by adaptive scheme. A fuzzy logic control (FLC) is proposed as adaptive scheme to estimate linear speed using speed tuning signal. The parameters of FLC are tuned using an off-line method through chaotic optimization algorithm (COA). The performance of the proposed observer is verified by both numerical simulation and real-time hardware-in-the-loop (HIL) implementation. Moreover, a detailed comparative study among proposed and other speed observers is obtained under different operation conditions.
Robust adaptive self-structuring fuzzy control design for nonaffine, nonlinear systems
NASA Astrophysics Data System (ADS)
Chen, Pin-Cheng; Wang, Chi-Hsu; Lee, Tsu-Tian
2011-01-01
In this article, a robust adaptive self-structuring fuzzy control (RASFC) scheme for the uncertain or ill-defined nonlinear, nonaffine systems is proposed. The RASFC scheme is composed of a robust adaptive controller and a self-structuring fuzzy controller. In the self-structuring fuzzy controller design, a novel self-structuring fuzzy system (SFS) is used to approximate the unknown plant nonlinearity, and the SFS can automatically grow and prune fuzzy rules to realise a compact fuzzy rule base. The robust adaptive controller is designed to achieve an L 2 tracking performance to stabilise the closed-loop system. This L 2 tracking performance can provide a clear expression of tracking error in terms of the sum of lumped uncertainty and external disturbance, which has not been shown in previous works. Finally, five examples are presented to show that the proposed RASFC scheme can achieve favourable tracking performance, yet heavy computational burden is relieved.
Fayek, H M; Elamvazuthi, I; Perumal, N; Venkatesh, B
2014-09-01
A computationally-efficient systematic procedure to design an Optimal Type-2 Fuzzy Logic Controller (OT2FLC) is proposed. The main scheme is to optimize the gains of the controller using Particle Swarm Optimization (PSO), then optimize only two parameters per type-2 membership function using Genetic Algorithm (GA). The proposed OT2FLC was implemented in real-time to control the position of a DC servomotor, which is part of a robotic arm. The performance judgments were carried out based on the Integral Absolute Error (IAE), as well as the computational cost. Various type-2 defuzzification methods were investigated in real-time. A comparative analysis with an Optimal Type-1 Fuzzy Logic Controller (OT1FLC) and a PI controller, demonstrated OT2FLC׳s superiority; which is evident in handling uncertainty and imprecision induced in the system by means of noise and disturbances.
NASA Astrophysics Data System (ADS)
Tsyganskaya, V.; Martinis, S.; Twele, A.; Cao, W.; Schmitt, A.; Marzahn, P.; Ludwig, R.
2016-06-01
In this paper an algorithm designed to map flooded vegetation from synthetic aperture radar (SAR) imagery is introduced. The approach is based on fuzzy logic which enables to deal with the ambiguity of SAR data and to integrate multiple ancillary data containing topographical information, simple hydraulic considerations and land cover information. This allows the exclusion of image elements with a backscatter value similar to flooded vegetation, to significantly reduce misclassification errors. The flooded vegetation mapping procedure is tested on a flood event that occurred in Germany over parts of the Saale catchment on January 2011 using a time series of high resolution TerraSAR-X data covering the time interval from 2009 to 2015. The results show that the analysis of multi-temporal X-band data combined with ancillary data using a fuzzy logic-based approach permits the detection of flooded vegetation areas.
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.
NASA Astrophysics Data System (ADS)
Ullah, Muhammed Zafar
Neural Network and Fuzzy Logic are the two key technologies that have recently received growing attention in solving real world, nonlinear, time variant problems. Because of their learning and/or reasoning capabilities, these techniques do not need a mathematical model of the system, which may be difficult, if not impossible, to obtain for complex systems. One of the major problems in portable or electric vehicle world is secondary cell charging, which shows non-linear characteristics. Portable-electronic equipment, such as notebook computers, cordless and cellular telephones and cordless-electric lawn tools use batteries in increasing numbers. These consumers demand fast charging times, increased battery lifetime and fuel gauge capabilities. All of these demands require that the state-of-charge within a battery be known. Charging secondary cells Fast is a problem, which is difficult to solve using conventional techniques. Charge control is important in fast charging, preventing overcharging and improving battery life. This research work provides a quick and reliable approach to charger design using Neural-Fuzzy technology, which learns the exact battery charging characteristics. Neural-Fuzzy technology is an intelligent combination of neural net with fuzzy logic that learns system behavior by using system input-output data rather than mathematical modeling. The primary objective of this research is to improve the secondary cell charging algorithm and to have faster charging time based on neural network and fuzzy logic technique. Also a new architecture of a controller will be developed for implementing the charging algorithm for the secondary battery.
A novel approach in water quality assessment based on fuzzy logic.
Gharibi, Hamed; Mahvi, Amir Hossein; Nabizadeh, Ramin; Arabalibeik, Hossein; Yunesian, Masud; Sowlat, Mohammad Hossein
2012-12-15
The present work aimed at developing a novel water quality index based on fuzzy logic, that is, a comprehensive artificial intelligence (AI) approach to the development of environmental indices for routine assessment of surface water quality, particularly for human drinking purposes. Twenty parameters were included based on their critical importance for the overall water quality and their potential impact on human health. To assess the performance of the proposed index under actual conditions, a case study was conducted at Mamloo dam, Iran, employing water quality data of four sampling stations in the water basin of the dam from 2006 to 2009. Results of this study indicated that the general quality of water in all the sampling stations over all the years of the study period is fairly low (yearly averages are usually in the range of 45-55). According to the results of ANOVA test, water quality did not significantly change over time in any of the sampling stations (P > 0.05). In addition, comparison of the outputs of the fuzzy-based proposed index proposed with those of the NSF water quality index (the WQI) and Canadian Water Quality Index (CWQI) showed similar results and were sensitive to changes in the level of water quality parameters. However, the index proposed by the present study produced a more stringent outputs compared to the WQI and CWQI. Results of the sensitivity analysis suggested that the index is robust against the changes in the rules. In conclusion, the proposed index seems to produce accurate and reliable results and can therefore be used as a comprehensive tool for water quality assessment, especially for the analysis of human drinking water.
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.
Cb-LIKE - Thunderstorm forecasts up to six hours with fuzzy logic
NASA Astrophysics Data System (ADS)
Köhler, Martin; Tafferner, Arnold
2016-04-01
Thunderstorms with their accompanying effects like heavy rain, hail, or downdrafts cause delays and flight cancellations and therefore high additional cost for airlines and airport operators. A reliable thunderstorm forecast up to several hours could provide more time for decision makers in air traffic for an appropriate reaction on possible storm cells and initiation of adequate counteractions. To provide the required forecasts Cb-LIKE (Cumulonimbus-LIKElihood) has been developed at the DLR (Deutsches Zentrum für Luft- und Raumfahrt) Institute of Atmospheric Physics. The new algorithm is an automated system which designates areas with possible thunderstorm development by using model data of the COSMO-DE weather model, which is driven by the German Meteorological Service (DWD). A newly developed "Best-Member- Selection" method allows the automatic selection of that particular model run of a time-lagged COSMO- DE model ensemble, which matches best the current thunderstorm situation. Thereby the application of the best available data basis for the calculation of the thunderstorm forecasts by Cb-LIKE is ensured. Altogether there are four different modes for the selection of the best member. Four atmospheric parameters (CAPE, vertical wind velocity, radar reflectivity and cloud top temperature) of the model output are used within the algorithm. A newly developed fuzzy logic system enables the subsequent combination of the model parameters and the calculation of a thunderstorm indicator within a value range of 12 up to 88 for each grid point of the model domain for the following six hours in one hour intervals. The higher the indicator value the more the model parameters imply the development of thunderstorms. The quality of the Cb-LIKE thunderstorm forecasts was evaluated by a substantial verification using a neighborhood verification approach and multi-event contingency tables. The verification was performed for the whole summer period of 2012. On the basis of a
NASA Astrophysics Data System (ADS)
Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.
2014-05-01
The purpose of this study is to examine the use of Artificial Neural Networks (ANN) combined with kriging interpolation method, in order to simulate the hydraulic head both spatially and temporally. Initially, ANNs are used for the temporal simulation of the hydraulic head change. The results of the most appropriate ANNs, determined through a fuzzy logic system, are used as an input for the kriging algorithm where the spatial simulation is conducted. The proposed algorithm is tested in an area located across Isar River in Bayern, Germany and covers an area of approximately 7800 km2. The available data extend to a time period from 1/11/2008 to 31/10/2012 (1460 days) and include the hydraulic head at 64 wells, temperature and rainfall at 7 weather stations and surface water elevation at 5 monitoring stations. One feedforward ANN was trained for each of the 64 wells, where hydraulic head data are available, using a backpropagation algorithm. The most appropriate input parameters for each wells' ANN are determined considering their proximity to the measuring station, as well as their statistical characteristics. For the rainfall, the data for two consecutive time lags for best correlated weather station, as well as a third and fourth input from the second best correlated weather station, are used as an input. The surface water monitoring stations with the three best correlations for each well are also used in every case. Finally, the temperature for the best correlated weather station is used. Two different architectures are considered and the one with the best results is used henceforward. The output of the ANNs corresponds to the hydraulic head change per time step. These predictions are used in the kriging interpolation algorithm. However, not all 64 simulated values should be used. The appropriate neighborhood for each prediction point is constructed based not only on the distance between known and prediction points, but also on the training and testing error of
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.
Fuzzy logic sensing of G-quadruplex DNA and its cleavage reagents based on reduced graphene oxide.
Huang, Wei Tao; Zhang, Jian Rong; Xie, Wan Yi; Shi, Yan; Luo, Hong Qun; Li, Nian Bing
2014-07-15
Herein, by combining the merits of nanotechnology and fuzzy logic theory, we develop a simple, label-free, and general strategy based on an organic dye-graphene hybrid system for fluorescence intelligent sensing of G-quadruplexes (G4) formation, hydroxyl radical (HO∙), and Fe(2+) in vitro. By exploiting acridine orange (AO) dyes-graphene as a nanofilter and nanoswitch and the ability of graphene to interact with DNA with different structures, our approach can efficiently distinguish, quantitatively detect target analytes. In vitro assays with G4DNA demonstrated increases in fluorescence intensity of the AO-rGO system with a linear range of 16-338 nM and a detection limit as low as 2.0 nM. The requenched fluorescence of the G4TBA-AO-rGO system has a non-linear response to Fenton reagent. But this requenching reduces the fluorescence intensity in a manner proportional to the logarithm to the base 10 of the concentration of Fenton reagent in the range of 0.1-100 μM and 100-2000 μM, respectively. Furthermore, we develop a novel and intelligent sensing method based on fuzzy logic which mimics human reasoning, solves complex and non-linear problems, and transforms the numerical output into the language description output for potential application in biochemical systems, environmental monitoring systems, and molecular-level fuzzy logic computing system.
