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Sample records for adaptive fuzzy logic

  1. Adaptive Background subtraction in Dynamic Environments Using Fuzzy Logic

    Sivabalakrishnan.M

    2010-03-01

    Full Text Available Extracting a background from an image is the enabling step for many high-level vision processing tasks, such as object tracking andactivity analysis. Although there are a number of object extraction algorithms proposed in the literature, most approaches work efficiently only in constrained environments where the background isrelatively simple and static. We extracted features from image regions, accumulated the feature information over time, fused high-level knowledge with low-level features, and built a time-varyingbackground model. A problem with our system is that by adapting the background model, objects moved are difficult to handle. In order to reinsert them into the background, we run the risk of cutting off part of the object. In this paper, we develop a fuzzy logic inference system to detach the moving object from the background. Our experimental results demonstrate that the fuzzy inference system is very efficient and robust.

  2. Adaptive process control using fuzzy logic and genetic algorithms

    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.

  3. Adaptive Process Control with Fuzzy Logic and Genetic Algorithms

    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.

  4. 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

  5. Particle Swarm Optimization Based Adaptive Strategy for Tuning of Fuzzy Logic Controller

    Sree Bash Chandra Debnath; Pintu Chandra Shill; Kazuyuki Murase

    2013-01-01

    This paper presents a new method for learning and tuning a fuzzy logic controller automatically by means of a particle swarm optimization (PSO). The proposed self-learning fuzzy logic control that uses the PSO with adaptive abilities can learn the fuzzy conclusion tables, their corresponding membership functions and fitness value where the optimization only considers certain points of the membership functions. To exhibit the effectiveness of proposed algorithm, it is used to optim...

  6. Particle Swarm Optimization Based Adaptive Strategy for Tuning of Fuzzy Logic Controller

    Sree Bash Chandra Debnath

    2013-01-01

    Full Text Available This paper presents a new method for learning and tuning a fuzzy logic controller automatically by means of a particle swarm optimization (PSO. The proposed self-learning fuzzy logic control that uses the PSO with adaptive abilities can learn the fuzzy conclusion tables, their corresponding membership functions and fitness value where the optimization only considers certain points of the membership functions. To exhibit the effectiveness of proposed algorithm, it is used to optimize the Gaussian membership functions of the fuzzy model of a nonlinear problem. Moreover, in order to design an effective adaptive fuzzy logic controller, an on line adaptive PSO based mechanism is presented to determine the parameters of the fuzzy mechanisms. Simulation results on two nonlinear problems are derived to demonstrate the powerful PSO learning algorithm and the proposed method is able to find good controllers better than neural controller and conventional controller for the target problem, cart pole type inverted pendulum system.

  7. The Adaptive Control of Nonlinear Systems Using the T-S-K Fuzzy Logic

    Martin Kratmüller

    2009-07-01

    Full Text Available Fuzzy adaptive tracking controllers for a class of uncertain nonlinear dynamicalsystems are proposed and analyzed. The controller consists of adaptive and robustifyingcomponents whose role is to nullify the effect of uncertainties and achieve a desiredtracking performance. The interactions between the two components have beeninvestigated. We use the Takagi-Sugeno-Kang type of the fuzzy logic system to approximatethe controller. It is proved that the closed-loop system using this adaptive fuzzy controlleris globally stable in the sense that all signals involved are bounded. Finally, we apply themethod of direct adaptive fuzzy controllers to control an inverted pendulum and thesimulation results are included.

  8. Uncovering transcriptional interactions via an adaptive fuzzy logic approach

    Chen Chung-Ming

    2009-12-01

    Full Text Available Abstract Background To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM performed poorly in inferring transcriptional interactions (TIs, which represent physical interactions between transcription factors (TF and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy. Results AdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF, and using 19 transcription factors (TFs, we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms. Conclusion AdaFuzzy successfully integrates multiple types of

  9. Particle Swarm Optimization Based Adaptive Strategy for Tuning of Fuzzy Logic Controller

    Sree Bash Chandra Debnath

    2013-02-01

    Full Text Available This paper presents a new method for learning and tuning a fuzzy logic controller automatically by meansof a particle swarm optimization (PSO. The proposed self-learning fuzzy logic control that uses the PSOwith adaptive abilities can learn the fuzzy conclusion tables, their corresponding membership functions andfitness value where the optimization only considers certain points of the membership functions. To exhibitthe effectiveness of proposed algorithm, it is used to optimize the Gaussian membership functions of thefuzzy model of a nonlinear problem. Moreover, in order to design an effective adaptive fuzzy logiccontroller, an on line adaptive PSO based mechanism is presented to determine the parameters of the fuzzymechanisms. Simulation results on two nonlinear problems are derived to demonstrate the powerful PSOlearning algorithm and the proposed method is able to find good controllers better than neural controllerand conventional controller for the target problem, cart pole type inverted pendulum system.

  10. Logical Fuzzy Optimization

    Saad, Emad

    2013-01-01

    We present a logical framework to represent and reason about fuzzy optimization problems based on fuzzy answer set optimization programming. This is accomplished by allowing fuzzy optimization aggregates, e.g., minimum and maximum in the language of fuzzy answer set optimization programming to allow minimization or maximization of some desired criteria under fuzzy environments. We show the application of the proposed logical fuzzy optimization framework under the fuzzy answer set optimization...

  11. Metamathematics of fuzzy logic

    Hájek, Petr

    1998-01-01

    This book presents a systematic treatment of deductive aspects and structures of fuzzy logic understood as many valued logic sui generis. Some important systems of real-valued propositional and predicate calculus are defined and investigated. The aim is to show that fuzzy logic as a logic of imprecise (vague) propositions does have well-developed formal foundations and that most things usually named `fuzzy inference' can be naturally understood as logical deduction.

  12. Obstacle avoidance for kinematically redundant robots using an adaptive fuzzy logic algorithm

    In this paper the Adaptive Fuzzy Logic approach for solving the inverse kinematics of redundant robots in an environment with obstacles is presented. The obstacles are modeled as convex bodies. A fuzzy rule base that is updated via an adaptive law is used to solve the inverse kinematic problem. Additional rules have been introduced to take care of the obstacles avoidance problem. The proposed method has advantages such as high accuracy, simplicity of computations and generality for all redundant robots. Simulation results illustrate much better tracking performance than the dynamic base solution for a given trajectory in cartesian space, while guaranteeing a collision-free trajectory and observation of a mechanical joint limit

  13. Fuzzy Logic Engine

    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.

  14. Adaptive Fuzzy Logic Control of Wind Turbine Emulator

    Bouzid, Mohamed Amine,; ZINE Souhila; ALLAOUI Tayeb

    2014-01-01

    In this paper, a Wind Turbine Emulator (WTE) based on a separately excited direct current (DC) motor is studied. The wind turbine was emulated by controlling the torque of the DC motor. The WTE is used as a prime mover for Permanent Magnet Synchronous Machine (PMSM). In order to extract maximum power from the wind, PI and Fuzzy controllers were tested. Simulation results are given to show performance of proposed fuzzy control system in maximum power points tracking in a wind energy conversion...

  15. Adaptive Fuzzy Logic Controllers for DC Drives: A Survey of the State of the art

    E. E. El-kholy; A. M. Dabroom; Adel E. El-kholy

    2006-01-01

    Fuzzy Logic Control (FLC) has gained a great demand in process control applications. Fuzzy Logic (FL) technology enables the use of engineering experience and experimental results in designing an expert system capable of handling uncertain or fuzzy quantities. This paper presents a comprehensive review of FLC in the field of Direct Current (DC) motor drive systems. Firstly, the principles of fuzzy logic theory will be briefly presented. Secondly, the employment of the FL techniques in a contr...

  16. Adaptive Fuzzy Logic Control of Wind Turbine Emulator

    BOUZID Mohamed Amine

    2014-03-01

    Full Text Available In this paper, a Wind Turbine Emulator (WTE based on a separately excited direct current (DC motor is studied. The wind turbine was emulated by controlling the torque of the DC motor. The WTE is used as a prime mover for Permanent Magnet Synchronous Machine (PMSM. In order to extract maximum power from the wind, PI and Fuzzy controllers were tested. Simulation results are given to show performance of proposed fuzzy control system in maximum power points tracking in a wind energy conversion system under various wind conditions. The strategy control was implemented in simulation using MATLAB/Simulink.

  17. Tutorial On Fuzzy Logic

    Jantzen, Jan

    1998-01-01

    A logic based on the two truth values True and False is sometimes inadequate when describing human reasoning. Fuzzy logic uses the whole interval between 0 (False) and 1 (True) to describe human reasoning. As a result, fuzzy logic is being applied in rule based automatic controllers, and this paper...

  18. Flight test results of the fuzzy logic adaptive controller-helicopter (FLAC-H)

    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.

  19. Fuzzy logic in management

    Carlsson, Christer; Fullér, Robert

    2004-01-01

    Fuzzy Logic in Management demonstrates that difficult problems and changes in the management environment can be more easily handled by bringing fuzzy logic into the practice of management. This explicit theme is developed through the book as follows: Chapter 1, "Management and Intelligent Support Technologies", is a short survey of management leadership and what can be gained from support technologies. Chapter 2, "Fuzzy Sets and Fuzzy Logic", provides a short introduction to fuzzy sets, fuzzy relations, the extension principle, fuzzy implications and linguistic variables. Chapter 3, "Group Decision Support Systems", deals with group decision making, and discusses methods for supporting the consensus reaching processes. Chapter 4, "Fuzzy Real Options for Strategic Planning", summarizes research where the fuzzy real options theory was implemented as a series of models. These models were thoroughly tested on a number of real life investments, and validated in 2001. Chapter 5, "Soft Computing Methods for Reducing...

  20. Fuzzy Logic Based Multi User Adaptive Test System

    2014-01-01

    The present proliferation of e-learning has been actively underway for the last 10 years. Current research in Adaptive Testing System focuses on the development of psychometric models with items selection strategies applicable to adaptive testing processes. The key aspect of proposed Adaptive Testing System is to develop an increasingly sophisticated latent trait model which can assist users in developing and enhancing their skills. Computerized Adaptive Test (CAT) System requires a lot of in...

  1. Mathematical Fuzzy Logic

    Cintula, Petr; Běhounek, Libor

    Rio de Janeiro : ECEME - Escola de Comando e Estado -Maior do Exército, 2013 - (Béziau, J.; Buchsbaum, A.; Costa-Leite, A.; Altair, A.). s. 43-45 [UniLog 2013. World Congress and School on Universal Logic /4./. 29.03.2013-07.04.2013, Rio de Janeiro] Institutional support: RVO:67985807 Keywords : fuzzy logic * substructural logics * metamathematics * fuzzy logika * substrukturální logiky * metamatematika Subject RIV: BA - General Mathematics

  2. Prototype of an adaptive disruption predictor for JET based on fuzzy logic and regression trees

    Disruptions remain one of the most hazardous events in the operation of a tokamak device, since they can cause damage to the vacuum vessel and surrounding structures. Their potential danger increases with the plasma volume and energy content and therefore they will constitute an even more serious issue for the next generation of machines. For these reasons, in the recent years a lot of attention has been devoted to devise predictors, capable of foreseeing the imminence of a disruption sufficiently in advance, to allow time for undertaking remedial actions. In this paper, the results of applying fuzzy logic and classification and regression trees (CART) to the problem of predicting disruptions at JET are reported. The conceptual tools of fuzzy logic, in addition to being well suited to accommodate the opinion of experts even if not formulated in mathematical but linguistic terms, are also fully transparent, since their governing rules are human defined. They can therefore help not only in forecasting disruptions but also in studying their behaviour. The analysis leading to the rules of the fuzzy predictor has been complemented with a systematic investigation of the correlation between the various experimental signals and the imminence of a disruption. This has been performed with an exhaustive, non-linear and unbiased method based on decision trees. This investigation has confirmed that the relative importance of various signals can change significantly depending on the plasma conditions. On the basis of the results provided by CART on the information content of the various quantities, the prototype of an adaptive fuzzy logic predictor was trained and tested on JET database. Its performance is significantly better than the previous static one, proving that more flexible prediction strategies, not uniform over the whole discharge but tuned to the operational region of the plasma at any given time, can be very competitive and should be investigated systematically

  3. Fuzzy Description Logic Programs

    Straccia, Umberto

    2005-01-01

    emph{Description Logic Programs} (DLPs), which combine the expressive power of classical description logics and logic programs, are emerging as an important ontology description language paradigm. In this work, we present fuzzy DLPs, which extend DLPs by allowing the representation of vague/imprecise information.

  4. Fuzzy Logic Control of Adaptive ARQ for Video Distribution over a Bluetooth Wireless Link

    R. Razavi

    2007-01-01

    Full Text Available Bluetooth's default automatic repeat request (ARQ scheme is not suited to video distribution resulting in missed display and decoded deadlines. Adaptive ARQ with active discard of expired packets from the send buffer is an alternative approach. However, even with the addition of cross-layer adaptation to picture-type packet importance, ARQ is not ideal in conditions of a deteriorating RF channel. The paper presents fuzzy logic control of ARQ, based on send buffer fullness and the head-of-line packet's deadline. The advantage of the fuzzy logic approach, which also scales its output according to picture type importance, is that the impact of delay can be directly introduced to the model, causing retransmissions to be reduced compared to all other schemes. The scheme considers both the delay constraints of the video stream and at the same time avoids send buffer overflow. Tests explore a variety of Bluetooth send buffer sizes and channel conditions. For adverse channel conditions and buffer size, the tests show an improvement of at least 4 dB in video quality compared to nonfuzzy schemes. The scheme can be applied to any codec with I-, P-, and (possibly B-slices by inspection of packet headers without the need for encoder intervention.

  5. A Novel Fuzzy Logic Based Adaptive Super-Twisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems

    Abdul Kareem

    2012-07-01

    Full Text Available This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness of the proposed controller over the first order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based simulations performed on a DC-DC Buck converter. Based on this comparison, the proposed controller is shown to obtain the desired transient response without causing chattering and error under steady-state conditions. The proposed controller is able to give robust performance in terms of rejection to input voltage variations and load variations.

  6. Fuzzy Logics Interpreted as Logics of Resources

    Běhounek, Libor

    Prague : Filosofia, 2008. s. 10-11. [ Logica 2008. 16.06.2008-20.06.2008, Hejnice] Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy logic * linear logic * logic of resources Subject RIV: BA - General Mathematics

  7. Fuzzy-Logic Adaptive Queuing for a Heuristic TCP Performance in Mobile Wireless Networks

    Ghaida A. AL-Suhail

    2012-06-01

    Full Text Available In this paper, we propose a new Fuzzy-Logic Adaptive Queuing controller (FLAQ based on a classical Random Early Detection (RED algorithm in wireless cellular network. The controller predicts dynamically the packet dropping rate and the corresponding average queue length. It relies on the average queue length at the base station router and the packet loss rate caused by the channel variations in mobile environment; assuming there is no buffer overflow due to the congestion. Using this model, a heuristic TCP performance can be estimated over a time-varying channel under different conditions of user’s mobility. The results show a significant improvement in TCP throughput performance when the user’s mobility is below 5 m/s; and becomes constant (i.e., close to i.i.d beyond this speed especially at 5% of predefined packet error rate.

  8. Fuzzy logic based Adaptive Modulation Using Non Data Aided SNR Estimation for OFDM system

    K.SESHADRI SASTRY

    2010-06-01

    Full Text Available As demand for high quality transmission increases increase of spectrum efficiency and an improvement of error performance in wireless communication systems are important . One of the promising approaches to 4G is adaptive OFDM (AOFDM . Fixed modulation systems uses only one type of modulation scheme (or order, so that either performance or capacity should be compromised Adaptive modulated systems are superior to fixed modulated systems, since they change modulation order depending on present SNR. In an adaptive modulation system SNR estimation is important since performance of adaptive modulated system depends of estimated SNR. Non-data-Aided (NDA SNR estimation systems are gaining importance in recent days since they estimate SNR range and requires less data as input .In this paper we propose an adaptive modulated OFDM system which uses NDA(Non-data Aided SNR estimation using fuzzy logic interface.The proposed system is simulated in Matlab 7.4 and The results of computer simulation show the improvement in system capacity .

  9. Introduction to Mathematical Fuzzy Logic. Chapter 1

    Běhounek, L. (Libor); Cintula, P. (Petr); P. Hájek

    2011-01-01

    The chapter provides a comprehensive introduction to the area of mathematical fuzzy logic. Starting from the syntax and semantics of t-norm fuzzy logics, it systematically surveys the systems of propositional fuzzy logics known from the literature, their general and particular metamathematical properties, predicate variants of fuzzy logics, and axiomatic fuzzy mathematics.

  10. A Gentle Introduction to Mathematical Fuzzy Logic

    Cintula, Petr; Noguera, Carles

    University of Tübingen, 2014. s. 21-21. [ESSLI 2014. European Summer School in Logic, Language and Information /26./. 11.08.2014-22.08.2014, Tübingen] Institutional support: RVO:67985807 Keywords : mathematical fuzzy logic * propositional fuzzy logic * predicate fuzzy logic * metamathematics of fuzzy logic Subject RIV: BA - General Mathematics

  11. Fuzzy Logics Interpreted as Logics of Resources

    Běhounek, Libor

    London : College Publications, 2009 - (Peliš, M.), s. 1-13 ISBN 978-1-904987-46-8. [ Logica 2008. Hejnice (CZ), 16.06.2008-20.06.2008] R&D Projects: GA AV ČR IAA900090703 Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy logic * linear logic * contraction-free substructural logics * resource-aware reasoning * prelinearity Subject RIV: BA - General Mathematics

  12. Fuzzy logic particle tracking velocimetry

    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.

  13. Fuzzy Logic Particle Tracking

    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

  14. Fuzzy logic and neural network technologies

    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.

  15. Neutrosophic Logic - Generalization of the Intuitionistic Fuzzy Logic

    Smarandache, Florentin

    2003-01-01

    One generalizes the intuitionistic fuzzy logic (IFL) and other logics to neutrosophic logic (NL). The distinctions between IFL and NL {and the corresponding intuitionistic fuzzy set (IFS) and neutrosophic set (NS) respectively} are presented.

  16. From (Deductive) Fuzzy Logic to (Logic-Based) Fuzzy Mathematics

    Cintula, Petr

    Berlin: Springer, 2009 - (Sossai, C.; Chemello, G.) ISBN 978-3-642-02905-9. ISSN 0302-9743. [ECSQARU 2009. European Conference /10./. 01.07.2009-03.07. 2009, Verona] Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy class theory * non-classical mathematics * logic-based fuzzy mathematics Subject RIV: BA - General Mathematics

  17. An Analysis of General Fuzzy Logic and Fuzzy Reasoning Method

    Il, Kwak Son

    2016-01-01

    In this article, we describe the fuzzy logic, fuzzy language and algorithms as the basis of fuzzy reasoning, one of the intelligent information processing method, and then describe the general fuzzy reasoning method.

  18. Mathematics of Fuzzy Sets and Fuzzy Logic

    Bede, Barnabas

    2013-01-01

    This book presents a mathematically-based introduction into the fascinating topic of Fuzzy Sets and Fuzzy Logic and might be used as textbook at both undergraduate and graduate levels and also as reference guide for mathematician, scientists or engineers who would like to get an insight into Fuzzy Logic.   Fuzzy Sets have been introduced by Lotfi Zadeh in 1965 and since then, they have been used in many applications. As a consequence, there is a vast literature on the practical applications of fuzzy sets, while theory has a more modest coverage. The main purpose of the present book is to reduce this gap by providing a theoretical introduction into Fuzzy Sets based on Mathematical Analysis and Approximation Theory. Well-known applications, as for example fuzzy control, are also discussed in this book and placed on new ground, a theoretical foundation. Moreover, a few advanced chapters and several new results are included. These comprise, among others, a new systematic and constructive approach for fuzzy infer...