Mansouri, Mohammad; Teshnehlab, Mohammad; Aliyari Shoorehdeli, Mahdi
2015-05-01
In this paper, a novel adaptive hierarchical fuzzy control system based on the variable structure control is developed for a class of SISO canonical nonlinear systems in the presence of bounded disturbances. It is assumed that nonlinear functions of the systems be completely unknown. Switching surfaces are incorporated into the hierarchical fuzzy control scheme to ensure the system stability. A fuzzy soft switching system decides the operation area of the hierarchical fuzzy control and variable structure control systems. All the nonlinearly appeared parameters of conclusion parts of fuzzy blocks located in different layers of the hierarchical fuzzy control system are adjusted through adaptation laws deduced from the defined Lyapunov function. The proposed hierarchical fuzzy control system reduces the number of rules and consequently the number of tunable parameters with respect to the ordinary fuzzy control system. Global boundedness of the overall adaptive system and the desired precision are achieved using the proposed adaptive control system. In this study, an adaptive hierarchical fuzzy system is used for two objectives; it can be as a function approximator or a control system based on an intelligent-classic approach. Three theorems are proven to investigate the stability of the nonlinear dynamic systems. The important point about the proposed theorems is that they can be applied not only to hierarchical fuzzy controllers with different structures of hierarchical fuzzy controller, but also to ordinary fuzzy controllers. Therefore, the proposed algorithm is more general. To show the effectiveness of the proposed method four systems (two mechanical, one mathematical and one chaotic) are considered in simulations. Simulation results demonstrate the validity, efficiency and feasibility of the proposed approach to control of nonlinear dynamic systems.
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.
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.
A novel fuzzy logic-based image steganography method to ensure medical data security.
Karakış, R; Güler, I; Çapraz, I; Bilir, E
2015-12-01
This study aims to secure medical data by combining them into one file format using steganographic methods. The electroencephalogram (EEG) is selected as hidden data, and magnetic resonance (MR) images are also used as the cover image. In addition to the EEG, the message is composed of the doctor׳s comments and patient information in the file header of images. Two new image steganography methods that are based on fuzzy-logic and similarity are proposed to select the non-sequential least significant bits (LSB) of image pixels. The similarity values of the gray levels in the pixels are used to hide the message. The message is secured to prevent attacks by using lossless compression and symmetric encryption algorithms. The performance of stego image quality is measured by mean square of error (MSE), peak signal-to-noise ratio (PSNR), structural similarity measure (SSIM), universal quality index (UQI), and correlation coefficient (R). According to the obtained result, the proposed method ensures the confidentiality of the patient information, and increases data repository and transmission capacity of both MR images and EEG signals.
Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data
Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie
2016-01-01
Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine. PMID:27774993
NASA Astrophysics Data System (ADS)
Kaloko, Bambang Sri; Atsari, Erinna Dyah
2017-03-01
Electric motive force which flows into the iron core continuously on a plate - plate iron isolated may cause heat posed by current eddy (eddy current). No water loss occurs due to detainees on the circuit at the the flow of current load because this loss happened on the entanglement of the transformer is made of copper. Continuously Transposed Conductors (CTC) consist of a number of enameled rectangular wires (5-84 strands) made into an assembly. Each strand is transposed in turn to each position in the cable and is then covered with layers of insulation paper. Continuously Transposed Conductors are used in winding wires for medium and ultra high power transformers. CTC is manufactured by OFHC copper and indeed, is able to supply polyester roped. CTC which has been designed to reduce production cost, oil pocket and improve cooling efficiency. Hardened type CTC (CPR1, CPR2, and CPR3: BS1432) and Self-bonding CTC which can be used to improve mechanical and electrical strength are also available. This analysis is performed using the methods of fuzzy logic in taking account of the resources.
Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic.
Keramitsoglou, Iphigenia; Kiranoudis, Chris T; Maiheu, Bino; De Ridder, Koen; Daglis, Ioannis A; Manunta, Paolo; Paganini, Marc
2013-10-01
The average summer temperatures as well as the frequency and intensity of hot days and heat waves are expected to increase due to climate change. Motivated by this consequence, we propose a methodology to evaluate the monthly heat wave hazard and risk and its spatial distribution within large cities. A simple urban climate model with assimilated satellite-derived land surface temperature images was used to generate a historic database of urban air temperature fields. Heat wave hazard was then estimated from the analysis of these hourly air temperatures distributed at a 1-km grid over Athens, Greece, by identifying the areas that are more likely to suffer higher temperatures in the case of a heat wave event. Innovation lies in the artificial intelligence fuzzy logic model that was used to classify the heat waves from mild to extreme by taking into consideration their duration, intensity and time of occurrence. The monthly hazard was subsequently estimated as the cumulative effect from the individual heat waves that occurred at each grid cell during a month. Finally, monthly heat wave risk maps were produced integrating geospatial information on the population vulnerability to heat waves calculated from socio-economic variables.
Fuzzy logic-based approach to detecting a passive RFID tag in an outpatient clinic.
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.
Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data
NASA Astrophysics Data System (ADS)
Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie
2016-10-01
Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine.
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.
Fernández-Camacho, R; Brito Cabeza, I; Aroba, J; Gómez-Bravo, F; Rodríguez, S; de la Rosa, J
2015-04-15
This study focuses on correlations between total number concentrations, road traffic emissions and noise levels in an urban area in the southwest of Spain during the winter and summer of 2009. The high temporal correlation between sound pressure levels, traffic intensity, particle number concentrations related to traffic, black carbon and NOx concentrations suggests that noise is linked to traffic emissions as a main source of pollution in urban areas. First, the association of these different variables was studied using PreFuRGe, a computational tool based on data mining and fuzzy logic. The results showed a clear association between noise levels and road-traffic intensity for non-extremely high wind speed levels. This behaviour points, therefore, to vehicular emissions being the main source of urban noise. An analysis for estimating the total number concentration from noise levels is also proposed in the study. The high linearity observed between particle number concentrations linked to traffic and noise levels with road traffic intensity can be used to calculate traffic related particle number concentrations experimentally. At low wind speeds, there are increases in noise levels of 1 dB for every 100 vehicles in circulation. This is equivalent to 2000 cm(-3) per vehicle in winter and 500 cm(-3) in summer. At high wind speeds, wind speed could be taken into account. This methodology allows low cost sensors to be used as a proxy for total number concentration monitoring in urban air quality networks.
Keller, Roland; Klein, Marcus; Thomas, Maria; Dräger, Andreas; Metzger, Ute; Templin, Markus F; Joos, Thomas O; Thasler, Wolfgang E; Zell, Andreas; Zanger, Ulrich M
2016-01-01
During various inflammatory processes circulating cytokines including IL-6, IL-1β, and TNFα elicit a broad and clinically relevant impairment of hepatic detoxification that is based on the simultaneous downregulation of many drug metabolizing enzymes and transporter genes. To address the question whether a common mechanism is involved we treated human primary hepatocytes with IL-6, the major mediator of the acute phase response in liver, and characterized acute phase and detoxification responses in quantitative gene expression and (phospho-)proteomics data sets. Selective inhibitors were used to disentangle the roles of JAK/STAT, MAPK, and PI3K signaling pathways. A prior knowledge-based fuzzy logic model comprising signal transduction and gene regulation was established and trained with perturbation-derived gene expression data from five hepatocyte donors. Our model suggests a greater role of MAPK/PI3K compared to JAK/STAT with the orphan nuclear receptor RXRα playing a central role in mediating transcriptional downregulation. Validation experiments revealed a striking similarity of RXRα gene silencing versus IL-6 induced negative gene regulation (rs = 0.79; P<0.0001). These results concur with RXRα functioning as obligatory heterodimerization partner for several nuclear receptors that regulate drug and lipid metabolism.
Novel intelligent real-time position tracking system using FPGA and fuzzy logic.
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.
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
Spatiotemporal prediction applying fuzzy logic in a sequence of satellite images
NASA Astrophysics Data System (ADS)
Mezzadri-Centeno, Tania; Selleron, Gilles
2002-01-01
Spatial evolutions of anthropized ecosystems and the progressive transformation of spaces in the course of time emerge more and more as a special interest issue in researches about the environment. This evolution constitutes one of the major concerns in the domain of environmental space management. The landscape evolution of a region area and the perspectives for a future state rises an issue particularly important. What will be the state of the region in 15, 30 or 50 years? Time can produce transformations over a region area like emergence, disappearance or union of spatial entities... These transformations are called temporal phenomena. We propose to predict the forestry evolution in the forthcoming years on an experimental area, which reveals these spatial transformations. The proposed method is based on the analysis of terrain landscape given a sequence of n satellite images, which represent the state of a region area in different years. For these purposes, we have developed a specific spatio-temporal prediction approach, linking results of forestry evolution analysis and fuzzy logic. The method is supported by the analysis of the landscape dynamics of a test-site located in a tropical rain country: the oriental piedmont of Andes Mountain in Venezuela. This large area - at the scale of a spot satellite image - is typical of tropical deforestation in a pioneer front. The presented approach allows the geographer interested in environmental prospective problems to get type cartographical documents showing future conditions of a landscape. The experimental tests have showed promising results.
Sattler, Claudia; Stachow, Ulrich; Berger, Gert
2012-03-01
The study presented here describes a modeling approach for the ex-ante assessment of farming practices with respect to their risk for several single-species biodiversity indicators. The approach is based on fuzzy-logic techniques and, thus, is tolerant to the inclusion of sources of uncertain knowledge, such as expert judgment into the assessment. The result of the assessment is a so-called Index of Suitability (IS) for the five selected biotic indicators calculated per farming practice. Results of IS values are presented for the comparison of crops and for the comparison of several production alternatives per crop (e.g., organic vs. integrated farming, mineral vs. organic fertilization, and reduced vs. plow tillage). Altogether, the modeled results show that the different farming practices can greatly differ in terms of their suitability for the different biotic indicators and that the farmer has a certain scope of flexibility in opting for a farming practice that is more in favor of biodiversity conservation. Thus, the approach is apt to identify farming practices that contribute to biodiversity conservation and, moreover, enables the identification of farming practices that are suitable with respect to more than one biotic indicator.
Thomas, Maria; Dräger, Andreas; Metzger, Ute; Templin, Markus F.; Joos, Thomas O.; Thasler, Wolfgang E.; Zell, Andreas; Zanger, Ulrich M.
2016-01-01
During various inflammatory processes circulating cytokines including IL-6, IL-1β, and TNFα elicit a broad and clinically relevant impairment of hepatic detoxification that is based on the simultaneous downregulation of many drug metabolizing enzymes and transporter genes. To address the question whether a common mechanism is involved we treated human primary hepatocytes with IL-6, the major mediator of the acute phase response in liver, and characterized acute phase and detoxification responses in quantitative gene expression and (phospho-)proteomics data sets. Selective inhibitors were used to disentangle the roles of JAK/STAT, MAPK, and PI3K signaling pathways. A prior knowledge-based fuzzy logic model comprising signal transduction and gene regulation was established and trained with perturbation-derived gene expression data from five hepatocyte donors. Our model suggests a greater role of MAPK/PI3K compared to JAK/STAT with the orphan nuclear receptor RXRα playing a central role in mediating transcriptional downregulation. Validation experiments revealed a striking similarity of RXRα gene silencing versus IL-6 induced negative gene regulation (rs = 0.79; P<0.0001). These results concur with RXRα functioning as obligatory heterodimerization partner for several nuclear receptors that regulate drug and lipid metabolism. PMID:26727233
Enhancing the selection of backoff interval using fuzzy logic over wireless Ad Hoc networks.
Ranganathan, Radha; Kannan, Kathiravan
2015-01-01
IEEE 802.11 is the de facto standard for medium access over wireless ad hoc network. The collision avoidance mechanism (i.e., random binary exponential backoff-BEB) of IEEE 802.11 DCF (distributed coordination function) is inefficient and unfair especially under heavy load. In the literature, many algorithms have been proposed to tune the contention window (CW) size. However, these algorithms make every node select its backoff interval between [0, CW] in a random and uniform manner. This randomness is incorporated to avoid collisions among the nodes. But this random backoff interval can change the optimal order and frequency of channel access among competing nodes which results in unfairness and increased delay. In this paper, we propose an algorithm that schedules the medium access in a fair and effective manner. This algorithm enhances IEEE 802.11 DCF with additional level of contention resolution that prioritizes the contending nodes according to its queue length and waiting time. Each node computes its unique backoff interval using fuzzy logic based on the input parameters collected from contending nodes through overhearing. We evaluate our algorithm against IEEE 802.11, GDCF (gentle distributed coordination function) protocols using ns-2.35 simulator and show that our algorithm achieves good performance.
A novel algorithm for ventricular arrhythmia classification using a fuzzy logic approach.
Weixin, Nong
2016-12-01
In the present study, it has been shown that an unnecessary implantable cardioverter-defibrillator (ICD) shock is often delivered to patients with an ambiguous ECG rhythm in the overlap zone between ventricular tachycardia (VT) and ventricular fibrillation (VF); these shocks significantly increase mortality. Therefore, accurate classification of the arrhythmia into VT, organized VF (OVF) or disorganized VF (DVF) is crucial to assist ICDs to deliver appropriate therapy. A classification algorithm using a fuzzy logic classifier was developed for accurately classifying the arrhythmias into VT, OVF or DVF. Compared with other studies, our method aims to combine ten ECG detectors that are calculated in the time domain and the frequency domain in addition to different levels of complexity for detecting subtle structure differences between VT, OVF and DVF. The classification in the overlap zone between VT and VF is refined by this study to avoid ambiguous identification. The present method was trained and tested using public ECG signal databases. A two-level classification was performed to first detect VT with an accuracy of 92.6 %, and then the discrimination between OVF and DVF was detected with an accuracy of 84.5 %. The validation results indicate that the proposed method has superior performance in identifying the organization level between the three types of arrhythmias (VT, OVF and DVF) and is promising for improving the appropriate therapy choice and decreasing the possibility of sudden cardiac death.
Liu, Hu-Chen; Liu, Long; Lin, Qing-Lian; Liu, Nan
2013-06-01
The two most important issues of expert systems are the acquisition of domain experts' professional knowledge and the representation and reasoning of the knowledge rules that have been identified. First, during expert knowledge acquisition processes, the domain expert panel often demonstrates different experience and knowledge from one another and produces different types of knowledge information such as complete and incomplete, precise and imprecise, and known and unknown because of its cross-functional and multidisciplinary nature. Second, as a promising tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) still suffer a couple of deficiencies. The parameters in current FPN models could not accurately represent the increasingly complex knowledge-based systems, and the rules in most existing knowledge inference frameworks could not be dynamically adjustable according to propositions' variation as human cognition and thinking. In this paper, we present a knowledge acquisition and representation approach using the fuzzy evidential reasoning approach and dynamic adaptive FPNs to solve the problems mentioned above. As is illustrated by the numerical example, the proposed approach can well capture experts' diversity experience, enhance the knowledge representation power, and reason the rule-based knowledge more intelligently.
Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images
NASA Astrophysics Data System (ADS)
Deng, He; Deng, Wankai; Sun, Xianping; Ye, Chaohui; Zhou, Xin
2016-10-01
Image enhancement techniques are able to improve the contrast and visual quality of magnetic resonance (MR) images. However, conventional methods cannot make up some deficiencies encountered by respective brain tumor MR imaging modes. In this paper, we propose an adaptive intuitionistic fuzzy sets-based scheme, called as AIFE, which takes information provided from different MR acquisitions and tries to enhance the normal and abnormal structural regions of the brain while displaying the enhanced results as a single image. The AIFE scheme firstly separates an input image into several sub images, then divides each sub image into object and background areas. After that, different novel fuzzification, hyperbolization and defuzzification operations are implemented on each object/background area, and finally an enhanced result is achieved via nonlinear fusion operators. The fuzzy implementations can be processed in parallel. Real data experiments demonstrate that the AIFE scheme is not only effectively useful to have information from images acquired with different MR sequences fused in a single image, but also has better enhancement performance when compared to conventional baseline algorithms. This indicates that the proposed AIFE scheme has potential for improving the detection and diagnosis of brain tumors.
Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images
Deng, He; Deng, Wankai; Sun, Xianping; Ye, Chaohui; Zhou, Xin
2016-01-01
Image enhancement techniques are able to improve the contrast and visual quality of magnetic resonance (MR) images. However, conventional methods cannot make up some deficiencies encountered by respective brain tumor MR imaging modes. In this paper, we propose an adaptive intuitionistic fuzzy sets-based scheme, called as AIFE, which takes information provided from different MR acquisitions and tries to enhance the normal and abnormal structural regions of the brain while displaying the enhanced results as a single image. The AIFE scheme firstly separates an input image into several sub images, then divides each sub image into object and background areas. After that, different novel fuzzification, hyperbolization and defuzzification operations are implemented on each object/background area, and finally an enhanced result is achieved via nonlinear fusion operators. The fuzzy implementations can be processed in parallel. Real data experiments demonstrate that the AIFE scheme is not only effectively useful to have information from images acquired with different MR sequences fused in a single image, but also has better enhancement performance when compared to conventional baseline algorithms. This indicates that the proposed AIFE scheme has potential for improving the detection and diagnosis of brain tumors. PMID:27786240
Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images.
Deng, He; Deng, Wankai; Sun, Xianping; Ye, Chaohui; Zhou, Xin
2016-10-27
Image enhancement techniques are able to improve the contrast and visual quality of magnetic resonance (MR) images. However, conventional methods cannot make up some deficiencies encountered by respective brain tumor MR imaging modes. In this paper, we propose an adaptive intuitionistic fuzzy sets-based scheme, called as AIFE, which takes information provided from different MR acquisitions and tries to enhance the normal and abnormal structural regions of the brain while displaying the enhanced results as a single image. The AIFE scheme firstly separates an input image into several sub images, then divides each sub image into object and background areas. After that, different novel fuzzification, hyperbolization and defuzzification operations are implemented on each object/background area, and finally an enhanced result is achieved via nonlinear fusion operators. The fuzzy implementations can be processed in parallel. Real data experiments demonstrate that the AIFE scheme is not only effectively useful to have information from images acquired with different MR sequences fused in a single image, but also has better enhancement performance when compared to conventional baseline algorithms. This indicates that the proposed AIFE scheme has potential for improving the detection and diagnosis of brain tumors.
An Application of Fuzzy Logic Control to a Classical Military Tracking Problem
1994-05-19
34Probability Measures of Fuzzy Events", Journal of Mathematical Analysis and Applications , vol.23, 1968, p.421. 9. Kosko, Bart. "Fuzziness Versus...January 1973, pp.28-44. Zadeh, L.A. "Probability Measures of Fuzzy Events", Journal of Mathematical Analysis and Applications , vol.23, 1968, pp.421
Adaptive Fuzzy Bounded Control for Consensus of Multiple Strict-Feedback Nonlinear Systems.
Wang, Wei; Tong, Shaocheng
2017-01-10
This paper studies the adaptive fuzzy bounded control problem for leader-follower multiagent systems, where each follower is modeled by the uncertain nonlinear strict-feedback system. Combining the fuzzy approximation with the dynamic surface control, an adaptive fuzzy control scheme is developed to guarantee the output consensus of all agents under directed communication topologies. Different from the existing results, the bounds of the control inputs are known as a priori, and they can be determined by the feedback control gains. To realize smooth and fast learning, a predictor is introduced to estimate each error surface, and the corresponding predictor error is employed to learn the optimal fuzzy parameter vector. It is proved that the developed adaptive fuzzy control scheme guarantees the uniformly ultimate boundedness of the closed-loop systems, and the tracking error converges to a small neighborhood of the origin. The simulation results and comparisons are provided to show the validity of the control strategy presented in this paper.