  19. Introduction to Mathematical Fuzzy Logic. Chapter 1

    Běhounek, Libor; Cintula, Petr; Hájek, Petr

    Vol. 1. London : College Publications, 2011 - (Cintula, P.; Hájek, P.; Noguera, C.), s. 1-101 ISBN 978-1-84890-039-4. - (Studies in Logic - Mathematical Logic and Foundations . 37) R&D Projects: GA ČR GEICC/08/E018; GA ČR GAP202/10/1826 Institutional research plan: CEZ:AV0Z10300504 Keywords : mathematical fuzzy logic * propositional fuzzy logic * predicate fuzzy logic * metamathematics of fuzzy logic Subject RIV: BA - General Mathematics

  20. n-ary Fuzzy Logic and Neutrosophic Logic Operators

    Smarandache, Florentin; V. Christianto

    2008-01-01

    We extend Knuth's 16 Boolean binary logic operators to fuzzy logic and neutrosophic logic binary operators. Then we generalize them to n-ary fuzzy logic and neutrosophic logic operators using the smarandache codification of the Venn diagram and a defined vector neutrosophic law. In such way, new operators in neutrosophic logic/set/probability are built.

  1. Deductive Systems of Fuzzy Logic

    Hájek, Petr

    Vol. 1. New Delhi : Allied Publishers PVT , 2007 - (Gupta, A.; Parikh, R.; van Benthem, J.), s. 60-74 ISBN 979-81-8424-272-9 R&D Projects: GA AV ČR IAA100300503 Institutional research plan: CEZ:AV0Z10300504 Keywords : mathematical fuzzy logic * many-valued logic t-norms Subject RIV: BA - General Mathematics

  2. Structural Completeness in Fuzzy Logics

    Cintula, Petr; Metcalfe, G.

    2009-01-01

    Roč. 50, č. 2 (2009), s. 153-183. ISSN 0029-4527 R&D Projects: GA MŠk(CZ) 1M0545 Institutional research plan: CEZ:AV0Z10300504 Keywords : structral logics * fuzzy logics * structural completeness * admissible rules * primitive variety * residuated lattices Subject RIV: BA - General Mathematics

  3. Fuzzy Logic and Arithmetical Hierarchy III

    Hájek, Petr

    2001-01-01

    Roč. 68, č. 1 (2001), s. 129-142. ISSN 0039-3215 R&D Projects: GA AV ČR IAA1030004 Institutional research plan: AV0Z1030915 Keywords : fuzzy logic * basic fuzzy logic * Lukasiewicz logic * Godel logic * product logic * arithmetical hierarchy Subject RIV: BA - General Mathematics

  4. Reasoning within Fuzzy Description Logics

    Straccia, U

    2011-01-01

    Description Logics (DLs) are suitable, well-known, logics for managing structured knowledge. They allow reasoning about individuals and well defined concepts, i.e., set of individuals with common properties. The experience in using DLs in applications has shown that in many cases we would like to extend their capabilities. In particular, their use in the context of Multimedia Information Retrieval (MIR) leads to the convincement that such DLs should allow the treatment of the inherent imprecision in multimedia object content representation and retrieval. In this paper we will present a fuzzy extension of ALC, combining Zadeh's fuzzy logic with a classical DL. In particular, concepts becomes fuzzy and, thus, reasoning about imprecise concepts is supported. We will define its syntax, its semantics, describe its properties and present a constraint propagation calculus for reasoning in it.

  5. Fuzzy Logic Reliability Centered Maintenance

    Felecia .

    2014-01-01

    Full Text Available Reliability Centered Maintenence (RCM is a systematic maintenence strategy based on system reliability. Application of RCM process will not always come out with a binary output of “yes” and “no”. Most of the time they are not supported with available detail information to calculate system reliability. The fuzzy logic method attempts to eliminate the uncertainty by providing “truth” in different degrees.Data and responses from maintenance department will be processed using the two methods (reliability centered maintenance and fuzzy logic to design maintenance strategy for the company. The results of the fuzzy logic RCM application are maintenance strategy which fit with current and future condition.

  6. Robot Control by Fuzzy Logic

    Stoian, Viorel; Ivanescu, Mircea

    2008-01-01

    The section 3 presents a new control method for mobile robots moving in its work field which is based on fuzzy logic and artificial potential field. First, the artificial potential field method is presented. The section treats unconstrained movement based on attractive artificial potential field and after that discuss the constrained movement based on attractive and repulsive artificial potential field. A fuzzy controller is designed. Finally, some applications are presented. The section 4 pr...

  7. Fuzzy Logic and Neuro-fuzzy Systems: A Systematic Introduction

    Yue Wu; Biaobiao Zhang; Jiabin Lu; K.-L. Du

    2011-01-01

    Fuzzy logic is a rigorous mathematical field, and it provides an effective vehicle for modeling the uncertainty in human reasoning. In fuzzy logic, the knowledge of experts is modeled by linguistic rules represented in the form of IF-THEN logic. Like neural network models such as the multilayer perceptron (MLP) and the radial basis function network (RBFN), some fuzzy inference systems (FISs) have the capability of universal approximation. Fuzzy logic can be used in most areas where neural net...

  8. 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).

  9. From Fuzzy Logic to Fuzzy Mathematics: A Methodological Manifesto

    Běhounek, Libor; Cintula, Petr

    2006-01-01

    Roč. 157, č. 5 (2006), s. 642-646. ISSN 0165-0114 R&D Projects: GA AV ČR KJB100300502 Institutional research plan: CEZ:AV0Z10300504 Keywords : non-classical logics * formal fuzzy logic * formal fuzzy mathematics * high-order fuzzy logic Subject RIV: BA - General Mathematics Impact factor: 1.181, year: 2006

  10. Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms

    Galantucci, L. M.; Percoco, G.; Spina, R

    2004-01-01

    The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA) methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while th...

  11. The first order fuzzy predicate logic (I)

    Some analysis tools of fuzzy measures, Sugeno's integrals, etc. are introduced into the semantic of the first order predicate logic to explain the concept of fuzzy quantifiers. The truth value of a fuzzy quantification proposition is represented by Sugeno's integral. With this framework, several important notions of formation rules, fuzzy valutions and fuzzy validity are discussed

  12. MULTI-DEPOT VEHICLE ROUTING PROBLEM WITH TIME WINDOW MENGGUNAKAN ADAPTIVE GENETIC ALGORITHM DENGAN FUZZY LOGIC CONTROLLER

    Tri Kusnandi Fazarudin

    2015-12-01

    Full Text Available Multi-Depot Vehicle Routing Problem with Time Window (MDVRPTW is a problem of finding an optimal route for a supplier. The supplier needs to deliver goods to a number of customers using the vehicles located in a number of depots. Each delivery must be done within the service time specified by each customer The vehicles used have a maximum limit on the amount of goods that can be loaded and the maximum time the vehicle may be used. MDVRPTW is one of the variations of Vehicle Routing Problem (VRP. There are various algorithms that have been used to solve VRP problems. Some of them are Genetic Algorithm (GA, Tabu Search, and Adaptive GA with Artificial Bee Colony. GA can solve the problem within a shorter time, but it is vulnerable to get trapped in a local optimum. A strategy to reduce the probability of it is to make the GA adaptive. In this research, MDVRPTW is solved with GA. To reduce the probability of getting trapped in a local optimum, the GA parameters are made adaptive using Fuzzy Logic Controller (FLC. Based on the results of this research, using FLC on GA causes the average of the solution to be better than the solution produced using GA without FLC.

  13. Deductive Systems of Fuzzy Logic

    Hájek, Petr

    Dordrecht : Springer, 2011 - (van Benthem, J.; Gupta, A.; Parikh, R.), s. 67-78 ISBN 978-94-007-0079-6. - (Synthese Library. 352) R&D Projects: GA AV ČR IAA100300503 Institutional research plan: CEZ:AV0Z10300504 Keywords : Mathematical Fuzzy Logic * axiomatic systems * completeness theorems Subject RIV: BA - General Mathematics

  14. Learning fuzzy logic control system

    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

  15. Logical Characterisation of Ontology Construction using Fuzzy Description Logics

    Badie, Farshad; Götzsche, Hans

    Ontologies based on Description Logics (DLs) have proved to be effective in formally sharing knowledge across semantic technologies, e.g. Semantic Web, Natural Language Processing, Text Analytics, Business intelligence. Our main goal is analysing ontology construction considering vagueness. We have...... had the extension of ontologies with Fuzzy Logic capabilities which plan to make proper backgrounds for ontology driven reasoning and argumentation on vague and imprecise domains. This presentation conceptualises learning from fuzzy classes using the Inductive Logic Programming framework. Then......, employs Description Logics in characterising and analysing fuzzy statements. And finally, provides a conceptual framework describing fuzzy concept learning in ontologies using the Inductive Logic Programming....

  16. Fuzzy logic control of telerobot manipulators

    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.

  17. Optimization of Fuzzy Logic Controller for Supervisory Power System Stabilizers

    Y. A. Al-Turki

    2012-01-01

    Full Text Available This paper presents a powerful supervisory power system stabilizer (PSS using an adaptive fuzzy logic controller driven by an adaptive fuzzy set (AFS. The system under study consists of two synchronous generators, each fitted with a PSS, which are connected via double transmission lines. Different types of PSS-controller techniques are considered. The proposed genetic adaptive fuzzy logic controller (GAFLC-PSS, using 25 rules, is compared with a static fuzzy logic controller (SFLC driven by a fixed fuzzy set (FFS which has 49 rules. Both fuzzy logic controller (FLC algorithms utilize the speed error and its rate of change as an input vector. The adaptive FLC algorithm uses a genetic algorithmto tune the parameters of the fuzzy set of each PSS. The FLC’s are simulated and tested when the system is subjected to different disturbances under a wide range of operating points. The proposed GAFLC using AFS reduced the computational time of the FLC, where the number of rules is reduced from 49 to 25 rules. In addition, the proposed adaptive FLC driven by a genetic algorithm also reduced the complexity of the fuzzy model, while achieving a good dynamic response of the system under study.

  18. Fuzzy logic and hybrid systems

    Song, Y.H.; Dunn, R.W.

    1997-12-31

    The real world is complex, complexity in the world generally arises from uncertainty in the form of ambiguity. Electric power systems are large, complex, geographically widely distributed systems and influenced by unexpected events. These facts make it difficult to effectively deal with many power system problems through strict mathematical approaches. Therefore, intelligent techniques such as expert systems, artificial neural networks, genetic algorithms and fuzzy logic have emerged in recent years in power systems as a complement to mathematical approaches and have proved to be effective when properly coupled. As the real world power system problems may neither fit the assumptions of a single technique nor be effectively solved by the strengths and capabilities of a single technique, it is now becoming apparent that the integration of various intelligent techniques is a very important way forward in the next generation of intelligent systems. Traditional logic uses variables that have precise values, called ``crisp`` values. Fuzzy logic, on the other hand, attempts to model the impreciseness of human reasoning by representing uncertainty for the variables that are used by assignment of a ``set`` of values to the variable. Each value has a ``degree of membership`` of the set which represents the probability of the variable having that value. A ``membership function`` identifies the degree of membership over the range of possible values, known as the ``universe of discourse``. This function can be defined to represent an adjective, known as a ``linguistic value`` or ``fuzzy set``, which describes the set of values. It is this ability to handle common linguistic terminology that allows fuzzy logic to model qualitative reasoning and to be used in knowledge representation. (Author)

  19. Apple Grading Using Fuzzy Logic

    KAVDIR, İsmail

    2003-01-01

    Classification is vital for the evaluation of agricultural produce. However, the high costs, subjectivity, tediousness and inconsistency associated with manual sorting have been forcing the post harvest industry to apply automation in sorting operations. Fuzzy logic (FL) was applied as a decision making support to grade apples in this study. Quality features such as the color, size and defects of apples were measured through different equipment. The same set of apples was graded by both a hum...

  20. Fuzzy Logic Reliability Centered Maintenance

    , Felecia

    2014-01-01

    Reliability Centered Maintenence (RCM) is a systematic maintenence strategy based on system reliability. Application of RCM process will not always come out with a binary output of “yes” and “no”. Most of the time they are not supported with available detail information to calculate system reliability. The fuzzy logic method attempts to eliminate the uncertainty by providing “truth” in different degrees.Data and responses from maintenance department will be processed using the two methods (re...

  1. A fuzzy-logic based dual-purpose adaptive circuit for vibration control and energy harvesting using piezoelectric transducer

    Liu, Zhe Peng; Li, Qing

    2013-04-01

    Due to their two-way electromechanical coupling effect, piezoelectric transducers can be used to synthesize passive vibration control schemes, e.g., RLC circuit with the integration of inductance and resistance elements that is conceptually similar to damped vibration absorber. Meanwhile, the wide usage of wireless sensors has led to the recent enthusiasm of developing piezoelectric-based energy harvesting devices that can convert ambient vibratory energy into useful electrical energy. It can be shown that the integration of circuitry elements such as resistance and inductance can benefit the energy harvesting capability. Here we explore a dual-purpose circuit that can facilitate simultaneous vibration suppression and energy harvesting. It is worth noting that the goal of vibration suppression and the goal of energy harvesting may not always complement each other. That is, the maximization of vibration suppression doesn't necessarily lead to the maximization of energy harvesting, and vice versa. In this research, we develop a fuzzy-logic based algorithm to decide the proper selection of circuitry elements to balance between the two goals. As the circuitry elements can be online tuned, this research yields an adaptive circuitry concept for the effective manipulation of system energy and vibration suppression. Comprehensive analyses are carried out to demonstrate the concept and operation.

  2. Justification Logics in a Fuzzy Setting

    Ghari, Meghdad

    2014-01-01

    Justification Logics provide a framework for reasoning about justifications and evidences. Most of the accounts of justification logics are crisp in the sense that agent's justifications for a statement is convincing or is not. In this paper, we study fuzzy variants of justification logics, in which an agent can have a justification for a statement with a certainty degree between 0 and 1. We replaced the classical base of the justification logics with some known fuzzy logics: Hajek's basic lo...

  3. The Quest for the Basic Fuzzy Logic

    Cintula, Petr; Horčík, Rostislav; Noguera, Carles

    Cham: Springer, 2015 - (Montagna, F.), s. 245-290. (Outstanding Contributions to Logic. 6). ISBN 978-3-319-06232-7 R&D Projects: GA ČR GAP202/10/1826; GA ČR GA13-14654S EU Projects: European Commission(XE) 247584 - MATOMUVI Grant ostatní: MICINN project TASSAT(XE) TIN2010-20967-C04-01 Institutional support: RVO:67985807 ; RVO:67985556 Keywords : mathematical fuzzy logic * basic fuzzy logic * T-norm * core fuzzy logics * core semilinear logics * non-associative substructural logics * standard completeness Subject RIV: BA - General Mathematics

  4. Opportunities for fuzzy logic in radiation protection

    This paper points at applications of fuzzy logic currently under development at the radiation protection research unit at the nuclear research center SCK/CEN. The illustrated applications are snapshots of the wide research area of radiation protection and radiological optimization. As such, it is not the intention of this paper to give a complete overview of fuzzy logic applications in these fields, but rather to try to reveal future opportunities for further developing fuzzy logic in nuclear science

  5. Application of fuzzy logic control in industry

    An overview is given of the various ways fuzzy logic can be used to improve industrial control. The application of fuzzy logic in control is illustrated by two case studies. The first example shows how fuzzy logic, incorporated in the hardware of an industrial controller, helps to finetune a PID controller, without the operator having any a priori knowledge of the system to be controlled. The second example is from process industry. Here, fuzzy logic supervisory control is implemented in software and enhances the operation of a sintering oven through a subtle combination of priority management and deviation-controlled timing

  6. Fuzzy Logic Applications in Filtering and Fusion for Target Tracking

    Kashyap, S K; J.R. Raol

    2008-01-01

    A fuzzy Kalman filter algorithm is developed for target tracking applications and itsperformance evaluated using several numerical examples. The approach is relatively novel. Acomparison with Kalman filter and an adaptive tuning algorithm is carried out. The applicabilityand usefulness of fuzzy logic in data fusion is also demonstrated. The performance of both theextended Kalman filter and fuzzy extended Kalman filter is evaluated using real data of amanoeuvering target and it is found that f...

  7. Achieving of Fuzzy Automata for Processing Fuzzy Logic

    SHU Lan; WU Qing-e

    2005-01-01

    At present, there has been an increasing interest in neuron-fuzzy systems, the combinations of artificial neural networks with fuzzy logic. In this paper, a definition of fuzzy finite state automata (FFA) is introduced and fuzzy knowledge equivalence representations between neural networks, fuzzy systems and models of automata are discussed. Once the network has been trained, we develop a method to extract a representation of the FFA encoded in the recurrent neural network that recognizes the training rules.

  8. Fuzzy logic based robotic controller

    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.

  9. Fuzzy Logic as a Tool for Assessing Students’ Knowledge and Skills

    Michael Gr. Voskoglou

    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 model for assessing student groups’ knowledge and skills. In this model the students’ characteristics under assessment (knowledge of the subject mat...

  10. Properties of Measure-based Fuzzy Logic

    2001-01-01

    Measure-based fuzzy logic, which is constructed on the basis of eight axioms, is a seemingly powerful fuzzy logic. It possesses several remarkable properties. (1) It is an extended Boolean logic, satisfying all the properties of Boolean algebra, including the law of excluded middle and the law of contradiction. (2) It is conditional. Conditional membership functions play an important role in this logic. (3) The negation operator is not independently defined with the conjunction and disjunction operators, but on the contrary, it is derived from them. (4) Zadehs fuzzy logic is included in it as a particular case. (5) It gives more hints to the relationship between fuzzy logic and probability logic.

  11. Paraconsistent degree-preserving fuzzy logic

    Biraben, R. C. E.; Noguera, Carles

    Kolkata : Indian Statistical Institute, 2014 - (Beziau, J.; Buchsbaum, A.; Altair, A.). s. 47-48 [World Congress on Paraconsistency /5./. 13.02.2014-17.02.2014, Kolkata] Institutional support: RVO:67985556 Keywords : fuzzy logic * paraconsistent logic s * fuzzy set theory Subject RIV: BA - General Mathematics http://www.paraconsistency.org/book/Handbook-WCP5.pdf

  12. Application of Fuzzy Logic in Servo Motor

    Shereen F. Abd-Alkarim

    2007-01-01

    In this work the design and application of a fuzzy logic controller to DC-servomotor is investigated. The proposed strategy is intended to improve the performance of the original control system by use of a fuzzy logic controller (FLC) as the motor load changes. Computer simulation demonstrates that FLC is effective in position control of a DC-servomotor comparing with conventional one.

  13. Fuzzy logic applications in engineering science

    Harris, J

    2006-01-01

    Fuzzy logic has been a conceptual process applied in the field of risk management. This book is intended for professional engineers and students and those with an interest in exploring the potential of fuzzy logic as an information processing kit with a variety of practical applications in the field of engineering science.

  14. Possible use of fuzzy logic in database

    Vaclav Bezdek

    2011-04-01

    Full Text Available The article deals with fuzzy logic and its possible use in database systems. At first fuzzy thinking style is shown on a simple example. Next the advantages of the fuzzy approach to database searching are considered on the database of used cars in the Czech Republic.

  15. Fuzzy Logic Approach for Diagnosis of Diabetics

    Radha, R.; S. P. Rajagopalan

    2007-01-01

    Fuzzy logic is a computational paradigm that provides a mathematical tool for dealing with the uncertainty and the imprecision typical of human reasoning. A prime characteristic of fuzzy logic is its capability of expressing knowledge in a linguistic way, allowing a system to be described by simple, human-friendly rules. The fuzzy set framework has been utilized in several different approaches to modeling the diagnostic process. In this paper Diabetes related diseases and their symptoms are t...

  16. Fuzzy logic control of an AGV

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

    1997-09-01

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

  17. Fuzzy Logic Unmanned Air Vehicle Motion Planning

    Chelsea Sabo

    2012-01-01

    Full Text Available There are a variety of scenarios in which the mission objectives rely on an unmanned aerial vehicle (UAV being capable of maneuvering in an environment containing obstacles in which there is little prior knowledge of the surroundings. With an appropriate dynamic motion planning algorithm, UAVs would be able to maneuver in any unknown environment towards a target in real time. This paper presents a methodology for two-dimensional motion planning of a UAV using fuzzy logic. The fuzzy inference system takes information in real time about obstacles (if within the agent's sensing range and target location and outputs a change in heading angle and speed. The FL controller was validated, and Monte Carlo testing was completed to evaluate the performance. Not only was the path traversed by the UAV often the exact path computed using an optimal method, the low failure rate makes the fuzzy logic controller (FLC feasible for exploration. The FLC showed only a total of 3% failure rate, whereas an artificial potential field (APF solution, a commonly used intelligent control method, had an average of 18% failure rate. These results highlighted one of the advantages of the FLC method: its adaptability to complex scenarios while maintaining low control effort.

  18. Fuzzy Logic and Neuro-fuzzy Systems: A Systematic Introduction

    Yue Wu

    2011-05-01

    Full Text Available Fuzzy logic is a rigorous mathematical field, and it provides an effective vehicle for modeling the uncertainty in human reasoning. In fuzzy logic, the knowledge of experts is modeled by linguistic rules represented in the form of IF-THEN logic. Like neural network models such as the multilayer perceptron (MLP and the radial basis function network (RBFN, some fuzzy inference systems (FISs have the capability of universal approximation. Fuzzy logic can be used in most areas where neural networks are applicable. In this paper, we first give an introduction to fuzzy sets and logic. We then make a comparison between FISs and some neural network models. Rule extraction from trained neural networks or numerical data is then described. We finally introduce the synergy of neural and fuzzy systems, and describe some neuro-fuzzy models as well. Some circuits implementations of neuro-fuzzy systems are also introduced. Examples are given to illustrate the cocepts of neuro-fuzzy systems.

  19. Mathematical fuzzy logic: first-order and beyond

    Cintula, Petr

    Olomouc : Faculty of Science, Palacky University, 2013. [WIUI 2013. International Workshop Information, Uncertainty, and Imprecision. Olomouc (CZ), 04.06.2013-06.06.2013] Institutional support: RVO:67985807 Keywords : mathematical fuzzy logic * predicate fuzzy logic * metamathematics of fuzzy logic * higher-order fuzzy logics Subject RIV: BA - General Mathematics http://mcin.upol.cz/WIUI-2013/page/1504/

  20. Optimization of fuzzy logic controller and simulation in matlab

    This paper introduces the method of optimizing fuzzy logic controller. The shape and type of membership function are synchronously selected by gene arithmetic. The scaling factors are decided by another fuzzy logic controller. Optimized fuzzy logic controller is applied to nuclear reactor control system. The simulation results in matlab show the attributes of fuzzy logic controller get improved by optimizing. (authors)

  1. A Hierarchy of (Fuzzy) Implicational Logics

    Cintula, Petr; Noguera i Clofent, C.

    Prague : Filosofia, 2008. s. 18-20. [ Logica 2008. 16.06.2008-20.06.2008, Hejnice] Institutional research plan: CEZ:AV0Z10300504 Keywords : abstract algebraic logic * fuzzy logic * weakly implicative logics * generalized implication Subject RIV: BA - General Mathematics

  2. Refining fuzzy logic controllers with machine learning

    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.

  3. Scheduling By Using Fuzzy Logic in Manufacturing

    Miss. Ashwini. A. Mate

    2014-07-01

    Full Text Available This paper represents the scheduling process in furniture manufacturing unit. It gives the fuzzy logic application in flexible manufacturing system. Flexible manufacturing systems are production system in furniture manufacturing unit. FMS consist of same multipurpose numerically controlled machines. Here in this project the scheduling has been done in FMS by using fuzzy logic tool in Matlab software. The fuzzy logic based scheduling model in this paper will deals with the job and best alternative route selection with multi-criteria of machine. Here two criteria for job and sequencing and routing with rules. This model is applicable to the scheduling of any manufacturing industry.

  4. Fuzzy logic for business, finance, and management

    Bojadziev, George

    2007-01-01

    This is truly an interdisciplinary book for knowledge workers in business, finance, management and socio-economic sciences based on fuzzy logic. It serves as a guide to and techniques for forecasting, decision making and evaluations in an environment involving uncertainty, vagueness, impression and subjectivity. Traditional modeling techniques, contrary to fuzzy logic, do not capture the nature of complex systems especially when humans are involved. Fuzzy logic uses human experience and judgement to facilitate plausible reasoning in order to reach a conclusion. Emphasis is on applications presented in the 27 case studies including Time Forecasting for Project Management, New Product Pricing, and Control of a Parasit-Pest System.

  5. Temperature Control System Using Fuzzy Logic Technique

    Isizoh A N

    2012-06-01

    Full Text Available Fuzzy logic technique is an innovative technology used in designing solutions for multi-parameter and non-linear control models for the definition of a control strategy. As a result, it delivers solutions faster than the conventional control design techniques. This paper thus presents a fuzzy logic based-temperature control system, which consists of a microcontroller, temperature sensor, and operational amplifier, Analogue to Digital Converter, display interface circuit and output interface circuit. It contains a design approach that uses fuzzy logic technique to achieve a controlled temperature output function.

  6. Synthesis water level control by fuzzy logic

    P. Berk

    2011-04-01

    Full Text Available Purpose: This paper focuses on evolving of two types fuzzy and classical PID liquid level controller and examining whether they are better able to handle modelling uncertainties. A two stage strategy is employed to design the synthesis fuzzy and classical PID controller with the process of the first and second order and implements disorder (quadratic function.Design/methodology/approach: The synthesis of fuzzy and classical PID liquid level controller was realized with the HP laptop 6830s Compaq NA779ES, software Matlab/Simulink 2008b, FIS (Fuzzy Inference System soft logical tool, input-output unit 500 Dragon Rider and ultrasonic sensor. Using the simulation program Matlab/Simulink/FIS we simulate the operation of fuzzy and classical controller in the liquid level regulating cycle and made a comparison between fuzzy and classical controller functioning.Findings: From the responses to step fuzzy and classical controller for first-order process shows that the actual value of the controlled variable takes the value one. Fuzzy and classical PID controller does not allow control derogation, which is also inappropriate for fuzzy and classical control cycle with incorporating disturbance. Classical PID controller in the first-order process provides short-term regulation, such as fuzzy PID controller. In fuzzy control cycle with fuzzy PID controller and incorporating disturbance in the process of second-order the control cycle is stable and at certain predetermined parameters (integral gain a control does not allow deviations.Research limitations/implications: In future research, the robustness of the fuzzy logic controller will be investigated in more details.Practical implications: Using fuzzy liquid level controller can reduce power consumption by 25%. Originality/value: Fuzzy logic controller is useful in applications of nonlinear static characteristic, where classical methods with usually classical PID controllers cannot be a satisfactory outcome

  7. Fuzzy logic foundations of optimal inference

    A. Averkin

    1994-11-01

    Full Text Available In this paper we propose to solve the problem of the optimal fuzzy model designing for the dynamic systems controlling, to develop new mathematical models of fuzzy inference, logical schemes of hardware support based on these models, software support, intellectual system based on these models. The proposed schemes will be able to perform an entire inference process required for real--time fuzzy control. Each scheme works independently of the number of control rules in the knowledge base. The necessary accuracy of the output results can be provided. Among the advantages of suggested architectures are: gain in memory size, simplicity in architectural decisions, fast implementation. The proposed intellectual system gives the new approaches to fuzzy logics acquisitionin the ES and FLC, based on t-norms approach. The system is supplied by cognitive graphics interface. The main functions of the system are: visualization of fuzzy logics by multi-color tables, fuzzy logics acquisition, simulation the fuzzy reasoning processes of the system, testing of fuzzy logics.

  8. Searching the Arcane Origins of Fuzzy Logic

    Angel Garrido

    2011-01-01

    It is well-known that Artificial Intelligence requires Logic. But its Classical version shows too many insufficiencies. So, it is very necessary to introduce more sophisticated tools, as may be
    Fuzzy Logic, Modal Logic, Non-Monotonic Logic, and so on. When you are searching the possible precedent of such new ideas, we may found that they are not totally new, because some ancient thinkers have suggested many centuries ago similar concepts, certainly without adequate mathematical for...

  9. Set Theory and Arithmetic in Fuzzy Logic

    Běhounek, Libor; Haniková, Zuzana

    Cham : Springer, 2015 - (Montagna, F.), s. 63-89 ISBN 978-3-319-06232-7. - (Outstanding Contributions to Logic. 6) R&D Projects: GA ČR GPP103/10/P234; GA ČR GAP202/10/1826 Institutional support: RVO:67985807 Keywords : fuzzy set theory * fuzzy logic * naive comprehension * non-classical arithmetic Subject RIV: BA - General Mathematics

  10. Can fuzzy logic make things more clear?

    Hazelzet, Jan

    2009-01-01

    textabstractIntensive care is a complex environment involving many signals, data and observations. Clinical decision support and artificial intelligence using fuzzy logic and closed loop techniques are methods that might help us to handle this complexity in a safe, effective and efficient way. Merouani and colleagues have performed a study using fuzzy logic and closed loop techniques to more effectively wean patients with sepsis from norepinephrine infusion.

  11. Application of Fuzzy Logic in Servo Motor

    Shereen F. Abd-Alkarim

    2007-01-01

    Full Text Available In this work the design and application of a fuzzy logic controller to DC-servomotor is investigated. The proposed strategy is intended to improve the performance of the original control system by use of a fuzzy logic controller (FLC as the motor load changes. Computer simulation demonstrates that FLC is effective in position control of a DC-servomotor comparing with conventional one.

  12. Set Theory and Arithmetic in Fuzzy Logic

    Běhounek, Libor; Haniková, Zuzana

    Cham: Springer, 2015 - (Montagna, F.), s. 63-89. (Outstanding Contributions to Logic. 6). ISBN 978-3-319-06232-7 R&D Projects: GA ČR GPP103/10/P234; GA ČR GAP202/10/1826 Institutional support: RVO:67985807 Keywords : fuzzy set theory * fuzzy logic * naive comprehension * non-classical arithmetic Subject RIV: BA - General Mathematics

  13. Fuzzy Logic as an Optimization Task

    Vojtáš, Peter

    Barcelona : -, 2005 - (Montseny, E.; Sobrevilla, P.), s. 781-786 ISBN 84-7653-872-3. [EUSFLAT - LFA 2005. Conference of the European Society for Fuzzy Logic and Technology /13./, Recontres Francophones sur la Logique Floue et ses Applications /11./. Barcelona (ES), 07.09.2005-09.09.2005] R&D Projects: GA AV ČR 1ET100300419 Keywords : fuzzy logic * best answer * optimization task Subject RIV: BA - General Mathematics

  14. An Advanced Fuzzy Logic Based Traffic Controller

    Bilal Ahmed Khan; Nai Shyan Lai

    2014-01-01

    Traffic light plays an important role in the urban traffic management. Therefore, it is necessary to improve the traffic controller for effective traffic management and better traffic flow leading to greener environment. In this paper, an advanced and intelligent traffic light controller is proposed, utilising the fuzzy logic technology and image processing technique. A fuzzy logic control has been implemented to provide the attribute of intelligence to the system. For real-time image acquisi...

  15. Fuzzy logic and nuclear production process

    The application of fuzzy logic to production processes in the nuclear industry is discussed. Particular attention is paid to the application to the conditioning process of radioactive waste. It is shown that, in case of clustering problems, the results of the fuzzy approach are better than those for the crisp approach. For the discrete homogenization, further improvements to the semi-fuzzy algorithms are required

  16. Mathematical Fuzzy Logic and Axiomatic Arithmetic

    Hájek, Petr

    Linz : Johannes Kepler Universität, 2010 - (Cintula, P.; Klement, E.; Stout, L.). s. 63-63 [Linz Seminar on Fuzzy Set Theory /31./. 03.02.2010-07.02.2010, Linz] Institutional research plan: CEZ:AV0Z10300504 Keywords : mathematical fuzzy logic * axiomatic arithmetic Subject RIV: BA - General Mathematics

  17. Fuzzy logic mode switching in helicopters

    Sherman, Porter D.; Warburton, Frank W.

    1993-01-01

    The application of fuzzy logic to a wide range of control problems has been gaining momentum internationally, fueled by a concentrated Japanese effort. Advanced Research & Development within the Engineering Department at Sikorsky Aircraft undertook a fuzzy logic research effort designed to evaluate how effective fuzzy logic control might be in relation to helicopter operations. The mode switching module in the advanced flight control portion of Sikorsky's motion based simulator was identified as a good candidate problem because it was simple to understand and contained imprecise (fuzzy) decision criteria. The purpose of the switching module is to aid a helicopter pilot in entering and leaving coordinated turns while in flight. The criteria that determine the transitions between modes are imprecise and depend on the varied ranges of three flight conditions (i.e., simulated parameters): Commanded Rate, Duration, and Roll Attitude. The parameters were given fuzzy ranges and used as input variables to a fuzzy rulebase containing the knowledge of mode switching. The fuzzy control program was integrated into a real time interactive helicopter simulation tool. Optimization of the heading hold and turn coordination was accomplished by interactive pilot simulation testing of the handling quality performance of the helicopter dynamic model. The fuzzy logic code satisfied all the requirements of this candidate control problem.

  18. Towards the future of fuzzy logic

    Trillas, Enric; Kacprzyk, Janusz

    2015-01-01

    This book provides readers with a snapshot of the state-of-the art in fuzzy logic. Throughout the chapters, key theories developed in the last fifty years as well as important applications to practical problems are presented and discussed from different perspectives, as the authors hail from different disciplines and therefore use fuzzy logic for different purposes.  The book aims at showing how fuzzy logic has evolved since the first theory formulation by Lotfi A. Zadeh in his seminal paper on Fuzzy Sets in 1965. Fuzzy theories and implementation grew at an impressive speed and achieved significant results, especially on the applicative side. The study of fuzzy logic and its practice spread all over the world, from Europe to Asia, America and Oceania. The editors believe that, thanks to the drive of young researchers, fuzzy logic will be able to face the challenging goals posed by computing with words. New frontiers of knowledge are waiting to be explored. In order to motivate young people to engage in the ...

  19. What does Mathematical Fuzzy Logic Offer to Description Logic?

    Hájek, Petr

    Amsterdam : Elsevier, 2006 - (Sanchez, E.), s. 91-100 ISBN 0-444-51948-3. - (Capturing Intelligence) R&D Projects: GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : mathematical fuzzy logic * description logic Subject RIV: BA - General Mathematics

  20. Reasoning with Very Expressive Fuzzy Description Logics

    Horrocks, I; Stamou, G; Stoilos, G; Tzouvaras, V; 10.1613/jair.2279

    2011-01-01

    It is widely recognized today that the management of imprecision and vagueness will yield more intelligent and realistic knowledge-based applications. Description Logics (DLs) are a family of knowledge representation languages that have gained considerable attention the last decade, mainly due to their decidability and the existence of empirically high performance of reasoning algorithms. In this paper, we extend the well known fuzzy ALC DL to the fuzzy SHIN DL, which extends the fuzzy ALC DL with transitive role axioms (S), inverse roles (I), role hierarchies (H) and number restrictions (N). We illustrate why transitive role axioms are difficult to handle in the presence of fuzzy interpretations and how to handle them properly. Then we extend these results by adding role hierarchies and finally number restrictions. The main contributions of the paper are the decidability proof of the fuzzy DL languages fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base satisfiability problem of th...

  1. Fuzzy Hypotheses Testing in the Framework of Fuzzy Logic

    Holeňa, Martin

    2004-01-01

    Roč. 145, - (2004), s. 229-252. ISSN 0165-0114 R&D Projects: GA AV ČR IAA1030004; GA MŠk OC 274.001 Grant ostatní: COST(XE) Action 274 TARSKI Institutional research plan: CEZ:AV0Z1030915 Keywords : non-classical logics * fuzzy predicate calculus * basic fuzzy logic * generalized quantifiers * fuzzy statistics and data analysis * vague hypotheses * vague significance level * method Guha Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.734, year: 2004

  2. Fuzzy Description Logics from a Mathematical Fuzzy Logic point of view

    Cerami, Marco

    2012-01-01

    [eng]Description Logic is a formalism that is widely used in the framework of Knowledge Representation and Reasoning in Artificial Intelligence. They are based on Classical Logic in order to guarantee the correctness of the inferences on the required reasoning tasks. It is indeed a fragment of First Order Predicate Logic whose language is strictly related to the one of Modal Logic. Fuzzy Description Logic is the generalization of the classical Description Logic framework thought for reasoning...

  3. A Fuzzy Logic Based Sentiment Classification

    J.I.Sheeba

    2014-07-01

    Full Text Available Sentiment classification aims to detect information such as opinions, explicit , implicit feelings expressed in text. The most existing approaches are able to detect either explicit expressions or implicit expressions of sentiments in the text separately. In this proposed framework it will detect both Implicit and Explicit expressions available in the meeting transcripts. It will classify the Positive, Negative, Neutral words and also identify the topic of the particular meeting transcripts by using fuzzy logic. This paper aims to add some additional features for improving the classification method. The quality of the sentiment classification is improved using proposed fuzzy logic framework .In this fuzzy logic it includes the features like Fuzzy rules and Fuzzy C-means algorithm.The quality of the output is evaluated using the parameters such as precision, recall, f-measure. Here Fuzzy C-means Clustering technique measured in terms of Purity and Entropy. The data set was validated using 10-fold cross validation method and observed 95% confidence interval between the accuracy values .Finally, the proposed fuzzy logic method produced more than 85 % accurate results and error rate is very less compared to existing sentiment classification techniques.

  4. Fuzzy logic control for camera tracking system

    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.

  5. Application of fuzzy logic in computer-aided design of digital systems

    Shragowitz, Eugene B.; Lee, Jun-Yong; Kang, Eric Q.

    1996-06-01

    Application of fuzzy logic structures in computer-aided design (CAD) of electronic systems substantially improves quality of design solutions by providing designers with flexibility in formulating goals and selecting trade-offs. In addition, the following aspects of a design process are positively impacted by application of fuzzy logic: utilization of domain knowledge, interpretation of uncertainties in design data, and adaptation of design algorithms. We successfully applied fuzzy logic structures in conjunction with constructive and iterative algorithms for selecting of design solutions for different stages of the design process. We also introduced a fuzzy logic software development tool to be used in CAD applications.

  6. Which Fuzzy Logic Satisfy the Compactness Problem?

    Cintula, Petr; Navara, M.