NASA Astrophysics Data System (ADS)
Ye, Bo-Young; Lee, GyuWon; Park, Hong-Mok
2015-09-01
A major issue in radar quantitative precipitation estimation is the contamination of radar echoes by non-meteorological targets such as ground clutter, chaff, clear air echoes etc. In this study, a fuzzy logic algorithm for the identification of non-meteorological echoes is developed using optimized membership functions and weights for the dual-polarization radar located at Mount Sobaek. For selected precipitation and non-meteorological events, the characteristics of the precipitation and non-meteorological echo are derived by the probability density functions of five fuzzy parameters as functions of reflectivity values. The membership functions and weights are then determined by these density functions. Finally, the nonmeteorological echoes are identified by combining the membership functions and weights. The performance is qualitatively evaluated by long-term rain accumulation. The detection accuracy of the fuzzy logic algorithm is calculated using the probability of detection (POD), false alarm rate (FAR), and clutter-signal ratio (CSR). In addition, the issues in using filtered dual-polarization data are alleviated.
Combining fuzzy logic and voting methods for matching pixel spectra with signature databases
NASA Astrophysics Data System (ADS)
Raeth, Peter G.; Pilati, Martin L.
2002-08-01
This paper discusses a method for searching a database of known material signatures to find the closest match with an unknown signature. This database search method combines fuzzy logic and voting methods to achieve a high level of classification accuracy with the signatures and data cubes tested. This paper discusses the method in detail to include background and test results. It makes reference to public literature concerning components used by the method but developed elsewhere. This paper results from a project whose main objective is to produce an easily integrated software tool that makes an accurate best-guess as to the material(s) indicated by the signature of a pixel found to be interesting according to some analysis method, such as anomaly detection and scene characterization. Anomaly detection examines a spectral cube and determines which pixels are unusual relative to the majority background. Scene characterization finds pixels whose signatures are representative of the unique pixel groups. The current project fully automates the process of determining unknown pixels of interest, taking the signatures from the flagged pixels, searching a database of known signatures, and making a best guess as to the material(s) represented by each pixel's signature. The method ranks the possible materials by order of likelihood with the purpose of accounting for multiple materials existing in the same pixel. In this way it is possible to deliver multiple reportings when more than one material is closely matched within some threshold. This facilitates human analysis and decision-making for productions purposes. The implementation facilitates rapid response to interactive analysis need in support of strategic and tactical operational requirements in both the civil and defense sectors.
Fuzzy Logic Trajectory Design and Guidance for Terminal Area Energy Management
NASA Technical Reports Server (NTRS)
Burchett, Bradley
2003-01-01
The second generation reusable launch vehicle will leverage many new technologies to make flight to low earth orbit safer and more cost effective. One important capability will be completely autonomous flight during reentry and landing, thus making it unnecessary to man the vehicle for cargo missions with stringent weight constraints. Implementation of sophisticated new guidance and control methods will enable the vehicle to return to earth under less than favorable conditions. The return to earth consists of three phases--Entry, Terminal Area Energy Management (TAEM), and Approach and Landing. The Space Shuttle is programmed to fly all three phases of flight automatically, and under normal circumstances the astronaut-pilot takes manual control only during the Approach and Landing phase. The automatic control algorithms used in the Shuttle for TAEM and Approach and Landing have been developed over the past 30 years. They are computationally efficient, and based on careful study of the spacecraft's flight dynamics, and heuristic reasoning. The gliding return trajectory is planned prior to the mission, and only minor adjustments are made during flight for perturbations in the vehicle energy state. With the advent of the X-33 and X-34 technology demonstration vehicles, several authors investigated implementing advanced control methods to provide autonomous real-time design of gliding return trajectories thus enhancing the ability of the vehicle to adjust to unusual energy states. The bulk of work published to date deals primarily with the approach and landing phase of flight where changes in heading angle are small, and range to the runway is monotonically decreasing. These benign flight conditions allow for model simplification and fairly straightforward optimization. This project focuses on the TAEM phase of flight where mathematically precise methods have produced limited results. Fuzzy Logic methods are used to make onboard autonomous gliding return trajectory
Fuzzy logic-based analogue forecasting and hybrid modelling of horizontal visibility
NASA Astrophysics Data System (ADS)
Tuba, Zoltán; Bottyán, Zsolt
2017-02-01
Forecasting visibility is one of the greatest challenges in aviation meteorology. At the same time, high accuracy visibility forecasts can significantly reduce or make avoidable weather-related risk in aviation as well. To improve forecasting visibility, this research links fuzzy logic-based analogue forecasting and post-processed numerical weather prediction model outputs in hybrid forecast. Performance of analogue forecasting model was improved by the application of Analytic Hierarchy Process. Then, linear combination of the mentioned outputs was applied to create ultra-short term hybrid visibility prediction which gradually shifts the focus from statistical to numerical products taking their advantages during the forecast period. It gives the opportunity to bring closer the numerical visibility forecast to the observations even it is wrong initially. Complete verification of categorical forecasts was carried out; results are available for persistence and terminal aerodrome forecasts (TAF) as well in order to compare. The average value of Heidke Skill Score (HSS) of examined airports of analogue and hybrid forecasts shows very similar results even at the end of forecast period where the rate of analogue prediction in the final hybrid output is 0.1-0.2 only. However, in case of poor visibility (1000-2500 m), hybrid (0.65) and analogue forecasts (0.64) have similar average of HSS in the first 6 h of forecast period, and have better performance than persistence (0.60) or TAF (0.56). Important achievement that hybrid model takes into consideration physics and dynamics of the atmosphere due to the increasing part of the numerical weather prediction. In spite of this, its performance is similar to the most effective visibility forecasting methods and does not follow the poor verification results of clearly numerical outputs.
FLEXnav: a fuzzy logic expert dead-reckoning system for the Segway RMP
NASA Astrophysics Data System (ADS)
Ojeda, Lauro; Raju, Mukunda; Borenstein, Johann
2004-09-01
Most mobile robots use a combination of absolute and relative sensing techniques for position estimation. Relative positioning techniques are generally known as dead-reckoning. Many systems use odometry as their only dead-reckoning means. However, in recent years fiber optic gyroscopes have become more affordable and are being used on many platforms to supplement odometry, especially in indoor applications. Still, if the terrain is not level (i.e., rugged or rolling terrain), the tilt of the vehicle introduces errors into the conversion of gyro readings to vehicle heading. In order to overcome this problem vehicle tilt must be measured and factored into the heading computation. A unique new mobile robot is the Segway Robotics Mobility Platform (RMP). This functionally close relative of the innovative Segway Human Transporter (HT) stabilizes a statically unstable single-axle robot dynamically, based on the principle of the inverted pendulum. While this approach works very well for human transportation, it introduces as unique set of challenges to navigation equipment using an onboard gyro. This is due to the fact that in operation the Segway RMP constantly changes its forward tilt, to prevent dynamically falling over. This paper introduces our new Fuzzy Logic Expert rule-based navigation (FLEXnav) method for fusing data from multiple gyroscopes and accelerometers in order to estimate accurately the attitude (i.e., heading and tilt) of a mobile robot. The attitude information is then further fused with wheel encoder data to estimate the three-dimensional position of the mobile robot. We have further extended this approach to include the special conditions of operation on the Segway RMP. The paper presents experimental results of a Segway RMP equipped with our system and running over moderately rugged terrain.
Fuzzy logic control of fuel cell for stand-alone and grid connection
NASA Astrophysics Data System (ADS)
Sakhare, Abhishek; Davari, Asad; Feliachi, Ali
Fuel cells have become one of the major areas of research in the academia and the industry with the numerous advantages they provide over the batteries and especially over the other small-scale sources of electricity including the photovoltaic and solar cells. Fuel cells generate electricity from hydrogen by a chemical process and are environmentally safe and efficient. Fuel cells have numerous stand-alone and grid-connected applications. The aim of the paper is to achieve the control of the fuel cell for stand-alone and grid connection. This is achieved by designing a suitable power conditioning unit. The power conditioning unit is needed for processing of the raw power output of the fuel cell in order to make it usable. The power conditioning unit might have only dc/dc converter or the two stages of dc/dc converter and dc/ac inverter. For the stand-alone part, the concentration is on the controlled direct current (dc) power, thus, only a boost converter (dc/dc) stage is used. For the grid interface of the fuel cell, controlled alternating current (ac) power is needed at the interface point of the fuel cell and the utility grid; thus, both stages, boost converter as well as the inverter (dc/ac), are needed. A power conditioning unit is designed for the solid oxide fuel cell, which can be used for other fuel cells with converter and the inverter of different ratings, but the control strategy will remain the same. The fuzzy logic control strategy is used for designing the controllers for both the stages.
NASA Astrophysics Data System (ADS)
Kong, Fansen; Chen, Ruheng
2004-01-01
A new combined method based on wavelet transformation, fuzzy logic and neuro-networks is proposed for fault diagnosis of a triplex. The failure characteristics of the fluid- and dynamic-end can be divided into wavelet transform in different scales at the same time (in: Jun Zhu et al. (Eds.), Proceedings of an International Conference on Condition Monitoring. National Defense Industry Press, Beijing, 1997, pp. 271-275). Therefore, the characteristic variables can be constructed making use of the coefficients of Edgeworth asymptotic spectrum expansion formula and fuzzified to train the neuro-network to identify the faults of fluid- and dynamic-end of triplex pump in fuzzy domain. Tests indicate that the information of wavelet transformation in scale 2 is related to the meshing state of the gear and the information in scales 4 and 5 is related to the running state of fluid-end. Good agreement between analytical and experimental results has been obtained.
Zaninelli, Mauro; Tangorra, Francesco Maria; Costa, Annamaria; Rossi, Luciana; Dell'Orto, Vittorio; Savoini, Giovanni
2016-07-13
The aim of this study was to develop and test a new fuzzy logic model for monitoring the udder health status (HS) of goats. The model evaluated, as input variables, the milk electrical conductivity (EC) signal, acquired on-line for each gland by a dedicated sensor, the bandwidth length and the frequency and amplitude of the first main peak of the Fourier frequency spectrum of the recorded milk EC signal. Two foremilk gland samples were collected from eight Saanen goats for six months at morning milking (lactation stages (LS): 0-60 Days In Milking (DIM); 61-120 DIM; 121-180 DIM), for a total of 5592 samples. Bacteriological analyses and somatic cell counts (SCC) were used to define the HS of the glands. With negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC > 1,000,000 cells/mL, glands were classified as not healthy (NH). For each EC signal, an estimated EC value was calculated and a relative deviation was obtained. Furthermore, the Fourier frequency spectrum was evaluated and bandwidth length, frequency and amplitude of the first main peak were identified. Before using these indexes as input variables of the fuzzy logic model a linear mixed-effects model was developed to evaluate the acquired data considering the HS, LS and LS × HS as explanatory variables. Results showed that performance of a fuzzy logic model, in the monitoring of mammary gland HS, could be improved by the use of EC indexes derived from the Fourier frequency spectra of gland milk EC signals recorded by on-line EC sensors.