    Savoie: ESIA, 2002, s. 405-409. ISBN 2-9516453-1-7. [IPMU '2002 /9./. Annecy (FR), 01.07.2002-05.07.2002] R&D Projects: GA AV ČR IAA1030004; GA ČR GA201/02/1540 Institutional research plan: AV0Z1030915 Keywords : many-valued logic * fuzzy logic * triangular norm * satisfiability * compactness of a logic Subject RIV: BA - General Mathematics

  7. A Brief History of Fuzzy Logic

    Angel Garrido

    2012-04-01

    Full Text Available

    The problems of uncertainty, imprecision and vagueness have been discussed for many years. These problems have been major topics in philosophical circles with much debate, in particular, about the nature of vagueness and the ability of traditional Boolean logic to cope with concepts and perceptions that are imprecise or vague. The Fuzzy Logic (which is usually translated into Castilian by “Lógica Borrosa”, or “Lógica Difusa”, but also by “Lógica Heurística” can be considered a bypass-valued logics (Multi-valued Logic, MVL, its acronym in English. It is founded on, and is closely related to-Fuzzy Sets Theory, and successfully applied on Fuzzy Systems. You might think that fuzzy logic is quite recent and what has worked for a short time, but its origins date back at least to the Greek philosophers and especially Plato (428-347 B.C.. It even seems plausible
    to trace their origins in China and India. Because it seems that they were the first to consider that all things need not be of a certain type or quit, but there are a stopover between. That is, be the pioneers in considering that there may be varying degrees of truth and falsehood. In case of colors, for example, between white and black there is a whole infinite scale: the shades of gray. Some recent theorems show that in principle fuzzy logic can be used to model any continuous system, be it based
    in AI, or physics, or biology, or economics, etc. Investigators in many fields may find that fuzzy, commonsense models are more useful, and many more accurate than are standard mathematical ones. We analyze here the history and development of this problem: Fuzziness, or “Borrosidad” (in Castilian, essential to work with Uncertainty.

  8. Relations in Higher-Order Fuzzy Logic I,II

    Běhounek, Libor; Cintula, Petr

    Linz : Johannes Kepler Universität, 2005 - (Gottwald, S.; Hájek, P.; Höhle, U.; Klement, E.). s. 10-15 [Linz Seminar on Fuzzy Set Theory /26./. 01.02.2005-05.02.2005, Linz] R&D Projects: GA MŠk OC 274.001; GA AV ČR KJB100300502 Grant ostatní: COST(XE) Action 274 TARSKI Institutional research plan: CEZ:AV0Z10300504 Keywords : formal fuzzy logic * fuzzy set * foundations of fuzzy mathematics * LPi logic * higher-order fuzzy logic * fuzzy type theory * multi-sorted fuzzy logic Subject RIV: BA - General Mathematics

  9. Fault Diagnosis in Deaerator Using Fuzzy Logic

    S Srinivasan

    2007-01-01

    Full Text Available In this paper a fuzzy logic based fault diagnosis system for a deaerator in a power plant unit is presented. The system parameters are obtained using the linearised state space deaerator model. The fuzzy inference system is created and rule base are evaluated relating the parameters to the type and severity of the faults. These rules are fired for specific changes in system parameters and the faults are diagnosed.

  10. Fuzzy Logic and Quantum Measurement Formulation

    Abbasvandi, Niloofar; Soleimani, M. J.; Radiman, Shahidan

    2013-01-01

    The Von Neumann quantum measurement theory and Zurek reformulation are based on an assumption that the quantum system, apparatus and environment obey the quantum mechanics rules. According to the Zurek theory the observers typically interact with their surrounding environments. In this article, we give a more realistic image of the quantum measurement theory; we have proposed a correction to Zurek quantum measurement theory based on the fuzzy logic and fuzzy set theory.

  11. Edge Detection Using Fuzzy Logic

    B. Govinda Lakshmi

    2014-09-01

    Full Text Available Edge detection is still difficult task in the image processing field. In this paper we implemented fuzzy techniques for detecting edges in the image. This algorithm also works for medical images. In this paper we also explained about Fuzzy inference system, which is more robust to contrast and lighting variations.

  12. On the relationship between fuzzy logic and four-valued relevance logic

    Straccia, Umberto

    2000-01-01

    In fuzzy propositional logic, to a proposition a partial truth in [0,1] is assigned. It is well known that under certain circumstances, fuzzy logic collapses to classical logic. In this paper, we will show that under dual conditions, fuzzy logic collapses to four-valued (relevance) logic, where propositions have truth-value true, false, unknown, or contradiction. As a consequence, fuzzy entailment may be considered as ``in between'' four-valued (relevance) entailment and classical entailment.

  13. A New Fuzzy Inference Technique for Singleton Type-2 Fuzzy Logic Systems

    Hwan-Joo Kwak; Dong-Won Kim; Gwi-Tae Park

    2012-01-01

    A new fuzzy inference technique is presented to replace the conventional fuzzy inference process of type‐2 fuzzy logic systems. Because conventional type‐2 fuzzy logic systems demand a large amount of memory, they cannot be used by most embedded systems, which do not have enough memory space. To overcome this problem, a new fuzzy inference technique for singleton type‐2 fuzzy logic systems is presented in this paper which designs mapping functions from input variables to firing sets and bring...

  14. Pattern recognition using linguistic fuzzy logic predictors

    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.

  15. Can fuzzy logic make things more clear?

    J.A. Hazelzet (Jan)

    2009-01-01

    textabstractIntensive care is a complex environment involving many signals, data and observations. Clinical decision support and artificial intelligence using fuzzy logic and closed loop techniques are methods that might help us to handle this complexity in a safe, effective and efficient way. Merou

  16. Fuzzy logic in automatic control devices

    Fuzzy logic is a theory that, applied to an automatic control device, allows to perform a regulation as efficiently as an operating expert could have done manually. The description of the behaviour of a regulation system implies the use of laws such as 'if...then', these laws link input variables that are 'conditions' to output variables that are 'conclusions'. In DAPNIA facilities fuzzy logic has been used to improve the performances of 3 control systems: -the regulation of the helium cycle compressor of a condenser, this regulation has required 21 laws, 4 conditions and 3 conclusions, -the regulation of the temperature of the LHC testing station at STCM, and -the regulation of the temperature of hydrogen target for the CLAS experiment, by means of fuzzy logic temperature stability has been driven from ±150 mK to ±20 mK, this regulation is based on 9 laws, 2 conditions and 2 conclusions. The application of fuzzy logic to regulation is presented on a simple example. (A.C.)

  17. The Logical Background of Fuzzy Plurivaluationism

    Běhounek, Libor

    Prague : Institute of Philosophy AS CR, 2012. s. 18-19. [ LOGICA 2012. 18.06.2012-22.06.2012, Hejnice] R&D Projects: GA ČR GPP103/10/P234 Institutional support: RVO:67985807 Keywords : degree theories of vagueness * fuzzy logic * formal semantics Subject RIV: BA - General Mathematics

  18. On Vagueness, Truth Values and Fuzzy Logics

    Hájek, Petr

    2009-01-01

    Roč. 91, č. 3 (2009), s. 367-382. ISSN 0039-3215 R&D Projects: GA AV ČR IAA100300503 Institutional research plan: CEZ:AV0Z10300504 Keywords : vagueness * truth values * fuzzy logics Subject RIV: BA - General Mathematics

  19. Fuzzy Logic and Arithmetical Hierarchy IV

    Hájek, Petr

    Berlin : Logos Verlag, 2004 - ( Hendricks , V.; Neuhaus, F.; Pedersen, S.; Scheffler, U.; Wansing, H.), s. 107-115 ISBN 3-8325-0475-3 R&D Projects: GA AV ČR IAA1030004 Institutional research plan: CEZ:AV0Z1030915 Keywords : fuzzy logic * arithmetical hierarchy Subject RIV: BA - General Mathematics

  20. Fuzzy logic and neural networks basic concepts & application

    Alavala, Chennakesava R

    2008-01-01

    About the Book: The primary purpose of this book is to provide the student with a comprehensive knowledge of basic concepts of fuzzy logic and neural networks. The hybridization of fuzzy logic and neural networks is also included. No previous knowledge of fuzzy logic and neural networks is required. Fuzzy logic and neural networks have been discussed in detail through illustrative examples, methods and generic applications. Extensive and carefully selected references is an invaluable resource for further study of fuzzy logic and neural networks. Each chapter is followed by a question bank

  1. Fuzzy logic systems are equivalent to feedforward neural networks

    2000-01-01

    Fuzzy logic systems and feedforward neural networks are equivalent in essence. First, interpolation representations of fuzzy logic systems are introduced and several important conclusions are given. Then three important kinds of neural networks are defined, i.e. linear neural networks, rectangle wave neural networks and nonlinear neural networks. Then it is proved that nonlinear neural networks can be represented by rectangle wave neural networks. Based on the results mentioned above, the equivalence between fuzzy logic systems and feedforward neural networks is proved, which will be very useful for theoretical research or applications on fuzzy logic systems or neural networks by means of combining fuzzy logic systems with neural networks.

  2. On-line tuning of a fuzzy-logic power system stabilizer

    A scheme for on-line tuning of a fuzzy-logic power system stabilizer is presented. firstly, a fuzzy-logic power system stabilizer is developed using speed deviation and accelerating power as the controller input variables. The inference mechanism of fuzzy-logic controller is represented by a decision table, constructed of linguistic IF-THEN rules. The Linguistic rules are available from experts and the design procedure is based on these rules. It assumed that an exact model of the plant is not available and it is difficult to extract the exact parameters of the power plant. Thus, the design procedure can not be based on an exact model. This is an advantage of fuzzy logic that makes the design of a controller possible without knowing the exact model of the plant. Secondly, two scaling parameters are introduced to tune the fuzzy-logic power system stabilizer. These scaling parameters are the outputs of another fuzzy-logic system, which gets the operating conditions of power system as inputs. These mechanism of tuning the fuzzy-logic power system stabilizer makes the fuzzy-logic power system stabilizer adaptive to changes in the operating conditions. Therefore, the degradation of the system response, under a wide range of operating conditions, is less compared to the system response with a fixed-parameter fuzzy-logic power system stabilizer and a conventional (linear) power system stabilizer. The tuned stabilizer has been tested by performing nonlinear simulations using a synchronous machine-infinite bus model. The responses are compared with a fixed parameters fuzzy-logic power system stabilizer and a conventional (linear) power system stabilizer. It is shown that the tuned fuzzy-logic power system stabilizer is superior to both of them

  3. Compensatory fuzzy logic for intelligent social network analysis

    Maikel Y. Leyva-Vázquez; Rafael Bello-Lara; Rafael Alejandro Espín-Andrade

    2014-01-01

    Fuzzy graph theory has gained in visibility for social network analysis. In this work fuzzy logic and their role in modeling social relational networks is discussed. We present a proposal for extending the fuzzy logic framework to intelligent social network analysis using the good properties of robustness and interpretability of compensatory fuzzy logic. We apply this approach to the concept path importance taking into account the length and strength of the connection. Results obtained with o...

  4. A STUDY OF FUZZY LOGICAL PETRI NETS AND ITS APPLICATION

    Jiang Changjun

    2001-01-01

    In this paper, a fuzzy Petri net approach to modelling fuzzy rule-based reasoning is proposed. Logical Petri net (LPN) and fuzzy logical Petri net (FLPN) are defined. The backward reasoning algorithm based on sub-fuzzy logical Petri net is given. It is simpler than the conventional algorithm of forward reasoning from initial propositions. An application to the partial fault model of a car engine in paper Portinale's(1993) is used as an illustrative example of FLPN.

  5. An Evaluation of Total Project Risk Based on Fuzzy Logic

    Radek Doskočil

    2015-01-01

    The article deals with the use of fuzzy logic as a support of evaluation of total project risk. A brief description of actual project risk management, fuzzy set theory, fuzzy logic and the process of calculation is given. The major goal of this paper is to present am new expert decision-making fuzzy model for evaluating total project risk. This fuzzy model based on RIPRAN method. RIPRAN (RIsk PRoject ANalysis) method is an empirical method for the analysis of project risks. The Fuzzy Logic To...

  6. reactor power control using fuzzy logic

    power stabilization is a critical issue in nuclear reactors. convention pd- controller is currently used in egypt second testing research reactor (ETRR-2). two fuzzy controllers are proposed to control the reactor power of ETRR-2 reactor. the design of the first one is based on a set of linguistic rules that were adopted from the human operators experience. after off-line fuzzy computations, the controller is a lookup table, and thus, real time controller is achieved. comparing this f lc response with the pd-controller response, which already exists in the system, through studying the expected transients during the normal operation of ETRR-2 reactor, the simulation results show that, fl s has the better response, the second controller is adaptive fuzzy controller, which is proposed to deal with system non-linearity . The simulation results show that the proposed adaptive fuzzy controller gives a better integral square error (i se) index than the existing conventional od controller

  7. Searching the Arcane Origins of Fuzzy Logic

    Angel Garrido

    2011-05-01

    Full Text Available It is well-known that Artificial Intelligence requires Logic. But its Classical version shows too many insufficiencies. So, it is very necessary to introduce more sophisticated tools, as may be
    Fuzzy Logic, Modal Logic, Non-Monotonic Logic, and so on. When you are searching the possible precedent of such new ideas, we may found that they are not totally new, because some ancient thinkers have suggested many centuries ago similar concepts, certainly without adequate mathematical formulation, but in the same line: against the dogmatism and the dualistic vision of
    the world: absolutely true vs. absolutely false, black vs. white, good or bad by nature, 0 vs.1, etc. We attempt to analyze here some of these greatly unexplored, and very interesting early origins.

  8. Fuzzy logic model to quantify risk perception

    The aim of this study is a quantification of public risk perception towards the nuclear field so as to be considered in decision making whenever the public involvement is sought. The proposed model includes both qualitative factors such as familiarity and voluntariness and numerical factors influencing risk perception, such as probability of occurrence and severity of consequence. Since part of these factors can be characterized only by qualitative expressions and the determination of them are linked with vagueness, imprecision and uncertainty, the most suitable method for the risk level assessment is Fuzzy Logic, which models qualitative aspects of knowledge and reasoning processes without employing precise quantitative analyses. This work, then, offers a Fuzzy-Logic based mean of representing the risk perception by a single numerical feature, which can be weighted and accounted for in decision making procedures. (author)

  9. Fuzzy logic controller to improve powerline communication

    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.

  10. Chassis Control based on Fuzzy Logic

    Vivas Lopez, Carlos Albertos; Morales-Menendez, Ruben; Ramirez-Mendoza, Ricardo,; Sename, Olivier; Dugard, Luc

    2016-01-01

    Based on a Global Chassis Control system with three-layers architecture (decision, control, and physical layers) a Fuzzy Logic (FL) approach is exploited. The FL based decision layer identifies the current driving condition of the vehicle and decides the control strategy to take care of this driving condition. A confusion matrix validates the classification results. The control strategy is implemented through the subsystems (suspension, steering, and braking) at the FL based control layer. Th...

  11. Fuzzy logic for business, finance, and management

    Bojadziev, George

    1997-01-01

    This is an interdisciplinary book for knowledge workers in business, finance, management, and socio-economic sciences. It provides a guide to and techniques for forecasting, decision making, conclusions, and evaluations in an environment involving uncertainty, vagueness, and impression. Traditional modeling techniques do not capture the nature of complex systems especially when humans are involved. Fuzzy logic provides effective tools for dealing with such systems. Emphasis is on applications presented in case studies including Time Forecasting for Project Management, New Product Pricing, Clie

  12. Intelligent control based on fuzzy logic and neural net theory

    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.

  13. Using fuzzy logic in business

    Bezděk, Václav

    2014-01-01

    Nowadays, the trader is forced to navigate a plethora of information about the products it has to offer. For the customer, some characteristics of a product are more important than others. The salesperson must consider this information. Therefore, sales-people often use computers in order to serve customers quickly. Conventional programming languages are based on Boolean logic. They are well suited to develop systems whose behaviour can be well represented by mathematical models. However, in ...

  14. Fuzzy logic methods for seismic damage assessment and control

    This paper goes on with some authors' previous approaches to models and methods, based on fuzzy sets and fuzzy logic, for the seismic fragility updating and seismic risk assessment (in their contributions to the IFIP 8 and SMiRT 16 Conferences held in Krakow-1998, respectively in Washington DC-2001). A short survey of earlier proposals for applying fuzzy concepts and methods in structural reliability and seismic risk studies is given in the Introduction. Basic concepts related to fuzzy sets and fuzzy logic inference rules follow in the next section. Certain applications of the fuzzy sets and fuzzy logic models to the seismic damage assessment of RC structures are presented in the third section. Finally, some recent models based on fuzzy logic for the damage estimation and control are also discussed, with emphasis on the fuzzification/defuzzification techniques. (authors)

  15. Structural modeling and fuzzy-logic based diagnosis of a ship propulsion benchmark

    Izadi-Zamanabadi, Roozbeh; Blanke, M.; Katebi, S.D.

    2000-01-01

    An analysis of structural model of a ship propulsion benchmark leads to identifying the subsystems with inherent redundant information. For a nonlinear part of the system, a Fuzzy logic based FD algorithm with adaptive threshold is employed. The results illustrate the applicability of structural...... analysis as well as fuzzy observer....

  16. Structural modeling and fuzzy-logic based diagnosis of a ship propulsion benchmark

    Izadi-Zamanabadi, Roozbeh; Blanke, M.; Katebi, S.D.

    2000-01-01

    An analysis of structural model of a ship propulsion benchmark leads to identifying the subsystems with inherent redundant information. For a nonlinear part of the system, a Fuzzy logic based FD algorithm with adaptive threshold is employed. The results illustrate the applicability of structural...... analysis as well as fuzzy observer...

  17. Genetic design of fuzzy-logic controllers for robotic manipulators

    Porter, B; Zadeh, NN

    1995-01-01

    In this paper, genetic algorithms are used to design multivariable fuzzy-logic controllers for robotic manipulators using no information regarding the dynamical parameters of such manipulators. In particular, it is shown that genetic algorithms provide a very effective means of determining both the optimal set of fuzzy rules and the domains of the associated fuzzy sets for such fuzzy-logic controllers. It is demonstrated, in the case of a particular direct-drive two-link robotic manipulator, ...

  18. INDUCTION MOTOR DIRECT TORQUE CONTROL – FUZZY LOGIC CONTRIBUTION

    CHIKHI, ABDESLEM; CHIKHI, KHALED; BELKACEM, SEBTI

    2012-01-01

    In this article we present the simulation results of the induction motor speed regulation by the direct torque control with a classic PI regulator. The MATLAB SIMULINK programming environment is used as a simulation tool. The results obtained, using a fuzzy logic, shows the importance of this method in the improvement of the performance of such regulationKeywords: DTC, Induction motor, PI, Fuzzy logic, FLR( Fuzzy logic regulator)

  19. Reactive power compensation using a fuzzy logic controlled synchronous motor

    This paper introduces the use of a fuzzy logic controlled synchronous motor for reactive power compensation. The fuzzy logic controlled synchronous motor can give a very fast response to the reactive power required by the load. Therefore, the over or under compensation and time delay are eliminated in this system. It is concluded that the reactive power compensation system with a fuzzy logic controlled synchronous motor is reliable, sensitive, economical, faster and more efficient than an other one with capacitor groups

  20. Diagnosis of Diabetes using Correlation fuzzy logic in Fuzzy Expert System

    M. Kalpana

    2012-01-01

    Full Text Available Fuzzy expert system framework constructs large scale knowledge based system effectively for diabetes. Fuzzy Expert System helps the medical practitioners to solve decision problem. The components of correlation fuzzy determination mechanism are determination logic and knowledge base. The fuzzification interface converts the crisp values into fuzzy values for the diagnosis of diabetes. The determination logic evaluates the effect on the number of membership functions, the shape of membership functions and the effect of fuzzy operators. Correlation fuzzy logic is computed for fuzzy numbers and membership function. Knowledge base is constructed by fuzzy if-then rules. Defuzzification interface converts the resulting fuzzy set into crisp values. The result of the proposed method is compared with earlier method using accuracy as metrics. The proposed fuzzy expert system can work more effectively for diabetes application and also improves the accuracy of fuzzy expert system.

  1. Improvement of flight simulator feeling using adaptive fuzzy backlash compensation

    Amara, Zied; Bordeneuve-Guibé, Joël

    2007-01-01

    In this paper we addressed the problem of improving the control of DC motors used for the specific application of a 3 degrees of freedom moving base flight simulator. Indeed the presence of backlash in DC motors gearboxes induces shocks and naturally limits the flight feeling. In this paper, dynamic inversion with Fuzzy Logic is used to design an adaptive backlash compensator. The classification property of fuzzy logic techniques makes them a natural candidate for the rejection of errors indu...