NASA Technical Reports Server (NTRS)
Lu, Thomas; Pham, Timothy; Liao, Jason
2011-01-01
This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.
Tan, Chao; Xu, Rongxin; Wang, Zhongbin; Si, Lei; Liu, Xinhua
2016-01-01
In order to reduce the enlargement of coal floor deformation and the manual adjustment frequency of rocker arms, an improved approach through integration of improved genetic algorithm and fuzzy logic control (GFLC) method is proposed. The enlargement of coal floor deformation is analyzed and a model is built. Then, the framework of proposed approach is built. Moreover, the constituents of GA such as tangent function roulette wheel selection (Tan-RWS) selection, uniform crossover, and nonuniform mutation are employed to enhance the performance of GFLC. Finally, two simulation examples and an industrial application example are carried out and the results indicate that the proposed method is feasible and efficient. PMID:27217824
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.
Tan, Chao; Xu, Rongxin; Wang, Zhongbin; Si, Lei; Liu, Xinhua
2016-01-01
In order to reduce the enlargement of coal floor deformation and the manual adjustment frequency of rocker arms, an improved approach through integration of improved genetic algorithm and fuzzy logic control (GFLC) method is proposed. The enlargement of coal floor deformation is analyzed and a model is built. Then, the framework of proposed approach is built. Moreover, the constituents of GA such as tangent function roulette wheel selection (Tan-RWS) selection, uniform crossover, and nonuniform mutation are employed to enhance the performance of GFLC. Finally, two simulation examples and an industrial application example are carried out and the results indicate that the proposed method is feasible and efficient.
Using a hybrid genetic algorithm and fuzzy logic for metabolic modeling
Yen, J.; Lee, B.; Liao, J.C.
1996-12-31
The identification of metabolic systems is a complex task due to the complexity of the system and limited knowledge about the model. Mathematical equations and ODE`s have been used to capture the structure of the model, and the conventional optimization techniques have been used to identify the parameters of the model. In general, however, a pure mathematical formulation of the model is difficult due to parametric uncertainty and incomplete knowledge of mechanisms. In this paper, we propose a modeling approach that (1) uses fuzzy rule-based model to augment algebraic enzyme models that are incomplete, and (2) uses a hybrid genetic algorithm to identify uncertain parameters in the model. The hybrid genetic algorithm (GA) integrates a GA with the simplex method in functional optimization to improve the GA`s convergence rate. We have applied this approach to modeling the rate of three enzyme reactions in E. coli central metabolism. The proposed modeling strategy allows (1) easy incorporation of qualitative insights into a pure mathematical model and (2) adaptive identification and optimization of key parameters to fit system behaviors observed in biochemical experiments.
Liu, Zhi; Wang, Fang; Zhang, Yun; Chen, Xin; Chen, C L Philip
2014-10-01
This paper focuses on an input-to-state practical stability (ISpS) problem of nonlinear systems which possess unmodeled dynamics in the presence of unstructured uncertainties and dynamic disturbances. The dynamic disturbances depend on the states and the measured output of the system, and its assumption conditions are relaxed compared with the common restrictions. Based on an input-driven filter, fuzzy logic systems are directly used to approximate the unknown and desired control signals instead of the unknown nonlinear functions, and an integrated backstepping technique is used to design an adaptive output-feedback controller that ensures robustness with respect to unknown parameters and uncertain nonlinearities. This paper, by applying the ISpS theory and the generalized small-gain approach, shows that the proposed adaptive fuzzy controller guarantees the closed-loop system being semi-globally uniformly ultimately bounded. A main advantage of the proposed controller is that it contains only three adaptive parameters that need to be updated online, no matter how many states there are in the systems. Finally, the effectiveness of the proposed approach is illustrated by two simulation examples.
Study on adaptive PID algorithm of hydraulic turbine governing system based on fuzzy neural network
NASA Astrophysics Data System (ADS)
Tang, Liangbao; Bao, Jumin
2006-11-01
The conventional hydraulic turbine governing system can't automatically modulate PID parameters according to the dynamic process of the system, the generator speed is unstable and the mains frequency fluctuation results in. To solve the above problem, the fuzzy neural network (FNN) and the adaptive control are combined to design an adaptive PID algorithm based on the fuzzy neural network which can effectively control the hydraulic turbine governing system. Finally, the improved mathematic model is simulated. The simulation results are compared with the conventional hydraulic turbine's. Thus the validity and superiority of the fuzzy neural network PID algorithm have been proved. The simulation results show that the algorithm not only retains the functions of fuzzy control, but also provides the ability to approach to the non-linear system. Also the dynamic process of the system can be reflected more precisely and the on-line adaptive control is implemented. The algorithm is superior to other methods in response and control effect.
Suppression of impulse noise in medical images with the use of Fuzzy Adaptive Median Filter.
Toprak, Abdullah; Güler, Inan
2006-12-01
A new rule based fuzzy filter for removal of highly impulse noise, called Rule Based Fuzzy Adaptive Median (RBFAM) Filter, is aimed to be discussed in this paper. The RBFAM filter is an improved version of Adaptive Median Filter (AMF) and is presented in the aim of noise reduction of images corrupted with additive impulse noise. The filter has three stages. Two of those stages are fuzzy rule based and last stage is based on standard median and adaptive median filter. The proposed filter can preserve image details better then AMF while suppressing additive salt & pepper or impulse type noise. In this paper, we placed our preference on bell-shaped membership function instead of triangular membership function in order to observe better results. Experimental results indicates that the proposed filter is improvable with increased fuzzy rules to reduce more noise corrupted images and to remove salt and pepper noise in a more effective way than what AMF filter does.
Reducing the Impact of Uncertainties in Networked Control Systems Using Type-2 Fuzzy Logic
NASA Astrophysics Data System (ADS)
Michal, Blaho; J´n, Murgaš; Eugen, Viszus; Peter, Fodrek
2015-01-01
The networked control systems (NCS) have grown in popularity in recent years. Despite their advantages over the traditional control schemes, some of their drawbacks emerged as well (time delays, packet losses). There are several ways of dealing with the time delays and packet losses in NCS, but only a few authors have ever used type-2 fuzzy controllers for this purpose to our knowledge. This paper is aimed at dealing with the negative effects that occur in NCS, by using type-2 fuzzy control systems. It is presented that this approach can be successfully used to decrease the effects of time delays and packet losses. A type-2 fuzzy controller has been designed and compared to a type-1 fuzzy controller. The intervals of type-2 fuzzy controller were optimized via genetic algorithm.
Adaptive Robust Online Constructive Fuzzy Control of a Complex Surface Vehicle System.
Wang, Ning; Er, Meng Joo; Sun, Jing-Chao; Liu, Yan-Cheng
2016-07-01
In this paper, a novel adaptive robust online constructive fuzzy control (AR-OCFC) scheme, employing an online constructive fuzzy approximator (OCFA), to deal with tracking surface vehicles with uncertainties and unknown disturbances is proposed. Significant contributions of this paper are as follows: 1) unlike previous self-organizing fuzzy neural networks, the OCFA employs decoupled distance measure to dynamically allocate discriminable and sparse fuzzy sets in each dimension and is able to parsimoniously self-construct high interpretable T-S fuzzy rules; 2) an OCFA-based dominant adaptive controller (DAC) is designed by employing the improved projection-based adaptive laws derived from the Lyapunov synthesis which can guarantee reasonable fuzzy partitions; 3) closed-loop system stability and robustness are ensured by stable cancelation and decoupled adaptive compensation, respectively, thereby contributing to an auxiliary robust controller (ARC); and 4) global asymptotic closed-loop system can be guaranteed by AR-OCFC consisting of DAC and ARC and all signals are bounded. Simulation studies and comprehensive comparisons with state-of-the-arts fixed- and dynamic-structure adaptive control schemes demonstrate superior performance of the AR-OCFC in terms of tracking and approximation accuracy.
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
NASA Astrophysics Data System (ADS)
de C. Carneiro, Cleyton; Crósta, Alvaro P.; Silva, Adalene M.; de M. Barros, Carlos Eduardo
2008-10-01
The Anapu-Tuer" region, in the Brazilian Amazon, is known for the occurrence of mineral deposits associated with mafic-ultramafic rocks. The spatial distribution of these rocks was investigated by integrating airborne magnetic and gamma ray spectrometry data using Geographic Information System (GIS) and fuzzy logic classification. Highest values of relevance fuzzy were associated with mafic-ultramafic rocks mapped regionally by the Brazilian Geological Survey (CPRM). Furthermore, mineral occurrences mapped by the CPRM showed good spatial correlation with the higher values of relevance fuzzy presented in this paper.
Tong, Shaocheng; Liu, Changliang; Li, Yongming; Zhang, Huaguang
2011-04-01
In this paper, an adaptive fuzzy decentralized robust output feedback control approach is proposed for a class of large-scale strict-feedback nonlinear systems without the measurements of the states. The nonlinear systems in this paper are assumed to possess unstructured uncertainties, time-varying delays, and unknown high-frequency gain sign. Fuzzy logic systems are used to approximate the unstructured uncertainties, K-filters are designed to estimate the unmeasured states, and a special Nussbaum gain function is introduced to solve the problem of unknown high-frequency gain sign. Combining the backstepping technique with adaptive fuzzy control theory, an adaptive fuzzy decentralized robust output feedback control scheme is developed. In order to obtain the stability of the closed-loop system, a new lemma is given and proved. Based on this lemma and Lyapunov-Krasovskii functions, it is proved that all the signals in the closed-loop system are uniformly ultimately bounded and that the tracking errors can converge to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated from simulation results.
Adaptive fuzzy-neural-network control for maglev transportation system.
Wai, Rong-Jong; Lee, Jeng-Dao
2008-01-01
A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.