  2. Safety regulations of fuzzy-logic control to nuclear reactors

    RUAN, Da

    2000-01-01

    We present an R&D project on fuzzy-logic control applications to the Belgian Nuclear Reactor 1 (BR1) at the Belgian Nuclear Research Centre (SCK•CEN). The project started in 1995 and aimed at investigating the added value of fuzzy logic control for nuclear reactors. We first review some relevant literature on fuzzy logic control in nuclear reactors, then present the state-of-the-art of the BR1 project, with an understanding of the safety requirements for this real fuzzy-logic control ...

  3. Fuzzy logic, neural networks, and soft computing

    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

  4. Improvement of Transient Stability using Fuzzy Logic Controlled SMES

    D Harikrishna; N.V. Srikanth; Y. Chandrasekhar

    2011-01-01

    In this paper, the transient stability of an electric power system is improved by fuzzy logic controlled superconducting magnetic energy storage (SMES). The effectiveness of the proposed fuzzy controlled SMES is compared with a conventional proportional integral (PI) controlled SMES. In addition to it a comparison between the fuzzy controlled SMES and fuzzy controlled braking resistor (BR) is also carried out. The simulation results show that under 3 phase fault, the fuzzy controlled SMES per...

  5. Relations in Higher-order Fuzzy Logic III

    Bodenhofer, U.; Běhounek, Libor; Cintula, Petr

    Linz : Johannes Kepler Universität, 2005 - (Gottwald, S.; Hájek, P.; Höhle, U.; Klement, E.). s. 20-22 [Linz Seminar on Fuzzy Set Theory /26./. 01.02.2005-05.02.2005, Linz] R&D Projects: GA MŠk OC 274.001; GA AV ČR KJB100300502; GA ČR GD401/03/H047 Grant ostatní: COST(XE) Action 274 TARSKI Institutional research plan: CEZ:AV0Z10300504 Source of funding: V - iné verejné zdroje Keywords : formal fuzzy logic * fuzzy set * foundations of fuzzy mathematics * LPi logic * higher-order fuzzy logic * fuzzy type theory * multi-sorted fuzzy logic Subject RIV: BA - General Mathematics

  6. Mapping Shape Geometry And Emotions Using Fuzzy Logic

    Achiche, Sofiane; Ahmed, Saeema

    2008-01-01

    intended emotion using fuzzy logic. To achieve this; 3D objects (shapes) created by design engineering students to match a set of words/emotions were analyzed. The authors identified geometric information as inputs of the fuzzy model and developed a set of fuzzy if/then rules to map the relationships...... between the fuzzy sets on each input premise and the output premise. In our case the output premise of the fuzzy logic model is the level of belonging to the design context (emotion). An evaluation of how users perceived the shapes was conducted to validate the fuzzy logic model and showed a high...... correlation between the fuzzy logic model and user perception....

  7. NETWORK INTRUSION DETECTION SYSTEM USING FUZZY LOGIC

    R. Shanmugavadivu

    2011-02-01

    Full Text Available IDS which are increasingly a key part of system defense are used to identify abnormal activities in a computer system. In general, the traditional intrusion detection relies on the extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, variousdata-mining and machine learning techniques have been used in the literature. In the proposed system, we have designed fuzzy logic-based system for effectively identifying the intrusion activities within a network. The proposed fuzzy logic-based system can be able to detect an intrusion behavior of the networks since the rule base contains a better set of rules. Here, we have used automated strategy for generation of fuzzy rules, which are obtained from the definite rules using frequent items. The experiments and evaluations of the proposed intrusion detection system are performed with the KDD Cup 99 intrusion detection dataset. The experimentalresults clearly show that the proposed system achieved higher precision in identifying whether the records are normal or attack one.

  8. Output-back fuzzy logic systems and equivalence with feedback neural networks

    2000-01-01

    A new idea, output-back fuzzy logic systems, is proposed. It is proved that output-back fuzzy logic systems must be equivalent to feedback neural networks. After the notion of generalized fuzzy logic systems is defined, which contains at least a typical fuzzy logic system and an output-back fuzzy logic system, one important conclusion is drawn that generalized fuzzy logic systems are almost equivalent to neural networks.

  9. Fuzzy Logic Connectivity in Semiconductor Defect Clustering

    Gleason, S.S.; Kamowski, T.P.; Tobin, K.W.

    1999-01-24

    In joining defects on semiconductor wafer maps into clusters, it is common for defects caused by different sources to overlap. Simple morphological image processing tends to either join too many unrelated defects together or not enough together. Expert semiconductor fabrication engineers have demonstrated that they can easily group clusters of defects from a common manufacturing problem source into a single signature. Capturing this thought process is ideally suited for fuzzy logic. A system of rules was developed to join disconnected clusters based on properties such as elongation, orientation, and distance. The clusters are evaluated on a pair-wise basis using the fuzzy rules and are joined or not joined based on a defuzzification and threshold. The system continuously re-evaluates the clusters under consideration as their fuzzy memberships change with each joining action. The fuzzy membership functions for each pair-wise feature, the techniques used to measure the features, and methods for improving the speed of the system are all developed. Examples of the process are shown using real-world semiconductor wafer maps obtained from chip manufacturers. The algorithm is utilized in the Spatial Signature Analyzer (SSA) software, a joint development project between Oak Ridge National Lab (ORNL) and SEMATECH.

  10. Energy Discriminant Analysis, Quantum Logic, and Fuzzy sets

    Melnichenko, Grigorii

    2007-01-01

    In the paper, we show that quantum logic of linear subspaces can be used for recognition of random signals by a Bayesian energy discriminant classifier. The energy distribution on linear subspaces is described by the correlation matrix of the probability distribution. We show that the correlation matrix corresponds to von Neumann density matrix in quantum theory. We suggest the interpretation of quantum logic as a fuzzy logic of fuzzy sets. The use of quantum logic for recognition is based on...

  11. Control of beam halo-chaos using fuzzy logic controller

    Considering the ion beam with initial K-V distribution in the periodic focusing magnetic filed channels (PFCs) as a typical sample, a fuzzy control method for control- ling beam halo-chaos was studied. A fuzzy proportional controller, using output of fuzzy inference as a control factor, was presented for adjusting exterior focusing magnetic field. The stability of controlled system was proved by fuzzy phase plane analysis. The simulation results demonstrate that the chaotic radius of envelope can be controlled to the matched radius via controlling magnetic field. This method was also applied to the multi-particle model. Under the control condition, the beam halos and its regeneration can be eliminated effectively, and that both the compactness and the uniformity of ion beam are improved evidently. Since the exterior magnetic field can be rather easily adjusted by proportional control and the fuzzy logic controller is independent to the mathematical model, this method has adaptive ability and is easily realized in experiment. The research offers a valuable reference for the design of the PFCs in the high- current linear ion accelerators. (authors)

  12. Power transformer protection by using Fuzzy Logic

    A. Aziz

    2009-01-01

    Full Text Available Power transformer protective relay should block the tripping during magnetizing inrush and rapidly operate the tripping during internal faults. Recently, the frequency environment of power system has been made more complicated and the quantity of 2nd frequency component in inrush state has been decreased because of the improvement of core steel. And then, traditional approaches will likely be maloperated in the case of magnetizing inrush with low second harmonic component and internal faults with high second harmonic component. This paper proposes a new relaying algorithm to enhance the fault detection sensitivities of conventional techniques by using a fuzzy logic approach. The proposed fuzzy-based relaying algorithm consists of flux-differential current derivative curve, harmonic restraint, and percentage differential characteristic curve. The proposed relaying was tested with MATLAB simulation software and showed a fast and accurate trip operation

  13. An Alternative Justification of the Axioms of Fuzzy Logics

    Běhounek, Libor

    2007-01-01

    Roč. 13, č. 2 (2007), s. 267-267. ISSN 1079-8986. [Logic Colloquium 2006. 27.07.2006-02.08.2006, Nijmegen] Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy logic * logical consequence * logical axioms

  14. FUZZY LOGIC CONTROLLER IMPLEMENTATION FOR PHOTOVOLTAIC STATION

    Imad Zein

    2014-01-01

    Full Text Available 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. This is why the controllers of all solar power electronic converters employ some method for maximum power point tracking (MPPT . Over the past years many MPPT techniques have been published and based on that the main paper’s objective is to analyze one of the most promising MPPT control algorithms: fuzzy logic controller. 

  15. Improving Cooperative PSO using Fuzzy Logic

    Afsahi, Zahra; Meybodi, Mohammadreza

    PSO is a population-based technique for optimization, which simulates the social behaviour of the fish schooling or bird flocking. Two significant weaknesses of this method are: first, falling into local optimum and second, the curse of dimensionality. In this work we present the FCPSO-H to overcome these weaknesses. Our approach was implemented in the cooperative PSO, which employs fuzzy logic to control the acceleration coefficients in velocity equation of each particle. The proposed approach is validated by function optimization problem form the standard literature simulation result indicates that the approach is highly competitive specifically in its better general convergence performance.

  16. Fuzzy logic control of a nitrogen laser

    Tam, Siu-Chung; Tan, Siong-Chai; Neo, Wah-Peng; Foong, Sze-Chern; Chan, Choon-Hao; Ho, Anthony T. S.; Chua, Hock-Chuan; Lee, Sing

    2001-01-01

    Traditionally, the stability of the output of a laser is controlled through scientific means or by a simple feedback loop. For multiinput multioutput control and for medium- to high-power lasers, however, these control schemes may break down. We report on the use of a fuzzy logic control scheme to improve the stability of a pulsed nitrogen laser. Specifically, the nitrogen laser is modeled as a two-input two-output system. The two input parameters are the discharge voltage (V) and nitrogen pr...

  17. Genetic algorithms in adaptive fuzzy control

    Karr, C. Lucas; Harper, Tony R.

    1992-01-01

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

  18. Looking for Oriental fundamentals Fuzzy Logic

    Angel Garrido

    2013-10-01

    Full Text Available For quite some time we have been trying to trace the river of Non-ClassicalLogics, and especially, Fuzzy Logic, trying to find the sources of this today flowing quite mighty river. Following from Lotfi A. Zadeh, we have traced his inspiring, the Polish logician Jan Lukasiewicz, who in turn was inspired by Aristotle's Peri Hermeneias (De Interpretatione. Also, Lukasiewicz occupies a central position in the Lvov-Warsaw School, who founded Kazimierz Twardowski, a student of Franz Brentano, and this in turn disciple of Bernard Bolzano. The connection with Leibniz and Bolzano come through medieval scholastic thinkers, especially John Duns Scotus and William of Ockham and the problem of future contingents, they had collected from the Aristotelian tradition. But there was to trace the “eastern (oriental track, which leads to the ancient Chinese and Indian philosophy. Here we will treat it as a first and necessary approach.

  19. Comparison of Anti-Virus Programs using Fuzzy Logic

    Vaclav Bezdek

    2013-07-01

    Full Text Available This work follows the previous author´s paper: Possible use of Fuzzy Logic in Database. It tries to show application of Fuzzy Logic in selecting the best anti-virus software based on testing made by AV-Comparatives.

  20. Fault Diagnosis and Reliability Analysis Using Fuzzy Logic Method

    Miao Zhinong; Xu Yang; Zhao Xiangyu

    2006-01-01

    A new fuzzy logic fault diagnosis method is proposed. In this method, fuzzy equations are employed to estimate the component state of a system based on the measured system performance and the relationship between component state and system performance which is called as "performance-parameter" knowledge base and constructed by expert. Compared with the traditional fault diagnosis method, this fuzzy logic method can use humans intuitive knowledge and dose not need a precise mapping between system performance and component state. Simulation proves its effectiveness in fault diagnosis. Then, the reliability analysis is performed based on the fuzzy logic method.

  1. Fuzzy Logic and Intelligent Technologies in Nuclear Science

    The key issues, addressed by the workshop on Fuzzy Logic and Intelligent Technologies in Nuclear Science (FLINS), are related to solving problems, relevant to fundamental and applied research in the nuclear industry by using modern and advanced technologies and tools. The proceedings of the first International FLINS Workshop include invited lectures and individual presentations, covering applications in radiation protection, nuclear safety (human factors and reliability), safeguards, nuclear power plant control, decision making, and nuclear reactor control. The papers cover three domains. The first section (Mathematics) deals with basic tools to treat fuzziness, probabilities, possibilities, decision-making, and software for fuzzy logic. The second part (Engineering) includes contributions dealing with engineering aspects such as knowledge based engineering, expert systems, process control integration, diagnosis, measurements and interpretation by fuzzy logic and fuzzy databases. The third part (Nuclear Science) focuses on the application of fuzzy logic in nuclear science

  2. Fuzzy logic and intelligent technologies in nuclear science

    The research project on Fuzzy Logic and Intelligent technologies (FLINS) aims to bridge the gap between novel technologies and the nuclear industry. It aims to initiate research and development programs for solving intricate problems pertaining to the nuclear environment by using modern technologies as additional tool. The major achievements for 1997 include the application of the fuzzy-logic to the BR-1 reactor, the elaboration of a Fuzzy-control model as well as contributions to several workshops and publications

  3. Diagnosis of Diabetes using Correlation fuzzy logic in Fuzzy Expert System

    M. Kalpana; A.V. Senthil Kumar

    2012-01-01

    Fuzzy expert system framework constructs large scale knowledge based system effectively for diabetes. Fuzzy Expert System helps the medical practitioners to solve decision problem. The components of correlation fuzzy determination mechanism are determination logic and knowledge base. The fuzzification interface converts the crisp values into fuzzy values for the diagnosis of diabetes. The determination logic evaluates the effect on the number of membership functions, the shape of membership f...

  4. Using an Adaptative Fuzzy-Logic System to Optimize the Performances and the Reduction of Chattering Phenomenon in the Control of Induction Motor

    M. M. Krishan

    2010-01-01

    Full Text Available Problem statement: Neural networks and fuzzy inference systems are becoming well-recognized tools of designing an identifier/controller capable of perceiving the operating environment and imitating a human operator with high performance. Also, by combining these two features, more versatile and robust models, called neuro-fuzzy architectures have been developed. The mo Approach: Motivation behind the use of neuro-fuzzy approaches was based on the complexity of real life systems, ambiguities on sensory information or time-varying nature of the system under investigation. In this way, the present contribution concerns the application of neuro-fuzzy approach in order to perform the responses of the speed regulation, ensure more robustness of the overall system and to reduce the chattering phenomenon introduced by sliding mode control which is very harmful to the actuators in our case and may excite the unmodeled dynamics of the system. Results: In fact, the aim of such a research consists first in simplifying the control of the motor by decoupling between two principles variables which provoque the torque in the motor by using the feedback linearization method. Then, using sliding mode controllers to give our process more robustness towards the variation of different parameters of the motor. However, the latter technique of control called sliding mode control caused an indesirable phenomenon which harmful and could leads to the deterioration of the inverters components called chattering. So, here the authors propose to use neuro-fuzzy systems to reduce this phenomenon and perform the performances of the adopted control process. The type of the neuro-fuzzy system used here is called: Adaptive Neuro Fuzzy Inference System (ANFIS. This neuro-fuzzy is destined to replace the speed fuzzy sliding mode controller after its training process. Conclusion: Therefore, from a control design consideration, the adopted neuro-fuzzy system has opened up a new

  5. Tightly coupled fuzzy description logic programs under the answer set semantics for the SemanticWeb

    Lukasiewicz, Thomas; Straccia, Umberto

    2010-01-01

    We present a novel approach to fuzzy description logic programs (or simply fuzzy dl-programs) under the answer set semantics, which is a tight integration of fuzzy disjunctive logic programs under the answer set semantics with fuzzy description logics. From a different perspective, it is a generalization of tightly coupled disjunctive dl-programs by fuzzy vagueness in both the description logic and the logic program component. We show that the new formalism faithfully extends both fuzzy disju...

  6. Active structural control by fuzzy logic rules: An introduction

    Tang, Y.

    1995-07-01

    An introduction to fuzzy logic control applied to the active structural control to reduce the dynamic response of structures subjected to earthquake excitations is presented. It is hoped that this presentation will increase the attractiveness of the methodology to structural engineers in research as well as in practice. The basic concept of the fuzzy logic control are explained by examples and by diagrams with a minimum of mathematics. The effectiveness and simplicity of the fuzzy logic control is demonstrated by a numerical example in which the response of a single-degree-of-freedom system subjected to earthquake excitations is controlled by making use of the fuzzy logic controller. In the example, the fuzzy rules are first learned from the results obtained from linear control theory; then they are fine tuned to improve their performance. It is shown that the performance of fuzzy logic control surpasses that of the linear control theory. The paper shows that linear control theory provides experience for fuzzy logic control, and fuzzy logic control can provide better performance; therefore, two controllers complement each other.

  7. Active structural control by fuzzy logic rules: An introduction

    Tang, Yu [Argonne National Lab., IL (United States). Reactor Engineering Div.; Wu, Kung C. [Texas Univ., El Paso, TX (United States). Dept. of Mechanical and Industrial Engineering

    1996-12-31

    A zeroth level introduction to fuzzy logic control applied to the active structural control to reduce the dynamic response of structures subjected to earthquake excitations is presented. It is hoped that this presentation will increase the attractiveness of the methodology to structural engineers in research as well as in practice. The basic concept of the fuzzy logic control are explained by examples and by diagrams with a minimum of mathematics. The effectiveness and simplicity of the fuzzy logic control is demonstrated by a numerical example in which the response of a single- degree-of-freedom system subjected to earthquake excitations is controlled by making use of the fuzzy logic controller. In the example, the fuzzy rules are first learned from the results obtained from linear control theory; then they are fine tuned to improve their performance. It is shown that the performance of fuzzy logic control surpasses that of the linear control theory. The paper shows that linear control theory provides experience for fuzzy logic control, and fuzzy logic control can provide better performance; therefore, two controllers complement each other.

  8. SOFC temperature evaluation based on an adaptive fuzzy controller

    Xiao-juan WU; Xin-jian ZHU; Guang-yi CAO; Heng-yong TU

    2008-01-01

    The operating temperature of a solid oxide fuel cell (SOFC) stack is a very important parameter to be controlled, which impacts the performance of the SOFC due to thermal cycling. In this paper, an adaptive fuzzy control method based on an affine nonlinear temperature model is developed to control the temperature of the SOFC within a specified range. Fuzzy logic systems are used to approximate nonlinear functions in the SOFC system and an adaptive technique is employed to construct the controller. Compared with the traditional fuzzy and proportion-integral-derivative (PID) control, the simulation results show that the designed adaptive fuzzy control method performed much better. So it is feasible to build an adaptive fuzzy controller for temperature control of the SOFC.

  9. A fuzzy logic approach to modeling a vehicle crash test

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

    2012-01-01

    This paper presents an application of fuzzy approach to vehicle crash modeling. A typical vehicle to pole collision is described and kinematics of a car involved in this type of crash event is thoroughly characterized. The basics of fuzzy set theory and modeling principles based on fuzzy logic approach are presented. In particular, exceptional attention is paid to explain the methodology of creation of a fuzzy model of a vehicle collision. Furthermore, the simulation results are presented and...