Chao, Fa-An; Kim, Jonggul; Xia, Youlin; Milligan, Michael; Rowe, Nancy; Veglia, Gianluigi
2014-08-01
We present an enhanced version of the FLAMEnGO (Fuzzy Logic Assignment of Methyl Group) software, a structure-based method to assign methyl group resonances in large proteins. FLAMEnGO utilizes a fuzzy logic algorithm coupled with Monte Carlo sampling to obtain a probability-based assignment of the methyl group resonances. As an input, FLAMEnGO requires either the protein X-ray structure or an NMR structural ensemble including data such as methyl-methyl NOESY, paramagnetic relaxation enhancement (PRE), methine-methyl TOCSY data. Version 2.0 of this software (FLAMEnGO 2.0) has a user-friendly graphic interface and presents improved modules that enable the input of partial assignments and additional NMR restraints. We tested the performance of FLAMEnGO 2.0 on maltose binding protein (MBP) as well as the C-subunit of the cAMP-dependent protein kinase A (PKA-C). FLAMEnGO 2.0 can be used as a standalone method or to assist in the completion of partial resonance assignments and can be downloaded at www.chem.umn.edu/groups/veglia/forms/flamengo2-form.html.
de Franciscis, Stefano; Fregola, Salvatore; Gallo, Alessandro; Argirò, Giuseppe; Barbetta, Andrea; Buffone, Gianluca; Caliò, Francesco G; De Caridi, Giovanni; Amato, Bruno; Serra, Raffaele
2016-12-01
Chronic leg ulcers (CLUs) are a common occurrence in the western population and are associated with a negative impact on the quality of life of patients. They also cause a substantial burden on the health budget. The pathogenesis of leg ulceration is quite heterogeneous, and chronic venous ulceration (CVU) is the most common manifestation representing the main complication of chronic venous disease (CVD). Prevention strategies and early identification of the risk represent the best form of management. Fuzzy logic is a flexible mathematical system that has proved to be a powerful tool for decision-making systems and pattern classification systems in medicine. In this study, we have elaborated a computerised prediction system for chronic leg ulcers (PredyCLU) based on fuzzy logic, which was retrospectively applied on a multicentre population of 77 patients with CVD. This evaluation system produced reliable risk score patterns and served effectively as a stratification risk tool in patients with CVD who were at the risk of developing CVUs.
A high-speed multiplexer-based fine-grain pipelined architecture for digital fuzzy logic controllers
NASA Astrophysics Data System (ADS)
Rashidi, Bahram; Masoud Sayedi, Sayed
2015-12-01
Design and implementation of a high-speed multiplexer-based fine-grain pipelined architecture for a general digital fuzzy logic controller has been presented. All the operators have been designed at gate level. For the multiplication, a multiplexer-based modified Wallace tree multiplier has been designed, and for the division and addition multiplexer-based non-restoring parallel divider and multiplexer-based Manchester adder have been used, respectively. To further increase the processing speed, fine-grain pipelining technique has been employed. By using this technique, the critical path of the circuit is broken into finer pieces. Based on the proposed architecture, and by using Quartus II 9.1, a sample two-input, one-output digital fuzzy logic controller with eight rules has been successfully synthesised and implemented on Stratix II field programmable gate array. Simulations were carried out using DSP Builder in the MATLAB/Simulink tool at a maximum clock rate of 301.84 MHz.
Jinturkar, A M; Deshmukh, S S; Agarkar, S V; Chavhan, G R
2010-01-01
The paper proposes fuzzy logic model that deals with the physico-chemical water analysis of ground water of Chikhli town for determination of Water Quality Index (WQI). The study was carried by collection of ground water samples from about eleven hand pumps located in this town. Ground water quality is studied by systematic collection and analysis of samples. The fuzzy logic is used for the deciding the water quality index on the basis of which, water quality rankings are given to determine the quality of water. The Water Quality Index presented here is a unitless number ranging from 1 to 10. A higher number is indicative of better water quality. Around 81% of samples were found suitable for drinking purpose. It is also observed that all the parameters fall within the permissible limits laid by WHO, ISI, and ICMR, except Total Hardness, Calcium and Magnesium. The quality parameters were compared with standards laid by the World Health Organization (WHO), Indian Standards Institute (ISI) and Indian Council of Medical Research (ICMR) for drinking water quality.
NASA Astrophysics Data System (ADS)
Chao, Fa-An; Kim, Jonggul; Xia, Youlin; Milligan, Michael; Rowe, Nancy; Veglia, Gianluigi
2014-08-01
We present an enhanced version of the FLAMEnGO (Fuzzy Logic Assignment of Methyl Group) software, a structure-based method to assign methyl group resonances in large proteins. FLAMEnGO utilizes a fuzzy logic algorithm coupled with Monte Carlo sampling to obtain a probability-based assignment of the methyl group resonances. As an input, FLAMEnGO requires either the protein X-ray structure or an NMR structural ensemble including data such as methyl-methyl NOESY, paramagnetic relaxation enhancement (PRE), methine-methyl TOCSY data. Version 2.0 of this software (FLAMEnGO 2.0) has a user-friendly graphic interface and presents improved modules that enable the input of partial assignments and additional NMR restraints. We tested the performance of FLAMEnGO 2.0 on maltose binding protein (MBP) as well as the C-subunit of the cAMP-dependent protein kinase A (PKA-C). FLAMEnGO 2.0 can be used as a standalone method or to assist in the completion of partial resonance assignments and can be downloaded at www.chem.umn.edu/groups/veglia/forms/flamengo2-form.html.
Pandey, Rupesh Kumar; Panda, Sudhansu Sekhar
2014-11-01
Drilling of bone is a common procedure in orthopedic surgery to produce hole for screw insertion to fixate the fracture devices and implants. The increase in temperature during such a procedure increases the chances of thermal invasion of bone which can cause thermal osteonecrosis resulting in the increase of healing time or reduction in the stability and strength of the fixation. Therefore, drilling of bone with minimum temperature is a major challenge for orthopedic fracture treatment. This investigation discusses the use of fuzzy logic and Taguchi methodology for predicting and minimizing the temperature produced during bone drilling. The drilling experiments have been conducted on bovine bone using Taguchi's L25 experimental design. A fuzzy model is developed for predicting the temperature during orthopedic drilling as a function of the drilling process parameters (point angle, helix angle, feed rate and cutting speed). Optimum bone drilling process parameters for minimizing the temperature are determined using Taguchi method. The effect of individual cutting parameters on the temperature produced is evaluated using analysis of variance. The fuzzy model using triangular and trapezoidal membership predicts the temperature within a maximum error of ±7%. Taguchi analysis of the obtained results determined the optimal drilling conditions for minimizing the temperature as A3B5C1.The developed system will simplify the tedious task of modeling and determination of the optimal process parameters to minimize the bone drilling temperature. It will reduce the risk of thermal osteonecrosis and can be very effective for the online condition monitoring of the process.
Khanesar, Mojtaba Ahmadieh; Kayacan, Erdal; Teshnehlab, Mohammad; Kaynak, Okyay
2011-10-01
In this paper, the noise reduction property of type-2 fuzzy logic (FL) systems (FLSs) (T2FLSs) that use a novel type-2 fuzzy membership function is studied. The proposed type-2 membership function has certain values on both ends of the support and the kernel and some uncertain values for the other values of the support. The parameter tuning rules of a T2FLS that uses such a membership function are derived using the gradient descend learning algorithm. There exist a number of papers in the literature that claim that the performance of T2FLSs is better than type-1 FLSs under noisy conditions, and the claim is tried to be justified by simulation studies only for some specific systems. In this paper, a simpler T2FLS is considered with the novel membership function proposed in which the effect of input noise in the rule base is shown numerically in a general way. The proposed type-2 fuzzy neuro structure is tested on different input-output data sets, and it is shown that the T2FLS with the proposed novel membership function has better noise reduction property when compared to the type-1 counterparts.
Study on Fuzzy Adaptive Fractional Order PIλDμ Control for Maglev Guiding System
NASA Astrophysics Data System (ADS)
Hu, Qing; Hu, Yuwei
The mathematical model of the linear elevator maglev guiding system is analyzed in this paper. For the linear elevator needs strong stability and robustness to run, the integer order PID was expanded to the fractional order, in order to improve the steady state precision, rapidity and robustness of the system, enhance the accuracy of the parameter in fractional order PIλDμ controller, the fuzzy control is combined with the fractional order PIλDμ control, using the fuzzy logic achieves the parameters online adjustment. The simulations reveal that the system has faster response speed, higher tracking precision, and has stronger robustness to the disturbance.
Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation.
Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi
2016-12-16
Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.
Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation
Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi
2016-01-01
Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency. PMID:27999261
Han, Honggui; Wu, Xiao-Long; Qiao, Jun-Fei
2014-04-01
In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems. This SOFNN-ACA is constructed online via simultaneous structure and parameter learning processes. In structure learning, a set of fuzzy rules can be self-designed using an information-theoretic methodology. The fuzzy rules with high spiking intensities (SI) are divided into new ones. And the fuzzy rules with a small relative mutual information (RMI) value will be pruned in order to simplify the FNN structure. In parameter learning, the consequent part parameters are learned through the use of an ACA that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed. Then, the convergence of SOFNN-ACA is analyzed. Finally, the proposed SOFNN-ACA is used to model nonlinear systems. The modeling results demonstrate that this proposed SOFNN-ACA can model nonlinear systems effectively.
Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System
Hosseini, Monireh Sheikh; Zekri, Maryam
2012-01-01
Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054
Study on application of adaptive fuzzy control and neural network in the automatic leveling system
NASA Astrophysics Data System (ADS)
Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng
2015-04-01
This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.
Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System.
Hosseini, Monireh Sheikh; Zekri, Maryam
2012-01-01
Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated.
Fuzzy Backstepping Torque Control Of Passive Torque Simulator With Algebraic Parameters Adaptation
NASA Astrophysics Data System (ADS)
Ullah, Nasim; Wang, Shaoping; Wang, Xingjian
2015-07-01
This work presents fuzzy backstepping control techniques applied to the load simulator for good tracking performance in presence of extra torque, and nonlinear friction effects. Assuming that the parameters of the system are uncertain and bounded, Algebraic parameters adaptation algorithm is used to adopt the unknown parameters. The effect of transient fuzzy estimation error on parameters adaptation algorithm is analyzed and the fuzzy estimation error is further compensated using saturation function based adaptive control law working in parallel with the actual system to improve the transient performance of closed loop system. The saturation function based adaptive control term is large in the transient time and settles to an optimal lower value in the steady state for which the closed loop system remains stable. The simulation results verify the validity of the proposed control method applied to the complex aerodynamics passive load simulator.