  10. On the Difference between Traditional and Deductive Fuzzy Logic

    Běhounek, Libor

    2008-01-01

    Roč. 159, č. 10 (2008), s. 1153-1164. ISSN 0165-0114 R&D Projects: GA AV ČR KJB100300502 Institutional research plan: CEZ:AV0Z10300504 Keywords : deductive fuzzy logic * fuzzy elements * gradual sets * entropy of fuzzy sets * aggregation * membership degrees * methodology of fuzzy mathematics Subject RIV: BA - General Mathematics Impact factor: 1.833, year: 2008

  11. On logical, algebraic, and probabilistic aspects of fuzzy set theory

    Mesiar, Radko

    2016-01-01

    The book is a collection of contributions by leading experts, developed around traditional themes discussed at the annual Linz Seminars on Fuzzy Set Theory. The different chapters have been written by former PhD students, colleagues, co-authors and friends of Peter Klement, a leading researcher and the organizer of the Linz Seminars on Fuzzy Set Theory. The book also includes advanced findings on topics inspired by Klement’s research activities, concerning copulas, measures and integrals, as well as aggregation problems. Some of the chapters reflect personal views and controversial aspects of traditional topics, while others deal with deep mathematical theories, such as the algebraic and logical foundations of fuzzy set theory and fuzzy logic. Originally thought as an homage to Peter Klement, the book also represents an advanced reference guide to the mathematical theories related to fuzzy logic and fuzzy set theory with the potential to stimulate important discussions on new research directions in the fiel...

  12. Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty

    Starczewski, Janusz T

    2013-01-01

    This book generalizes fuzzy logic systems for different types of uncertainty, including - semantic ambiguity resulting from limited perception or lack of knowledge about exact membership functions - lack of attributes or granularity arising from discretization of real data - imprecise description of membership functions - vagueness perceived as fuzzification of conditional attributes. Consequently, the membership uncertainty can be modeled by combining methods of conventional and type-2 fuzzy logic, rough set theory and possibility theory.            In particular, this book provides a number of formulae for implementing the operation extended on fuzzy-valued fuzzy sets and presents some basic structures of generalized uncertain fuzzy logic systems, as well as introduces several of methods to generate fuzzy membership uncertainty. It is desirable as a reference book for under-graduates in higher education, master and doctor graduates in the courses of computer science, computational intelligence, or...

  13. Compensatory fuzzy logic for intelligent social network analysis

    Maikel Y. Leyva-Vázquez

    2014-10-01

    Full Text Available Fuzzy graph theory has gained in visibility for social network analysis. In this work fuzzy logic and their role in modeling social relational networks is discussed. We present a proposal for extending the fuzzy logic framework to intelligent social network analysis using the good properties of robustness and interpretability of compensatory fuzzy logic. We apply this approach to the concept path importance taking into account the length and strength of the connection. Results obtained with our model are more consistent with the way human make decisions. Additionally a case study to illustrate the applicability of the proposal on a coauthorship network is developed. Our main outcome is a new model for social network analysis based on compensatory fuzzy logic that gives more robust results and allows compensation. Moreover this approach makes emphasis in using language for social network analysis.

  14. Fuzzy logic based automatic voltage regulator for damping power oscillations

    Prasertwong, K. [Srinakharinwirot Univ., Ongkharak, Nahhonnayok (Thailand). Dept. of Electrical Engineering; Mithulananthan, N. [Asian Inst. of Technology, Klong Luang, Pathumthani (Thailand). Energy Field of Study

    2008-07-01

    Low frequency oscillations in a power system can result in instability and widespread blackouts. A new fuzzy logic based automatic voltage regulator for damping power system oscillations was presented. The proposed controller has one voltage control loop which functions as an automatic voltage regulating unit in a synchronous machine. The input signals for voltage control include the terminal voltage error and its derivative. Comparison studies were also conducted to determine the performance of the proposed controller with the conventional automatic voltage regulator (AVR) compared with the conventional AVR combined with a power system stabilizer (PSS). This paper systematically explained the steps involved in fuzzy logic control design for oscillation damping in power systems. A comparison between fuzzy logic AVR and conventional AVR revealed that fuzzy logic AVR performed better. The proposed fuzzy logic AVR provided good damping and improved dynamics. Although fuzzy based controllers have a number of advantages, different operating points need to be considered in order to gain the robustness of the fuzzy based controllers. Fuzzy logic controllers are suitable for nonlinear, dynamic processes for which an exact mathematical model may not be available. 9 refs, 5 tabs., 14 figs.

  15. Studies on Fuzzy Logic and Dispositions for Medical Diagnosis

    Prof. Sripati Mukhopadhyay

    2011-09-01

    Full Text Available For designing and developing a knowledge-based system we need to store expert’s knowledge in a suitable form, known as knowledge base, and then applying a suitable reasoning process to arrive at a decision. Formal logic, a two-valued logic, known as predicate logic, is suitable for developing and inferring for systems like mechanical theorem proving. But if we want to develop a serious real life knowledge-based system like Medical Diagnosis, formal logic fails to describe the knowledge-base, and obviously Fuzzy logic and extension of Fuzzy logic, known as dispositions as proposed by Zadeh, come in to rescue and ultimately enabling us to use linguistic variables. In this article an attempt has been made to show how fuzzy logic and it’s extension, particularly dispositions, can be used for modeling a medical diagnosis system.

  16. Fuzzy Optimized Metric for Adaptive Network Routing

    Ahmad Khader Haboush

    2012-04-01

    Full Text Available Network routing algorithms used today calculate least cost (shortest paths between nodes. The cost of a path is the sum of the cost of all links on that path. The use of a single metric for adaptive routing is insufficient to reflect the actual state of the link. In general, there is a limitation on the accuracy of the link state information obtained by the routing protocol. Hence it becomes useful if two or more metrics can be associated to produce a single metric that can describe the state of the link more accurately. In this paper, a fuzzy inference rule base is implemented to generate the fuzzy cost of each candidate path to be used in routing the incoming calls. This fuzzy cost is based on the crisp values of the different metrics; a fuzzy membership function is defined. The parameters of these membership functions reflect dynamically the requirement of the incoming traffic service as well as the current state of the links in the path. And this paper investigates how three metrics, the mean link bandwidth, queue utilization and the mean link delay, can be related using a simple fuzzy logic algorithm to produce a optimized cost of the link for a certain interval that is more „precise‟ than either of the single metric, to solve routing problem .

  17. APPLICATION OF TYPE-2 FUZZY LOGIC BASED DATA MINING TO FORECASTING OF TIME SERIES

    Özek, Müzeyyen Bulut

    2010-01-01

    In this study, a type-2 fuzzy logic based data mining method is developed. Both type-1 fuzzy logic and type-2 fuzzy logic based data mining methods are used for the prediction of future values of Elazig's meterologic data. Performances of the type-1 and type-2 fuzzy logic based data mining softwares are compared.

  18. Introspection of vague knowledge in fuzzy epistemic logic

    Běhounek, Libor

    Torun: Nicolaus Copernicus University, 2012. s. 8. [Conference on Non-classical Logic /5./. 27.09.2012-29.09.2012, Torun] R&D Projects: GA ČR GAP202/10/1826 Institutional support: RVO:67985807 Keywords : epistemic logic * fuzzy logic * vagueness * Williamson's paradox Subject RIV: BA - General Mathematics

  19. Adaptive Fuzzy Control for CVT Vehicle

    2005-01-01

    On the simple continuously variable transmission (CVT) driveline model, the design of adaptive fuzzy control system for CVT vehicle is presented. The adaptive fuzzy control system consists of a scaling factor self-tuning fuzzy-PI throttle controller, and a hybrid fuzzy-PID CVT ratio and brake controller. The presented adaptive fuzzy control strategy is vehicle model independent, which depends only on the instantaneous vehicle states, but does not depend on vehicle parameters. So it has good robustness against uncertain vehicle parameters and exogenous load disturbance. Simulation results show that the proposed adaptive fuzzy strategy has good adaptability and practicality value.

  20. A simple fuzzy logic real-time camera tracking system

    Magee, Kevin N.; Cheatham, John B., Jr.

    1993-01-01

    A fuzzy logic control of camera pan and tilt has been implemented to provide real-time camera tracking of a moving object. The user clicks a mouse button to identify the object that is to be tracked. A rapid centroid estimation algorithm is used to estimate the location of the moving object, and based on simple fuzzy membership functions, fuzzy x and y values are input into a six-rule fuzzy logic rule base. The output of this system is de-fuzzified to provide pan and tilt velocities required to keep the image of the object approximately centered in the camera field of view.

  1. Petr Hájek on mathematical fuzzy logic

    Montagna, Franco

    2014-01-01

    This volume celebrates the work of Petr Hájek on mathematical fuzzy logic and presents how his efforts have influenced prominent logicians who are continuing his work. The book opens with a discussion on Hájek's contribution to mathematical fuzzy logic and with a scientific biography of him, progresses to include two articles with a foundation flavour, that demonstrate some important aspects of Hájek's production, namely, a paper on the development of fuzzy sets and another paper on some fuzzy versions of set theory and arithmetic. Articles in the volume also focus on the treatment of vague

  2. CAC Algorithm Based on Fuzzy Logic

    Ľubomír DOBOŠ

    2009-05-01

    Full Text Available Quality of Service (QoS represent one ofmajor parameters that describe mobile wirelesscommunication systems. Thanks growing popularity ofmobile communication in last years, there is anincreasing expansion of connection admission controlschemes (CAC that plays important role in QoSdelivering in terms of connection blocking probability,connection dropping probability, data loss rate andsignal quality.With expansion of services provided by the mobilenetworks growing the requirements to QoS andtogether growing requirements to CAC schemes.Therefore, still more sophisticated CAC schemes arerequired to guarantee the QoS. This paper containsshort introduction into division of connectionadmission control schemes and presents thresholdoriented CAC scheme with fuzzy logic used foradaptation of the threshold value.

  3. Fuzzy Logic Enhanced Digital PIV Processing Software

    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.

  4. Hybrid Genetic Algorithms with Fuzzy Logic Controller

    2001-01-01

    In this paper, a new implementation of genetic algorithms (GAs) is developed for the machine scheduling problem, which is abundant among the modern manufacturing systems. The performance measure of early and tardy completion of jobs is very natural as one's aim, which is usually to minimize simultaneously both earliness and tardiness of all jobs. As the problem is NP-hard and no effective algorithms exist, we propose a hybrid genetic algorithms approach to deal with it. We adjust the crossover and mutation probabilities by fuzzy logic controller whereas the hybrid genetic algorithm does not require preliminary experiments to determine probabilities for genetic operators. The experimental results show the effectiveness of the GAs method proposed in the paper.``

  5. Application of fuzzy logic operation and control to BWRs

    Fuzzy logic control schemes employing linguistic decision rules for flexible operator control strategies have undergone application tests in dynamic systems. The advantages claimed for fuzzy logic control are its abilities: (a) to facilitate direct use of skillful operator know-how for automatic operation and control of the systems and (b) to provide robust multivariable control for complex plants. The authors have also studied applications of fuzzy logic control to automatic startup operations and load-following control in boiling water reactors, pursuing these same advantages

  6. Dynamic regimes of random fuzzy logic networks

    Random multistate networks, generalizations of the Boolean Kauffman networks, are generic models for complex systems of interacting agents. Depending on their mean connectivity, these networks exhibit ordered as well as chaotic behavior with a critical boundary separating both regimes. Typically, the nodes of these networks are assigned single discrete states. Here, we describe nodes by fuzzy numbers, i.e. vectors of degree-of-membership (DOM) functions specifying the degree to which the nodes are in each of their discrete states. This allows our models to deal with imprecision and uncertainties. Compatible update rules are constructed by expressing the update rules of the multistate network in terms of Boolean operators and generalizing them to fuzzy logic (FL) operators. The standard choice for these generalizations is the Goedel FL, where AND and OR are replaced by the minimum and maximum of two DOMs, respectively. In mean-field approximations we are able to analytically describe the percolation and asymptotic distribution of DOMs in random Goedel FL networks. This allows us to characterize the different dynamic regimes of random multistate networks in terms of FL. In a low-dimensional example, we provide explicit computations and validate our mean-field results by showing that they agree well with network simulations.

  7. Dynamic regimes of random fuzzy logic networks

    Wittmann, Dominik M.; Theis, Fabian J.

    2011-01-01

    Random multistate networks, generalizations of the Boolean Kauffman networks, are generic models for complex systems of interacting agents. Depending on their mean connectivity, these networks exhibit ordered as well as chaotic behavior with a critical boundary separating both regimes. Typically, the nodes of these networks are assigned single discrete states. Here, we describe nodes by fuzzy numbers, i.e. vectors of degree-of-membership (DOM) functions specifying the degree to which the nodes are in each of their discrete states. This allows our models to deal with imprecision and uncertainties. Compatible update rules are constructed by expressing the update rules of the multistate network in terms of Boolean operators and generalizing them to fuzzy logic (FL) operators. The standard choice for these generalizations is the Gödel FL, where AND and OR are replaced by the minimum and maximum of two DOMs, respectively. In mean-field approximations we are able to analytically describe the percolation and asymptotic distribution of DOMs in random Gödel FL networks. This allows us to characterize the different dynamic regimes of random multistate networks in terms of FL. In a low-dimensional example, we provide explicit computations and validate our mean-field results by showing that they agree well with network simulations.

  8. An Evaluation of Total Project Risk Based on Fuzzy Logic

    Radek Doskočil

    2015-12-01

    Full Text Available The article deals with the use of fuzzy logic as a support of evaluation of total project risk. A brief description of actual project risk management, fuzzy set theory, fuzzy logic and the process of calculation is given. The major goal of this paper is to present am new expert decision-making fuzzy model for evaluating total project risk. This fuzzy model based on RIPRAN method. RIPRAN (RIsk PRoject ANalysis method is an empirical method for the analysis of project risks. The Fuzzy Logic Toolbox in MATLAB software was used to create the decision-making fuzzy model. The advantage of the fuzzy model is the ability to transform the input variables The Number of Sub-Risks (NSR and The Total Value of Sub-Risks (TVSR to linguistic variables, as well as linguistic evaluation of the Total Value of Project Risk (TVPR – output variable. With this approach it is possible to simulate the risk value and uncertainty that are always associated with real projects. The scheme of the model, rule block, attributes and their membership functions are mentioned in a case study. The use of fuzzy logic is a particular advantage in decision-making processes where description by algorithms is extremely difficult and criteria are multiplied.

  9. Twenty-Five Years of the Fuzzy Factor: Fuzzy Logic, the Courts, and Student Press Law.

    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…

  10. Enric Trillas a passion for fuzzy sets : a collection of recent works on fuzzy logic

    Verdegay, Jose; Esteva, Francesc

    2015-01-01

    This book presents a comprehensive collection of the latest and most significant research advances and applications in the field of fuzzy logic. It covers fuzzy structures, rules, operations and mathematical formalisms, as well as important applications of fuzzy logic in a number of fields, like decision-making, environmental prediction and prevention, communication, controls and many others. Dedicated to Enric Trillas in recognition of his pioneering research in the field, the book also includes a foreword by Lotfi A. Zadeh and an outlook on the future of fuzzy logic.

  11. Building a fuzzy logic based tool for E-readiness measurement

    Davidrajuh, Reggie

    2008-01-01

    Firstly, this paper presents fuzzy logic based approaches for building a tool for measuring ereadiness of a country. This paper proposes fuzzy logic for realizing the measuring tool as fuzzy logic allows processing of heterogeneous indicators and imprecise values assigned for them. The tool is constructed by using one or more fuzzy logic based inference engines. Secondly, due to the problems in constructing pure fuzzy logic based inference engines, this paper also proposes some...

  12. Improvement of Transient Stability using Fuzzy Logic Controlled SMES

    D. Harikrishna

    2011-12-01

    Full Text Available In this paper, the transient stability of an electric power system is improved by fuzzy logic controlled superconducting magnetic energy storage (SMES. The effectiveness of the proposed fuzzy controlled SMES is compared with a conventional proportional integral (PI controlled SMES. In addition to it a comparison between the fuzzy controlled SMES and fuzzy controlled braking resistor (BR is also carried out. The simulation results show that under 3 phase fault, the fuzzy controlled SMES performance is better than PI controlled SMES. Furthermore, the performance of SMES is better than that of BR. The proposed method provides a very simple and effective means of improvement of transient stability.

  13. Fifty years of fuzzy logic and its applications

    Rishe, Naphtali; Kandel, Abraham

    2015-01-01

    This book presents a comprehensive report on the evolution of Fuzzy Logic since its formulation in Lotfi Zadeh’s seminal paper on “fuzzy sets,” published in 1965. In addition, it features a stimulating sampling from the broad field of research and development inspired by Zadeh’s paper. The chapters, written by pioneers and prominent scholars in the field, show how fuzzy sets have been successfully applied to artificial intelligence, control theory, inference, and reasoning. The book also reports on theoretical issues; features recent applications of Fuzzy Logic in the fields of neural networks, clustering, data mining, and software testing; and highlights an important paradigm shift caused by Fuzzy Logic in the area of uncertainty management. Conceived by the editors as an academic celebration of the fifty years’ anniversary of the 1965 paper, this work is a must-have for students and researchers willing to get an inspiring picture of the potentialities, limitations, achievements and accomplishments...

  14. Type-2 Fuzzy Logic in Intelligent Control Applications

    Castillo, Oscar

    2012-01-01

    We describe in this book, hybrid intelligent systems based mainly on type-2 fuzzy logic for intelligent control. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, and bio-inspired optimization algorithms, which can be used to produce powerful automatic control systems. The book is organized in three main parts, which contain a group of chapters around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which can be the basis for achieving intelligent control with interval type-2 fuzzy logic. The second part of the book is comprised of chapters with the main theme of evolutionary optimization of type-2 fuzzy systems in intelligent control with the aim of designing optimal type-2 fuzzy controllers for complex control problems in diverse areas of application, including mobile robotics, aircraft dynamics systems and hardware implementations. Th...

  15. Experiments of fuzzy logic control ion a nuclear research reactor

    The application of a fuzzy logic control scheme is presented in order to improve the power system stability of BR1 (Belgian Reactor 1) at the Belgian Nuclear Research Centre (SCK-CEN). The control scheme is developed based on OMRON's fuzzy hardware (C200H-FZ001) and the Fuzzy Support Software (FSS) because of their high performance and flexibility. The various possibilities are discussed to find the best or optimal fuzzy logic control scheme for controlling BR1. On basis of the previous researches 1,2,3, some experiments have been carried out in both the steady-state and dynamic operation conditions. The results reveal that the fuzzy logic control scheme has the potential to replace nuclear reactor operators in the control room. Hence, the entire control process can be automatic, simple and effective

  16. French-speaking meeting on fuzzy logic and its applications

    The 1997 edition of LFA'97 meeting for fuzzy logic has been organized by the Pattern Recognition and Computer Vision Laboratory of the National Institute of Applied Sciences. The objective of the meeting was to provide a forum for researchers and users of fuzzy logic and possibility theory to present and discuss theoretical researches and concrete applications. The domains in concern are: the control decision theory, the pattern recognition and image analysis, the artificial intelligence and the information systems. From the 41 papers of this book, two were selected for ETDE and deal with fuzzy regulation systems for heating systems and with fuzzy controllers for gas refining plants, and one was selected for INIS and deal with real-time surveillance and fuzzy logic control systems for nuclear power plants. (J.S.)

  17. Fuzzy logic control for PWR load-follow

    The developments of fuzzy logic control theory promote the application of fuzzy logic controller to load-follow in Pressurized Water Reactors. A control law combined fuzzy logic controller to load-follow in Pressurized Water Reactors. A control law combined fuzzy logic controller with conventional PID controller, using the strategy of output gains varying with nuclear reactor power, was proposed to control load-follow operations in PWR. This method solves the nonlinear time-varying close-loop control problem and overcomes the shortcomings and limitations of the model-based method. The simulation results show the method is of both satisfactory dynamic performance and high steady state precision. This approach will improve the automaticity of load-follow operations

  18. Two-frequency voltage flicker estimation using fuzzy logic

    Al-Hamadi, H.M. [Kuwait Univ., Safat (Kuwait). Dept. of Information Science

    2008-07-01

    Voltage flicker is a significant power quality problem that is typically caused by rapidly occurring voltage fluctuations caused by sudden and large increases in the load current. This paper presented a survey of flicker analysis for an electric arc furnace. In particular, it presented a tracking technique of two-frequency voltage flicker sinusoidal signals occurring in electric power systems. The flicker signals were estimated using a voltage flicker model consisting of two distinct flicker frequencies, notably amplitudes and phases. The voltage flicker signals were modeled as a discrete time linear dynamic system with flicker voltage parameters. A discrete time linear dynamic state space model was adapted for a Kalman filter to estimate the flicker parameters. The Kalman filtering technique was used in conjunction with fuzzy rule-based logic to estimate the instantaneous voltage flicker magnitudes, frequencies and phases of the 2 flicker signals. The system and measurement covariance matrices were tuned using a set of fuzzy logic rules to adjust their noise levels. The simulation results showed the convergence of the estimated parameters using Kalman filter iterations. The values of estimated parameters were very close to the original values. 14 refs., 2 tabs., 5 figs.