Wai, Rong-Jong; Yang, Zhi-Wei
2008-10-01
This paper focuses on the development of adaptive fuzzy neural network control (AFNNC), including indirect and direct frameworks for an n-link robot manipulator, to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to cope with this problem, an indirect AFNNC (IAFNNC) scheme and a direct AFNNC (DAFNNC) strategy are investigated without the requirement of prior system information. In these model-free control topologies, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with online learning ability is constructed to represent the system dynamics of an n-link robot manipulator. In the IAFNNC, an FNN estimator is designed to tune the nonlinear dynamic function vector in fuzzy local models, and then, the estimative vector is used to indirectly develop a stable IAFNNC law. In the DAFNNC, an FNN controller is directly designed to imitate a predetermined model-based stabilizing control law, and then, the stable control performance can be achieved by only using joint position information. All the IAFNNC and DAFNNC laws and the corresponding adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed methodologies. In addition, the superiority of the proposed control schemes is indicated in comparison with proportional-differential control, fuzzy-model-based control, T-S-type FNN control, and robust neural fuzzy network control systems.
Fuzzy adaptive synchronization of uncertain chaotic systems via delayed feedback control
NASA Astrophysics Data System (ADS)
Zhang, Lingling; Huang, Lihong; Zhang, Zhizhou; Wang, Zengyun
2008-09-01
Based on the T-S fuzzy model and the delayed feedback control (DFC) scheme, this Letter presents a robust synchronization strategy for a class of chaotic system with unknown parameters and disturbances. Being the response system, the designed robust observer can adaptively track the drive system globally. The T-S fuzzy model of the 4D chaotic system (Lorenz-Stenflo) is developed as an example for illustration. Numerical simulations are shown to verify the results.
NASA Astrophysics Data System (ADS)
Akdemir, Bayram; Doǧan, Sercan; Aksoy, Muharrem H.; Canli, Eyüp; Özgören, Muammer
2015-03-01
Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.
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.
NASA Astrophysics Data System (ADS)
Phu, Do Xuan; Shin, Do Kyun; Choi, Seung-Bok
2015-08-01
This paper presents a new adaptive fuzzy controller featuring a combination of two different control methodologies: H infinity control technique and sliding mode control. It is known that both controllers are powerful in terms of high performance and robust stability. However, both control methods require an accurate dynamic model to design a state variable based controller in order to maintain their advantages. Thus, in this work a fuzzy control method which does not require an accurate dynamic model is adopted and two control methodologies are integrated to maintain the advantages even in an uncertain environment of the dynamic system. After a brief explanation of the interval type 2 fuzzy logic, a new adaptive fuzzy controller associated with the H infinity control and sliding mode control is formulated on the basis of Lyapunov stability theory. Subsequently, the formulated controller is applied to vibration control of a vehicle seat equipped with magnetorheological fluid damper (MR damper in short). An experimental setup for realization of the proposed controller is established and vibration control performances such as acceleration at the driver’s seat are evaluated. In addition, in order to demonstrate the effectiveness of the proposed controller, a comparative work with two existing controllers is undertaken. It is shown through simulation and experiment that the proposed controller can provide much better vibration control performance than the two existing controllers.
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.
Analysis of microseismicity using fuzzy logic and fractals for fracture network characterization
NASA Astrophysics Data System (ADS)
Aminzadeh, F.; Ayatollahy Tafti, T.; Maity, D.; Boyle, K.; Sahimi, M.; Sammis, C. G.
2010-12-01
The area where microseismic events occur may be correlated with the fracture network at a geothermal field. For an Enhanced Geothermal System (EGS) reservoir, an extensive fracture network with a large aerial distribution is required. Pore-pressure increase, temperature changes, volume change due to fluid withdrawal/injection and chemical alteration of fracture surfaces are all mechanisms that may explain microseismic events at a geothermal field. If these mechanisms are operative, any fuzzy cluster of the microseismic events should represent a connected fracture network. Drilling new EGS wells (both injection and production wells) in these locations may facilitate the creation of an EGS reservoir. In this article we use the fuzzy clustering technique to find the location and characteristics of fracture networks in the Geysers geothermal field. We also show that the centers of these fuzzy clusters move in time, which may represent fracture propagation or fluid movement within the fracture network. Furthermore, analyzing the distribution of fuzzy hypocenters and quantifying their fractal structure helps us to develop an accurate fracture map for the reservoir. Combining the fuzzy clustering results with the fractal analysis allows us to better understand the mechanisms for fracture stimulation and better characterize the evolution of the fracture network. We also show how micro-earthquake date collected in different time periods can be correlated with drastic changes in the distribution of active fractures resulting from injection, production or other transient events.
NASA Astrophysics Data System (ADS)
El-Sebakhy, Emad A.
2009-09-01
Pressure-volume-temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson-Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.
NASA Astrophysics Data System (ADS)
Lai, Y. C.; Chang, C. C.; Tsai, C. M.; Lin, S. Y.; Huang, S. C.
2015-05-01
This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU) has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS). There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS sensors suffer from various errors due to the results of manufacturing imperfections and other effects. Therefore, a sensor calibration procedure based on the scalar calibration and the least squares methods has been induced in this study to improve the accuracy of the inertial sensors. With the calibrated data acquired from the inertial sensors, the step length and strength of the pedestrian are estimated by multi-sensor fusion and fuzzy logic estimation algorithms. The developed multi-sensor fusion algorithm provides the amount of the walking steps and the strength of each steps in real-time. Consequently, the estimated walking amount and strength per step are taken into the proposed fuzzy logic estimation algorithm to estimates the step lengths of the user. Since the walking length and direction are both the required information of the dead reckoning navigation, the walking direction is calculated by integrating the angular rate acquired by the gyroscope of the developed IMU module. Both the walking length and direction are calculated on the IMU module and transmit to a smartphone with Bluetooth to perform the dead reckoning navigation which is run on a self-developed APP. Due to the error accumulating of dead reckoning navigation, a particle filter and a pre-loaded map of indoor environment have been applied to the APP of the proposed navigation system to extend its
Developing a new transformer fault diagnosis system through evolutionary fuzzy logic
Huang, Y.C.; Huang, C.L.; Yang, H.T.
1997-04-01
To improve the diagnosis accuracy of the conventional dissolved gas analysis (DGA) approaches, this paper proposes an evolutionary programming (EP) based fuzzy system development technique to identify the incipient faults of the power transformers. Using the IEC/IEEE DGA criteria as references, a preliminary framework of the fuzzy diagnosis system is first built. Based on previous dissolved gas test records and their actual fault types, the proposed EP-based development technique is then employed to automatically modify the fuzzy if-then rules and simultaneously adjust the corresponding membership functions. In comparison to results of the conventional DGA and the artificial neural networks (ANN) classification methods, the proposed method has been verified to possess superior performance both in developing the diagnosis system and in identifying the practical transformer fault cases.
An Intuitionistic Fuzzy Logic Models for Multicriteria Decision Making Under Uncertainty
NASA Astrophysics Data System (ADS)
Jana, Biswajit; Mohanty, Sachi Nandan
2016-06-01
The purpose of this paper is to enhance the applicability of the fuzzy sets for developing mathematical models for decision making under uncertainty, In general a decision making process consist of four stages, namely collection of information from various sources, compile the information, execute the information and finally take the decision/action. Only fuzzy sets theory is capable to quantifying the linguistic expression to mathematical form in complex situation. Intuitionistic fuzzy set (IFSs) which reflects the fact that the degree of non membership is not always equal to one minus degree of membership. There may be some degree of hesitation. Thus, there are some situations where IFS theory provides a more meaningful and applicable to cope with imprecise information present for solving multiple criteria decision making problem. This paper emphasis on IFSs, which is help for solving real world problem in uncertainty situation.
Adaptive Fuzzy Tracking Control for a Class of MIMO Nonlinear Systems in Nonstrict-Feedback Form.
Chen, Bing; Lin, Chong; Liu, Xiaoping; Liu, Kefu
2015-12-01
This paper focuses on the problem of fuzzy adaptive control for a class of multiinput and multioutput (MIMO) nonlinear systems in nonstrict-feedback form, which contains the strict-feedback form as a special case. By the condition of variable partition, a new fuzzy adaptive backstepping is proposed for such a class of nonlinear MIMO systems. The suggested fuzzy adaptive controller guarantees that the proposed control scheme can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking errors eventually converge to a small neighborhood around the origin. The main advantage of this paper is that a control approach is systematically derived for nonlinear systems with strong interconnected terms which are the functions of all states of the whole system. Simulation results further illustrate the effectiveness of the suggested approach.
Method study on fuzzy-PID adaptive control of electric-hydraulic hitch system
NASA Astrophysics Data System (ADS)
Li, Mingsheng; Wang, Liubu; Liu, Jian; Ye, Jin
2017-03-01
In this paper, fuzzy-PID adaptive control method is applied to the control of tractor electric-hydraulic hitch system. According to the characteristics of the system, a fuzzy-PID adaptive controller is designed and the electric-hydraulic hitch system model is established. Traction control and position control performance simulation are carried out with the common PID control method. A field test rig was set up to test the electric-hydraulic hitch system. The test results showed that, after the fuzzy-PID adaptive control is adopted, when the tillage depth steps from 0.1m to 0.3m, the system transition process time is 4s, without overshoot, and when the tractive force steps from 3000N to 7000N, the system transition process time is 5s, the system overshoot is 25%.
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.
NASA Astrophysics Data System (ADS)
Aziz, Nur Liyana Afiqah Abdul; Siah Yap, Keem; Afif Bunyamin, Muhammad
2013-06-01
This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of "computing the word". The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions.
NASA Astrophysics Data System (ADS)
Fazio, Peppino; Santamaria, Amilcare Francesco; De Rango, Floriano; Tropea, Mauro; Serianni, Abdon
2016-05-01
In the last years the physical security in transportation systems is becoming a critical issue due to the high number of accidents and emergency situations. With the increasing availability of technological applications in vehicular environments researchers aimed at minimizing the probability of road accidents. In this paper, we propose a new platform able to discover dangerous driving behaviors. We based our application on the on-board diagnosis standard, able to provide all the needed information directly from the electronic vehicle control unit . We integrated the received data with a fuzzy logic approach, obtaining a description of the driver behavior. The overall system can take several initiatives (alarms, rpm corrections, etc.), in order to notify the driver bad behavior. The performance of the proposed scheme has been validated through a deep campaign of driving simulations.
Controlling Discrete Time T-S Fuzzy Chaotic Systems via Adaptive Adjustment
NASA Astrophysics Data System (ADS)
Nian, Yibei; Zheng, Yongai
In order to overcome typical drawbacks of the OGY control, i.e. the long waiting time for control to be applied and the accessible turning system parameter in advance, this paper presents a new chaos control method based on Takagi- Sugeno (T-S) fuzzy model and adaptive adjustment. This method represents a chaotic system by linear models in different state space regions based on T-S fuzzy model and then stabilize the linear models in different state space regions by the adaptive adjustment mechanism. An example for the Henon map is given to demonstrate the effectiveness of the proposed method.
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…
Farhoud, Aidin; Erfanian, Abbas
2014-05-01
In this paper, a fully automatic robust control strategy is proposed for control of paraplegic pedaling using functional electrical stimulation (FES). The method is based on higher-order sliding mode (HOSM) control and fuzzy logic control. In FES, the strength of muscle contraction can be altered either by varying the pulse width (PW) or by the pulse amplitude (PA) of the stimulation signal. The proposed control strategy regulates simultaneously both PA and PW (i.e., PA/PW modulation). A HOSM controller is designed for regulating the PW and a fuzzy logic controller for the PA. The proposed control scheme is free-model and does not require any offline training phase and subject-specific information. Simulation studies on a virtual patient and experiments on three paraplegic subjects demonstrate good tracking performance and robustness of the proposed control strategy against muscle fatigue and external disturbances during FES-induced pedaling. The results of simulation studies show that the power and cadence tracking errors are 5.4% and 4.8%, respectively. The experimental results indicate that the proposed controller can improve pedaling system efficacy and increase the endurance of FES pedaling. The average of power tracking error over three paraplegic subjects is 7.4±1.4% using PA/PW modulation, while the tracking error is 10.2±1.2% when PW modulation is used. The subjects could pedal for 15 min with about 4.1% power loss at the end of experiment using proposed control strategy, while the power loss is 14.3% using PW modulation. The controller could adjust the stimulation intensity to compensate the muscle fatigue during long period of FES pedaling.
The Use of a Predictive Habitat Model and a Fuzzy Logic Approach for Marine Management and Planning
Hattab, Tarek; Ben Rais Lasram, Frida; Albouy, Camille; Sammari, Chérif; Romdhane, Mohamed Salah; Cury, Philippe; Leprieur, Fabien; Le Loc’h, François
2013-01-01
Bottom trawl survey data are commonly used as a sampling technique to assess the spatial distribution of commercial species. However, this sampling technique does not always correctly detect a species even when it is present, and this can create significant limitations when fitting species distribution models. In this study, we aim to test the relevance of a mixed methodological approach that combines presence-only and presence-absence distribution models. We illustrate this approach using bottom trawl survey data to model the spatial distributions of 27 commercially targeted marine species. We use an environmentally- and geographically-weighted method to simulate pseudo-absence data. The species distributions are modelled using regression kriging, a technique that explicitly incorporates spatial dependence into predictions. Model outputs are then used to identify areas that met the conservation targets for the deployment of artificial anti-trawling reefs. To achieve this, we propose the use of a fuzzy logic framework that accounts for the uncertainty associated with different model predictions. For each species, the predictive accuracy of the model is classified as ‘high’. A better result is observed when a large number of occurrences are used to develop the model. The map resulting from the fuzzy overlay shows that three main areas have a high level of agreement with the conservation criteria. These results align with expert opinion, confirming the relevance of the proposed methodology in this study. PMID:24146867
Improving the performance of water balance equation using fuzzy logic approach
NASA Astrophysics Data System (ADS)
Khazaei, Bahram; Hosseini, Seyed Mahmood
2015-05-01
It is a common practice to conduct the water budget or water balance analysis in a given area within a specified time in order to investigate the balance between the inputs and outputs of the water system. Such an analysis can be used for water management and water allocation in a designated study area. Due to appearance of an error in water balance equation because of difficulty in accurate estimation of its individual components, the main objective of the current paper was to apply a set of fuzzy coefficients to the components of the water balance equation in order to reduce this error. The fuzzy coefficients reflect the uncertainty and imprecision in evaluating each component, and minimize the overall error of the water balance equation. These coefficients are adjusted by an error minimization procedure, based on fuzzy regression concepts and using available recorded data for a given study area within a specified time scale. The adjusted coefficients can effectively estimate the water balance components in the future. In this study, four different models, representing different types of fuzzy coefficients, were considered and used for annual water balance of Azghand catchment in Khorasan Razavi Province, Iran as a case study. Analysis of results showed that all models were effective in reducing water balance error in Azghand catchment. The best model reduced the error up to 79% in terms of mean absolute error compared with error in water balance equation when conventional (with no correction coefficients) water balance analysis was conducted. Moreover, the results indicated that the performance of the proposed fuzzy models was not significantly sensitive to selection of confidence level in data (h) and improved slightly as h increased.
Li, Tzuu-Hseng S; Su, Yu-Te; Lai, Shao-Wei; Hu, Jhen-Jia
2011-06-01
This paper proposes the implementation of fuzzy motion control based on reinforcement learning (RL) and Lagrange polynomial interpolation (LPI) for gait synthesis of biped robots. First, the procedure of a walking gait is redefined into three states, and the parameters of this designed walking gait are determined. Then, the machine learning approach applied to adjusting the walking parameters is policy gradient RL (PGRL), which can execute real-time performance and directly modify the policy without calculating the dynamic function. Given a parameterized walking motion designed for biped robots, the PGRL algorithm automatically searches the set of possible parameters and finds the fastest possible walking motion. The reward function mainly considered is first the walking speed, which can be estimated from the vision system. However, the experiment illustrates that there are some stability problems in this kind of learning process. To solve these problems, the desired zero moment point trajectory is added to the reward function. The results show that the robot not only has more stable walking but also increases its walking speed after learning. This is more effective and attractive than manual trial-and-error tuning. LPI, moreover, is employed to transform the existing motions to the motion which has a revised angle determined by the fuzzy motion controller. Then, the biped robot can continuously walk in any desired direction through this fuzzy motion control. Finally, the fuzzy-based gait synthesis control is demonstrated by tasks and point- and line-target tracking. The experiments show the feasibility and effectiveness of gait learning with PGRL and the practicability of the proposed fuzzy motion control scheme.
Adaptive fuzzy control of underactuated robotic systems with the use of differential flatness theory
NASA Astrophysics Data System (ADS)
Rigatos, Gerasimos G.
2013-10-01
An adaptive fuzzy controller is designed for a class of underactuated nonlinear robotic manipulators, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the robotic system into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H∞ tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked in the case of a 2-DOF planar robotic manipulator that has the structure of a closed-chain mechanism.
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.
MR images restoration with the use of fuzzy filter having adaptive membership parameters.
Güler, I; Toprak, A; Demirhan, A; Karakiş, R
2008-06-01
A new fuzzy adaptive median filter is presented for the noise reduction of magnetic resonance images corrupted with heavy impulse (salt and pepper) noise. In this paper, we have proposed a Fuzzy Adaptive Median Filter with Adaptive Membership Parameters (FAMFAMP) for removing highly corrupted salt and pepper noise, with preserving image edges and details. The FAMFAMP filter is an improved version of Adaptive Median Filter (AMF) and is presented in the aim of noise reduction of images corrupted with additive impulse noise. The proposed filter can preserve image details better than AMF while suppressing additive salt and pepper or impulse type noise. In this paper, we placed our preference on bell-shaped membership function with adaptive parameters instead of triangular membership function without variable coefficients in order to observe better results.
NASA Astrophysics Data System (ADS)
Chai, Runqi; Savvaris, Al; Tsourdos, Antonios
2016-06-01
In this paper, a fuzzy physical programming (FPP) method has been introduced for solving multi-objective Space Manoeuvre Vehicles (SMV) skip trajectory optimization problem based on hp-adaptive pseudospectral methods. The dynamic model of SMV is elaborated and then, by employing hp-adaptive pseudospectral methods, the problem has been transformed to nonlinear programming (NLP) problem. According to the mission requirements, the solutions were calculated for each single-objective scenario. To get a compromised solution for each target, the fuzzy physical programming (FPP) model is proposed. The preference function is established with considering the fuzzy factor of the system such that a proper compromised trajectory can be acquired. In addition, the NSGA-II is tested to obtain the Pareto-optimal solution set and verify the Pareto optimality of the FPP solution. Simulation results indicate that the proposed method is effective and feasible in terms of dealing with the multi-objective skip trajectory optimization for the SMV.
Design of sewage treatment system by applying fuzzy adaptive PID controller
NASA Astrophysics Data System (ADS)
Jin, Liang-Ping; Li, Hong-Chan
2013-03-01
In the sewage treatment system, the dissolved oxygen concentration control, due to its nonlinear, time-varying, large time delay and uncertainty, is difficult to establish the exact mathematical model. While the conventional PID controller only works with good linear not far from its operating point, it is difficult to realize the system control when the operating point far off. In order to solve the above problems, the paper proposed a method which combine fuzzy control with PID methods and designed a fuzzy adaptive PID controller based on S7-300 PLC .It employs fuzzy inference method to achieve the online tuning for PID parameters. The control algorithm by simulation and practical application show that the system has stronger robustness and better adaptability.
Lin, Geng-Cheng; Wang, Chuin-Mu; Wang, Wen-June; Sun, Sheng-Yih
2010-06-01
Constrained energy minimization (CEM) has proven highly effective for hyperspectral (or multispectral) target detection and classification. It requires a complete knowledge of the desired target signature in images. This work presents "Unsupervised CEM (UCEM)," a novel approach to automatically target detection and classification in multispectral magnetic resonance (MR) images. The UCEM involves two processes, namely, target generation process (TGP) and CEM. The TGP is a fuzzy-set process that generates a set of potential targets from unknown information and then applies these targets to be desired targets in CEM. Finally, two sets of images, namely, computer-generated phantom images and real MR images, are used in the experiments to evaluate the effectiveness of UCEM. Experimental results demonstrate that UCEM segments a multispectral MR image much more effectively than either Functional MRI of the Brain's (FMRIB's) automated segmentation tool or fuzzy C-means does.
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.
Djukanovic, M.B.; Calovic, M.S.; Vesovic, B.V.; Sobajic, D.J.
1997-12-01
This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.