  19. Fuzzy logic controllers: A knowledge-based system perspective

    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.

  20. Diagnosis aiding in Regulation Thermography using Fuzzy Logic

    Knaf, H.; Lang, P.; S., Zeiser.

    2003-01-01

    The objective of the present article is to give an overview of an application of Fuzzy Logic in Regulation Thermography, a method of medical diagnosis support. An introduction to this method of the complementary medical science based on temperature measurements – so-called thermograms – is provided. The process of modelling the physician’s thermogram evaluation rules using the calculus of Fuzzy Logic is explained.

  1. Fuzzy Logic, Informativeness and Bayesian Decision-Making Problems

    Golubtsov, P. V.; Moskaliuk, S. S.

    2006-01-01

    This paper develops a category-theoretic approach to uncertainty, informativeness and decision-making problems. It is based on appropriate first order fuzzy logic in which not only logical connectives but also quantifiers have fuzzy interpretation. It is shown that all fundamental concepts of probability and statistics such as joint distribution, conditional distribution, etc., have meaningful analogs in new context. This approach makes it possible to utilize rich conceptual experience of sta...

  2. Application of fuzzy logic controller in a nuclear power plant

    Possible application of a fuzzy logic controller in a PWR nuclear power plant is investigated in this paper. A simplified model of the complex dynamics of the system is used for simulation purposes. The goal is to keep average coolant temperature as close as possible to a desired (but changing) reference value. The position of the control rods is selected as control variable. Simulation results demonstrate the possibility of using fuzzy logic controllers in load following control of nuclear power plants

  3. Fuzzy logic based ELF magnetic field estimation in substations

    This paper examines estimation of the extremely low frequency magnetic fields (MF) in the power substation. First, the results of the previous relevant research studies and the MF measurements in a sample power substation are presented. Then, a fuzzy logic model based on the geometric definitions in order to estimate the MF distribution is explained. Visual software, which has a three-dimensional screening unit, based on the fuzzy logic technique, has been developed. (authors)

  4. Control of Overhead Crane while Using Fuzzy Logic Dispatcher

    Alavi Said Anaitollah

    2008-01-01

    Full Text Available The objective of a crane control system is to ensure movement of a load with minimum load swing. A classical Proportional-Derivative (PID controller is not the best solution of this problem due to the non-linear system. The paper presents a designing procedure of fuzzy logic controller on the basis of fuzzy logic and theory of a rough controller. Comparative indices of both controllers obtained as a result of digital modeling are given in the paper.

  5. Fuzzy Logic and Intelligent Technologies in Nuclear Science

    FLINS is the acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science. The main task for FLINS is to solve intricate problems pertaining to the nuclear environment by using modern technologies as additional tools and to bridge the gap between novel technologies and the industrial nuclear world. In 1997, major efforts went to the specific prototyping of Fuzzy Logic Control of SCK-CEN's BR1 research Reactor. Progress and achievements are reported

  6. Some Fuzzy Logic Based Methods to Deal with Sensorial Information

    Bernadette Bouchon-Meunier

    2004-01-01

    Sensorial information is very difficult to elicit, to represent and to manage because of its complexity. Fuzzy logic provides an interesting means to deal with such information, since it allows us to represent imprecise, vague or incomplete descriptions, which are very common in the management of subjective information. Aggregation methods proposed by fuzzy logic are further useful to combine the characteristics of the various components of sensorial information.

  7. Genetic Algorithm Tuned Fuzzy Logic Controller for Rotary Inverted Pendulum

    Tzu-Chun Kuo; Ying-Jeh Huang; Ping-Chou Wu

    2013-01-01

    In this study, a Genetic Algorithm (GA) is proposed to search for the optimal input membership functions of the fuzzy logic controller. With the optimal membership function, the fuzzy logic controller can efficiently control a rotary inverted pendulum. The advantage of the proposed method is tuning the parameters of membership functions automatically rather than tuning them manually. In genetic algorithm, these parameters are converted to a chromosome which is encoded into a binary string. Be...

  8. Towards a Fuzzy Description Logic for the Semantic Web

    Straccia, Umberto

    2004-01-01

    In this paper we present a fuzzy version of ${cal SHOIN}(D)$, the corresponding Description Logic of the ontology description language OWL DL. We show that the representation and reasoning capabilities of fuzzy ${cal SHOIN}(D)$ go clearly beyond classical ${cal SHOIN}(D)$. We present its syntax and semantics. Interesting features are that concrete domains are fuzzy and entailment and subsumption relationships may hold to some degree in the unit interval $[0,1]$.

  9. Inverted Pendulum Design with Hardware Fuzzy Logic Controller

    Eric Minnaert; Brian Hemmelman; Dan Dolan

    2008-01-01

    An inverted pendulum robot has been designed and built using a fuzzy logic controller implemented in a Field Programmable Gate Array (FPGA). The Mamdani fuzzy controller has been implemented using integer numbers to simplify its construction and improve system throughput. An accelerometer and rate gyroscope are used along with a complementary filter to monitor the state of the robot. Using angular velocity and angle error the fuzzy controller can successfully balance the inverted pendulum robot.

  10. Fuzzy Logic and Preference Uncertainty in Non-market Valuation

    Lili Sun; G. Cornelis van Kooten

    2005-01-01

    In seeking to value environmental amenities and public goods, individuals often have trouble trading off the (vague) amenity or good against a monetary measure. Valuation in these circumstances can best be described as fuzzy in terms of the amenity valued, perceptions of property rights, and the numbers chosen to reflect values. In this paper, we apply fuzzy logic to contingent valuation, employing a fuzzy clustering approach for incorporating preference uncertainty obtained from a follow-up ...

  11. An Observation on (un)decidable Theories in Fuzzy Logics

    Hájek, Petr

    Linz : Johannes Kepler Universität, 2009 - (Bodenhofer, U.; De Baets, B.; Klement, E.; Saminger-Platz, S.). s. 55-56 [Linz Seminar on Fuzzy Set Theory /30./. 03.02.2009-07.02.2009, Linz] Institutional research plan: CEZ:AV0Z10300504 Keywords : (un)decidable theories * fuzzy logics Subject RIV: BA - General Mathematics

  12. Fuzzy logic-based battery charge controller

    Photovoltaic power system are generally classified according to their functional and operational requirements, their component configurations, and how the equipment is connected to other power sources and electrical loads, photovoltaic systems can be designed to provide DC and/or AC power service, can operate interconnected with or independent of the utility grid, and can be connected with other energy sources and energy storage systems. Batteries are often used in PV systems for the purpose of storing energy produced by the PV array during the day, and to supply it to electrical loads as needed (during the night and periods of cloudy weather). The lead acid battery, although know for more than one hundred years, has currently offered the best response in terms of price, energetic efficiency and lifetime. The main function of controller or regulator in PV system is too fully charge the battery without permitting overcharge while preventing reverse current flow at night. If a no-self-regulating solar array is connected to lead acid batteries with no overcharge protection, battery life will be compromised. Simple controllers contain a transistor that disconnects or reconnects the PV in the charging circuit once a pre-set voltage is reached. More sophisticated controllers utilize pulse with modulation (PWM) to assure the battery is being fully charged. The first 70% to 80% of battery capacity is easily replaced, but the last 20% to 30% requires more attention and therefore more complexity. This complexity is avoided by using a skilled operators experience in the form of the rules. Thus a fuzzy control system seeks to control the battery that cannot be controlled well by a conventional control such as PID, PD, PI etc., due to the unavailability of an accurate mathematical model of the battery. In this paper design of an intelligent battery charger, in which the control algorithm is implemented with fuzzy logic is discussed. The digital architecture is implemented with

  13. Qualitative assessment of environmental impacts through fuzzy logic

    The vagueness of many concepts usually utilized in environmental impact studies, along with frequent lack of quantitative information, suggests that fuzzy logic can be applied to carry out qualitative assessment of such impacts. This paper proposes a method for valuing environmental impacts caused by projects, based on fuzzy sets theory and methods of approximate reasoning. First, impacts must be described by a set of features. A linguistic variable is assigned to each feature, whose values are fuzzy sets. A fuzzy evaluation of environmental impacts is achieved using rule based fuzzy inference and the estimated fuzzy value of each feature. Generalized modus ponens has been the inference method. Finally, a crisp value of impact is attained by aggregation and defuzzification of all fuzzy results

  14. Adaptive MIMO Fuzzy Compensate Fuzzy Sliding Mode Algorithm: Applied to Second Order Nonlinear System

    Farzin Piltan, N. Sulaiman, Payman Ferdosali, Mehdi Rashidi, Zahra Tajpeikar

    2011-12-01

    Full Text Available This research is focused on proposed adaptive fuzzy sliding mode algorithms with the adaptation lawsderived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based onthe Lyapunov method. Adaptive MIMO fuzzy compensate fuzzy sliding mode method design a MIMO fuzzysystem to compensate for the model uncertainties of the system, and chattering also solved by linearsaturation method. Since there is no tuning method to adjust the premise part of fuzzy rules so wepresented a scheme to online tune consequence part of fuzzy rules. Classical sliding mode control isrobust to control model uncertainties and external disturbances. A sliding mode method with a switchingcontrol low guarantees the stability of the certain and/or uncertain system, but the addition of the switchingcontrol low introduces chattering into the system. One way to reduce or eliminate chattering is to insert aboundary layer method inside of a boundary layer around the sliding surface. Classical sliding modecontrol method has difficulty in handling unstructured model uncertainties. One can overcome this problemby combining a sliding mode controller and artificial intelligence (e.g. fuzzy logic. To approximate a timevaryingnonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This largenumber of fuzzy rules will cause a high computation load. The addition of an adaptive law to a fuzzy slidingmode controller to online tune the parameters of the fuzzy rules in use will ensure a moderatecomputational load. The adaptive laws in this algorithm are designed based on the Lyapunov stabilitytheorem. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov.

  15. A FUZZY LOGIC CONTROLLERFORA TWO-LINK FUNCTIONAL MANIPULATOR

    Sherif Kamel Hussein

    2014-12-01

    Full Text Available This paper presents a new approach for designing a Fuzzy Logic Controller "FLC"for a dynamically multivariable nonlinear coupling system. The conventional controller with constant gains for different operating points may not be sufficient to guarantee satisfactory performance for Robot manipulator. The Fuzzy Logic Controller utilizes the error and the change of error as fuzzy linguistic inputs to regulate the system performance. The proposed controller have been developed to simulate the dynamic behavior of A Two-Link Functional Manipulator. The new controller uses only the available information of the inputoutput for controlling the position and velocity of the robot axes of the motion of the end effectors

  16. Optimized and Self-Organized Fuzzy Logic Controller for pH Neutralization Process

    Parikshit Kishor Singh

    2013-11-01

    Full Text Available To conform to strict environmental safety regulations, pH control is used in many industrial applications. For this purpose modern process industries are increasingly relying on intelligent and adaptive control strategies. On one hand intelligent control strategies try to imitate human way of thinking and decision making using artificial intelligence (AI based techniques such as fuzzy logic whereas on the other hand adaptive mechanism ensures adjusting of the controller parameters. A self-organized fuzzy logic controller (SOFLC is intelligent in nature and adapts its performance to meet the figure of merit. This paper presents an optimized SOFLC for pH control using performance correction table. The fuzzy adaptation mechanism basically involves a penalty for the output membership functions if the controller performance is poor. The evolutionary genetic algorithm (GA is used for optimization of input-output scaling factors of the conventional fuzzy logic controller (FLC as well as elements of the fuzzy performance correction table. The resulting optimized SOFLC is compared with optimized FLC for servo and regulatory control. Comparison indicate superior performance of SOFLC over FLC in terms of much reduced integral of squared error (ISE, maximum overshoot and undershoot, and increased speed of response.

  17. Predictive Condition Monitoring of Induction Motor Bearing Using Fuzzy Logic

    Prof. Rakeshkumar A. Patel

    2012-10-01

    Full Text Available Induction motor is critical component in industrial processes and is frequently integrated in commercially available equipment. Safety, reliability, efficiency and performance are the major concerns of induction motor applications. Due to high reliability requirements and cost of breakdown, condition monitoring, diagnosis and Protection increasing importance. Protection of an induction motor (IM against possible problems, such as stator faults, rotor faults and mechanical faults, occurring in the course of its operation is very important, because it is very popular in industries. Bearing fault is well known mechanical fault of IM.41�0faults related to bearing in IM. To avoid break down of IM condition monitoring of motor bearing condition is very important during the normal operation. Various classical and AI techniques like fuzzy logic, neural network, neuro-fuzzy are used for condition monitoring and diagnosis of IM. Among the above mentioned AI techniques, Fuzzy logic is the best technique for condition monitoring and diagnosis of IM bearing condition. Therefore, the present paper focuses on fuzzy logic technique. In this paper Fuzzy logic is design for the condition monitoring and diagnosis of induction motor bearing condition using motor current and speed. After applying Fuzzy logic it has been seen that continuous monitoring of the current and speed values of the motor conditioned monitoring and diagnosis of induction motor bearing condition can be done.

  18. A fuzzy logic cooperative MAC for MANET

    WANG Zhao-xiang; XIA Hai-lun; DING Wei

    2008-01-01

    In both wireless local area networks (WLAN) andmobile ad hoc networks(MANET), the IEEE 802.11e mediumaccess control (MAC) protocol is proposed for an effectivequality of service (QoS) solution. A number of studies havebeen done to enhance the performance of 802.11e in MANETby independently adjusting contention window (CW) size ofeach access category(AC) in every node. However, without thecooperation between the high priority flows and lower priorityflows, the QoS goal of high priority flows cannot achieveeffectively. In this article, a fuzzy logic based cooperative MACprotocol (FLCMAC) is proposed to cooperate amongst networkflows and dynamically adjust access probability of each lowpriority flow affecting the high priority flows to satisfy theftQoS requirement. The simulation results indicate that comparedto the enhanced distributed channel access (EDCA) scheme of802.11e, the FLCMAC consistently excels, in terms ofthroughput and delay under moderate and heavy backgroundtraffic both in single-hop and multi-hop scenarios.

  19. Can fuzzy logic bring complex problems into focus? Modeling imprecise factors in environmental policy

    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

  20. Fuzzy Logic Controller based on geothermal recirculating aquaculture system

    Hanaa M. Farghally

    2014-01-01

    Full Text Available One of the most common uses of geothermal heat is in recirculation aquaculture systems (RAS where the water temperature is accurately controlled for optimum growing conditions for sustainable and intensive rearing of marine and freshwater fish. This paper presents a design for RAS rearing tank and brazed heat exchanger to be used with geothermal energy as a source of heating water. The heat losses from the RAS tank are calculated using Geo Heat Center Software. Then a plate type heat exchanger is designed using the epsilon – NTU analysis method. For optimal growth and abundance of production, a Fuzzy Logic control (FLC system is applied to control the water temperature (29 °C. A FLC system has several advantages over conventional techniques; relatively simple, fast, adaptive, and its response is better and faster at all atmospheric conditions. Finally, the total system is built in MATLAB/SIMULINK to study the overall performance of control unit.

  1. Adaptive Neuro-Fuzzy Model Tuning for Early-Warning of Financial Crises

    Erika SZTOJANOV; Stamatescu, Grigore

    2015-01-01

    The paper introduces an early-warning method using multiple financial crises indicators which outputs relevant alerts compared to a precise indication of crisis inception, serving as an effective tool for decision makers. By leveraging fuzzy logic techniques, we design a multi-level fuzzy decision support system based on the evolution of credit growth, housing prices and GDP gap. A neuro-fuzzy approach allows fine tuning of the individual fuzzy sub-systems towards adaptive structures which ca...

  2. Multi Model Criteria for the Estimation of Road Traffic Congestion from Traffic Flow Information Based on Fuzzy Logic

    K.Ram Mohan Rao; P. L. N. Raju; Hari Shankar

    2012-01-01

    In this study, the road traffic congestion of Dehradun city is evaluated from traffic flow information using fuzzy techniques. Three different approaches namely Sugeno, Mamdani models which are manually tuned techniques, and an Adaptive Neuo-Fuzzy Inference System (ANFIS) which an automated model decides the ranges and parameters of the membership functions using grid partition technique, based on fuzzy logic. The systems are designed to human’s feelings on inputs and output levels. There are...

  3. Fuzzy logic controller using different inference methods

    In this paper the design of fuzzy controllers by using different inference methods is introduced. Configuration of the fuzzy controllers includes a general rule-base which is a collection of fuzzy PI or PD rules, the triangular fuzzy data model and a centre of gravity defuzzification algorithm. The generalized modus ponens (GMP) is used with the minimum operator of the triangular norm. Under the sup-min inference rule, six fuzzy implication operators are employed to calculate the fuzzy look-up tables for each rule base. The performance is tested in simulated systems with MATLAB/SIMULINK. Results show the effects of using the fuzzy controllers with different inference methods and applied to different test processes

  4. Adaptive fuzzy controllers based on variable universe

    李洪兴

    1999-01-01

    Adaptive fuzzy controllers by means of variable universe are proposed based on interpolation forms of fuzzy control. First, monotonicity of control rules is defined, and it is proved that the monotonicity of interpolation functions of fuzzy control is equivalent to the monotonicity of control rules. This means that there is not any contradiction among the control rules under the condition for the control rules being monotonic. Then structure of the contraction-expansion factor is discussed. At last, three models of adaptive fuzzy control based on variable universe are given which are adaptive fuzzy control model with potential heredity, adaptive fuzzy control model with obvious heredity and adaptive fuzzy control model with successively obvious heredity.

  5. INDIRECT ACCELERATED ADAPTIVE FUZZY CONTROLLER

    ZHU Liye; FANG Yuan; ZHANG Weidong

    2008-01-01

    According to a type of normal nonlinear system, an indirect adaptive fuzzy (IAF) controller has been applied to those systems where no accurate mathematical models of the systems under control are available. To satisfy with system performance, an indirect accelerated adaptive fuzzy (IAAF) controller is proposed, and its general form is presented. The general form IAAF controller ensures necessary control criteria and system's global stability using Lyapunov Theorem. It has been proved that the close-loop system error converges to a small neighborhood of equilibrium point. The optimal IAAF controller is derived to guarantee the process's shortest settling time. Simulation results indicate the IAAF controller make the system more stable, accurate, and fast.

  6. Navigating a Mobile Robot Across Terrain Using Fuzzy Logic

    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.

  7. Driver's Behavior Modeling Using Fuzzy Logic

    Sehraneh Ghaemi; Sohrab Khanmohammadi; Mohammadali Tinati

    2010-01-01

    In this study, we propose a hierarchical fuzzy system for human in a driver-vehicle-environment system to model takeover by different drivers. The driver's behavior is affected by the environment. The climate, road and car conditions are included in fuzzy modeling. For obtaining fuzzy rules, experts' opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. Also the precision, age and driving individuality are ...

  8. A fuzzy logic controller for an autonomous mobile robot

    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.

  9. FUZZY LOGIC BASED ENERGY EFFICIENT PROTOCOL IN WIRELESS SENSOR NETWORKS

    Zhan Wei Siew

    2012-12-01

    Full Text Available Wireless sensor networks (WSNs have been vastly developed due to the advances in microelectromechanical systems (MEMS using WSN to study and monitor the environments towards climates changes. In environmental monitoring, sensors are randomly deployed over the interest area to periodically sense the physical environments for a few months or even a year. Therefore, to prolong the network lifetime with limited battery capacity becomes a challenging issue. Low energy adaptive cluster hierarchical (LEACH is the common clustering protocol that aim to reduce the energy consumption by rotating the heavy workload cluster heads (CHs. The CHs election in LEACH is based on probability model which will lead to inefficient in energy consumption due to least desired CHs location in the network. In WSNs, the CHs location can directly influence the network energy consumption and further affect the network lifetime. In this paper, factors which will affect the network lifetime will be presented and the demonstration of fuzzy logic based CH selection conducted in base station (BS will also be carried out. To select suitable CHs that will prolong the network first node dies (FND round and consistent throughput to the BS, energy level and distance to the BS are selected as fuzzy inputs.

  10. A Fuzzy EL Descritpion Logic with Crisp Roles and Fuzzy Aggregation for Web Consulting

    Vojtáš, Peter

    Paris: Editions EDK, 2006 - (Bouchon-Meunier, B.; Yager, R.), s. 1834-1841 ISBN 2-84254-112-X. [IPMU 2006 /11./. Paris (FR), 02.07.2006-07.07.2006] R&D Projects: GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy description logic with extential description * fuzzy concept * user preference query * fuzzy aggregation operator * instance problem Subject RIV: BA - General Mathematics

  11. Fuzzy Logic as a Tool for Assessing Students’ Knowledge and Skills

    Michael Gr. Voskoglou

    2013-05-01

    Full Text Available 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 model for assessing student groups’ knowledge and skills. In this model the students’ characteristics under assessment (knowledge of the subject matter, problem solving skills and analogical reasoning abilities are represented as fuzzy subsets of a set of linguistic labels characterizing their performance, and the possibilities of all student profiles are calculated. In this way, a detailed quantitative/qualitative study of the students’ group performance is obtained. The centroid method and the group’s total possibilistic uncertainty are used as defuzzification methods in converting our fuzzy outputs to a crisp number. According to the centroid method, the coordinates of the center of gravity of the graph of the membership function involved provide a measure of the students’ performance. Techniques of assessing the individual students’ abilities are also studied and examples are presented to illustrate the use of our results in practice.

  12. Fuzzy logic control and optimization system

    Lou, Xinsheng

    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.

  13. Strategy missile control system design using adaptive fuzzy control based on Popov stability criterion

    Zhang, Jianling; An, Jinwen; Wang, Mina

    2005-11-01

    This paper describes the application and simulation of an adaptive fuzzy controller for a missile model. The fuzzy control system is tested using different values of fuzzy controller correctional factor on a nonlinear missile model. It is shown that the self-tuning fuzzy controller is well suited for controlling the pitch loop of the missile control system with air turbulence and parameter variety. The research shows that the Popov stability criterion could successfully guarantee the stability of the fuzzy system. It provides a good method for the design of missile control system. Simulation results suggest significant benefits from fuzzy logic in control task for missile pitch loop control.

  14. Neutral network and fuzzy logic based grate control; Roststyrning med neutrala naetverk och fuzzy logic

    Ramstroem, Erik [TPS Termiska Processer AB, Nykoeping (Sweden)

    2002-04-01

    Grate-control is a complex task in many ways. The relations between controlled variables and the values they depend on are mostly unknown. Research projects are going on to create grate models based on physical laws. Those models are too complex for control implementation. The evaluation time is to long for control use. Another fundamental difficulty is that the relationships are none linear. That is, for a specific change in control value, the change in controlled value depends on the original size of control value, process disturbances and controlled values. There are extensive theories for linear process control. Non-linear control theory is used in robotic applications, but not in process and combustion control. The aim of grate control is to use as much of the grate area as possible, without having unburned material in ash. The outlined strategy is: To keep the position of the final bum out zone constant and its extension controlled. The control variables should be primary airflow, distribution of primary air, and fuel flow. Disturbances that should be measured are the fuel moisture content, the temperature of primary air and the grate temperature under the fuel bed. Technologies used are, fuzzy-logic and neural networks. A combination of booth could be used as well as any of them separately. A Fuzzy-logic controller acts as a computerised operator. Rules are specified with 'if - then' thesis. An example of that is: - if temperature is low, then close the valve The boundaries between the rules are made fuzzy. That makes it possible for the temperature to be just a bit low, which makes the valve open a bit. A lot of rules are created so that the controller knows what to do in every situation. Neural networks are sort of multi dimensional curves, with arbitrary degrees of freedom. The nets are used to predict future process values from measured ones. The model is evaluated from collected data. Parameters are adjusted for best correspondence between

  15. Modeling Academic Performance Evaluation Using Soft Computing Techniques: A Fuzzy Logic Approach

    Ramjeet Singh Yadav; Vijendra Pratap Singh

    2011-01-01

    We have proposed a Fuzzy Expert System (FES) for student academic performance evaluation based on fuzzy logic techniques. A suitable fuzzy inference mechanism and associated rule has been discussed. It introduces the principles behind fuzzy logic and illustrates how these principles could be applied by educators to evaluating student academic performance. Several approaches using fuzzy logic techniques have been proposed to provide a practical method for evaluating student academic performanc...

  16. Modeling Academic Performance Evaluation Using Soft Computing Techniques: A Fuzzy Logic Approach

    Ramjeet Singh Yadav

    2011-02-01

    Full Text Available We have proposed a Fuzzy Expert System (FES for student academic performance evaluation based on fuzzy logic techniques. A suitable fuzzy inference mechanism and associated rule has been discussed. It introduces the principles behind fuzzy logic and illustrates how these principles could be applied by educators to evaluating student academic performance. Several approaches using fuzzy logic techniques have been proposed to provide a practical method for evaluating student academic performance and compare the results (performance with existing tatistical method.

  17. Improved adaptive fuzzy control for MIMO nonlinear time-delay systems

    2011-01-01

    This paper presents an improved observer-based indirect adaptive fuzzy control scheme for multiinput-multioutput (MIMO) nonlinear time-delay systems.The control scheme synthesizes adaptive fuzzy control with adaptive fuzzy identification.An observer is designed to observe the system state,and an identifier is developed to identify the unknown parts of the system.The update laws for parameters utilize two types of errors in the adaptive time-delay fuzzy logic systems,the observation error and the identificat...

  18. Completed Optimised Structure of Threonine Molecule by Fuzzy Logic Modelling

    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.

  19. The design of thermoelectric footwear heating system via fuzzy logic.

    Işik, Hakan; Saraçoğlu, Esra

    2007-12-01

    In this study, Heat Control of Thermoelectric Footwear System via Fuzzy Logic has been implemented in order to use efficiently in cold weather conditions. Temperature control is very important in domestic as well as in many industrial applications. The final product is seriously affected from the changes in temperature. So it is necessary to reach some desired temperature points quickly and avoid large overshoot. Here, fuzzy logic acts an important role. PIC 16F877 microcontroller has been designed to act as fuzzy logic controller. The designed system provides energy saving and has better performance than proportional control that was implemented in the previous study. The designed system takes into consideration so appropriate parameters that it can also be applied to the people safely who has illnesses like diabetes, etc. PMID:18041286

  20. On Theories and Models in Fuzzy Predicate Logics

    Hájek, Petr; Cintula, Petr

    Linz : Johannes Kepler Universität, 2005 - (Gottwald, S.; Hájek, P.; Höhle, U.; Klement, E.). s. 55-58 [Linz Seminar on Fuzzy Set Theory /26./. 01.02.2005-05.02.2005, Linz] R&D Projects: GA AV ČR IAA100300503 Institutional research plan: CEZ:AV0Z10300504 Keywords : predicate fuzzy logic * conservative extensions * wittnessed models Subject RIV: BA - General Mathematics

  1. Modeling uncertainty in computerized guidelines using fuzzy logic.

    Jaulent, M. C.; Joyaux, C.; Colombet, I.; Gillois, P.; Degoulet, P.; Chatellier, G.

    2001-01-01

    Computerized Clinical Practice Guidelines (CPGs) improve quality of care by assisting physicians in their decision making. A number of problems emerges since patients with close characteristics are given contradictory recommendations. In this article, we propose to use fuzzy logic to model uncertainty due to the use of thresholds in CPGs. A fuzzy classification procedure has been developed that provides for each message of the CPG, a strength of recommendation that rates the appropriateness o...

  2. Fuzzy Logic in Clinical Practice Decision Support Systems

    Warren, Jim; Beliakov, Gleb; Zwaag, van der, B.J.

    2000-01-01

    Computerized clinical guidelines can provide significant benefits to health outcomes and costs, however, their effective implementation presents significant problems. Vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. This paper discusses sources of fuzziness in clinical practice guidelines. We consider how ...

  3. Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories

    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.

  4. Fuzzy Logic and Neural Networks - a Glimpse of the Future

    Manley, Raymond

    2015-01-01

    In 1965 Lofti Zadeh published his paper on fuzzy set theory , putting it forward as a way of more closely realising the human thought process. Many systems developed to aid human activities have been based on definitive , yes/no, type decision making processes. An example is the way all computers are based on the binary logic system where only two possible and separate logic levels are allowed, a logic 1 or logic 0. However, we know from everyday experience that humans think in terms of vague...

  5. Coordinated signal control for arterial intersections using fuzzy logic

    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.

  6. Robust position control of induction motor using fuzzy logic control

    In recent years, fuzzy logic or fuzzy set theory has reveived attention of a number of researchers in the area of power electronics and motion control. The paper describes a vector-controlled induction motor position servo drive where fuzzy control is used to get robustness against parameter variation and load torque disturbance effects. Both coarse and fine control with the help of look-up rule tables are used to improve transient response and system settling time. The performance characteristics are then compared with those of proportional-integral(PI) control. The simulation results clearly indicate the superiority of fuzzy control with larger number of rules. The fuzzy controller was implemented with a 16-bit microprocessor and tested in laboratory on a 3-hp IGBT inverter induction motor drive system. The test results verify the simulation performance. (Author)

  7. Fuzzy logic control for active bus suspension system

    In this study an active controller is presented for vibration suppression of a full-bus suspension model that use air spring. Since the air spring on the full-bus model may face different working conditions, auxiliary chambers have been designed. The vibrations, caused by the irregularities of the road surfaces, are tried to be suppressed via a multi input-single output fuzzy logic controller. The effect of changes in the number of auxiliary chambers on the vehicle vibrations is also investigated. The numerical results demonstrate that the presented fuzzy logic controller improves both ride comfort and road holding.

  8. Mapping Shape Geometry And Emotions Using Fuzzy Logic

    Achiche, Sofiane; Ahmed, Saeema

    2008-01-01

    would evoke a certain emotion (aggressive, soft, heavy, friendly, etc.), each would most likely interpret the emotion with a different set of geometric features and shapes. In this paper the authors propose an approach to formalize the relationship between geometric information of a 3D object and the...... intended emotion using fuzzy logic. To achieve this; 3D objects (shapes) created by design engineering students to match a set of words/emotions were analyzed. The authors identified geometric information as inputs of the fuzzy model and developed a set of fuzzy if/then rules to map the relationships...

  9. Foundations of fuzzy logic and semantic web languages

    Straccia, Umberto

    2013-01-01

    Managing vagueness/fuzziness is starting to play an important role in Semantic Web research, with a large number of research efforts underway. Foundations of Fuzzy Logic and Semantic Web Languages provides a rigorous and succinct account of the mathematical methods and tools used for representing and reasoning with fuzzy information within Semantic Web languages. The book focuses on the three main streams of Semantic Web languages: Triple languages RDF and RDFS Conceptual languages OWL and OWL 2, and their profiles OWL EL, OWL QL, and OWL RL Rule-based languages, such as SWRL and RIF Written b

  10. Adaptation of the FPGA to Logic Failures

    Tyurin S.F.; Grekov A.V.; Gromov O.A.

    2013-01-01

    The paper proposes the restoration of logic programmable logic integrated circuits such as FPGA (field-programmable gate array) for critical applications by adapting to failures of logic elements. The principle of adaptation FPGA is to switch to the remaining functionality of the LUT (Look Up Table), with the possibility of hardware and software they use in the event of hardware failure after massive failures. Asked to ensure the preservation of the basis in the sense of Post logic functions ...

  11. Skolem and Herbrand theorems for uninorm-based fuzzy logics

    Cintula, Petr; Metcalfe, G.

    Linz : Johannes Kepler Universität, 2014 - (Flaminio, T.; Godo, L.; Gottwald, S.; Klement, E.). s. 29-33 [Linz Seminar on Fuzzy Set Theory /35./. 18.02.2014-22.02.2014, Linz] R&D Projects: GA ČR GAP202/10/1826 Institutional support: RVO:67985807 Keywords : substructural logics * residuated lattices * Herbrand theorem * Skolemization * predicate logics Subject RIV: BA - General Mathematics

  12. Automated cloud classification with a fuzzy logic expert system

    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.

  13. Noise-based logic: Binary, multi-valued, or fuzzy, with optional superposition of logic states

    Kish, Laszlo B

    2008-01-01

    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 nonexistent because the logic value is an AC signal. A similar logic system can be built with orthogonal sinu...

  14. Genetic rule induction in the design of computed-torque/fuzzy-logic controllers for robotic manipulators

    Porter, B; Zadeh, NN

    1996-01-01

    In this paper, genetic algorithms are used to design computed-torque/fuzzy-logic controllers for robotic manipulators. It is shown that this use of genetic algorithms provides a very effective means of determining the optimal set of fuzzy rules as well as the optimal domains of the associated fuzzy sets of the fuzzy-logic components of such controllers. It is demonstrated that these computed-torque/fuzzy-logic controllers are more robust than computed-torque/fuzzy-logic controllers in which o...

  15. A practical introduction to fuzzy logic using LISP

    Argüelles Mendez, Luis

    2016-01-01

    This book makes use of the LISP programming language to provide readers with the necessary background to understand and use fuzzy logic to solve simple to medium-complexity real-world problems. It introduces the basics of LISP required to use a Fuzzy LISP programming toolbox, which was specifically implemented by the author to “teach” the theory behind fuzzy logic and at the same time equip readers to use their newly-acquired knowledge to build fuzzy models of increasing complexity. The book fills an important gap in the literature, providing readers with a practice-oriented reference guide to fuzzy logic that offers more complexity than popular books yet is more accessible than other mathematical treatises on the topic. As such, students in first-year university courses with a basic tertiary mathematical background and no previous experience with programming should be able to easily follow the content. The book is intended for students and professionals in the fields of computer science and engineering, ...

  16. Intelligent control-I: review of fuzzy logic and fuzzy set theory

    In the past decade or so, fuzzy systems have supplanted conventional technologies in many engineering systems, in particular in control systems and pattern recognition. Fuzzy logic has found applications in a variety of consumer products e.g. washing machines, camcorders, digital cameras, air conditioners, subway trains, cement kilns and many others. The fuzzy technology is also being applied in information technology, where it provides decision-support and expert systems with powerful reasoning capabilities. Fuzzy sets, introduced by Zadeh in 1965 as a mathematical way to represent vagueness in linguistics, can be considered a generalisation of classical set theory. Fuzziness is often confused with probability. This lecture will introduce the principal concepts and mathematical notions of fuzzy set theory. (author)

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

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

    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

  18. Towards Metamathematics of Weak Arithmetics over Fuzzy Logic

    Hájek, Petr

    2011-01-01

    Roč. 19, č. 3 (2011), s. 467-475. ISSN 1367-0751 R&D Projects: GA AV ČR IAA100300503 Institutional research plan: CEZ:AV0Z10300504 Keywords : weak arithmetic s * mathematical fuzzy logic * Gödel’s theorem * essential undecidability Subject RIV: BA - General Mathematics Impact factor: 0.913, year: 2011

  19. Professional Learning: A Fuzzy Logic-Based Modelling Approach

    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…

  20. Self-learning fuzzy logic controllers based on reinforcement

    This paper proposes a new method for learning and tuning Fuzzy Logic Controllers. The self-learning scheme in this paper is composed of Bucket-Brigade and Genetic Algorithm. The proposed method is tested on the cart-pole system. Simulation results show that our approach has good learning and control performance

  1. Use of fuzzy logic in signal processing and validation

    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

  2. Bicycle Frame Prediction Techniques with Fuzzy Logic Method

    Rafiuddin Syam

    2015-03-01

    Full Text Available In general, an appropriate size bike frame would get comfort to the rider while biking. This study aims to predict the simulation system on the bike frame sizes with fuzzy logic. Testing method used is the simulation test. In this study, fuzzy logic will be simulated using Matlab language to test their performance. Mamdani fuzzy logic using 3 variables and 1 output variable intake. Triangle function for the input and output. The controller is designed in the type mamdani with max-min composition and the method deffuzification using center of gravity method. The results showed that height, inseam and Crank Size generating appropriate frame size for the rider associated with comfort. Has a height range between 142 cm and 201 cm. Inseam has a range between 64 cm and 97 cm. Crank has a size range between 175 mm and 180 mm. The simulation results have a range of frame sizes between 13 inches and 22 inches. By using the fuzzy logic can be predicted the size frame of bicycle suitable for the biker.

  3. On Theories and Models in Fuzzy Predicate Logics

    Hájek, Petr; Cintula, Petr

    2006-01-01

    Roč. 71, č. 3 (2006), s. 863-880. ISSN 0022-4812 R&D Projects: GA AV ČR IAA100300503 Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy logic * model theory * witnessed models * conservative extension * completeness theorem Subject RIV: BA - General Mathematics Impact factor: 0.664, year: 2006

  4. Genetic Algorithm Tuned Fuzzy Logic Controller for Rotary Inverted Pendulum

    Tzu-Chun Kuo

    2013-06-01

    Full Text Available In this study, a Genetic Algorithm (GA is proposed to search for the optimal input membership functions of the fuzzy logic controller. With the optimal membership function, the fuzzy logic controller can efficiently control a rotary inverted pendulum. The advantage of the proposed method is tuning the parameters of membership functions automatically rather than tuning them manually. In genetic algorithm, these parameters are converted to a chromosome which is encoded into a binary string. Because the membership functions are symmetric to zero, the length of each chromosome could be reduced by half. The computation time will also be shorter with the shorter chromosomes. Moreover, the roulette wheel selection is chosen as reproduction operator and one-point crossover operator and random mutation operator are also used. After the genetic algorithm completes searching for optimal parameters, the optimal membership function will be introduced to the fuzzy logic controller. Finally, simulation results show that the proposed GA-tuned fuzzy logic controller is effective for the rotary inverted pendulum control system with robust stabilization capability.

  5. Autonomous vehicle motion control, approximate maps, and fuzzy logic

    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.

  6. Towards Evaluation Games for Fuzzy Logics. Chapter 6

    Cintula, Petr; Majer, Ondrej

    New York: Springer Science+ Business Media B.V, 2009 - (Majer, O.; Pietarien, A.; Tulenheimo, T.), s. 117-138. (Logic, Epistemology , and the Unity of Science. 15). ISBN 978-1-4020-9373-9 R&D Projects: GA MŠk(CZ) 1M0545; GA ČR GA401/04/0117 Institutional research plan: CEZ:AV0Z10300504; CEZ:AV0Z90090514 Keywords : game-theoretic semantics * evaluation games * fuzzy logic * Lukasiewicz logic Subject RIV: BA - General Mathematics

  7. Stock and option portfolio using fuzzy logic approach

    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.

  8. Approach to Synchronization Control of Magnetic Bearings Using Fuzzy Logic

    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.

  9. On enhancing on-line collaboration using fuzzy logic modeling

    Leontios J. Hadjileontiadis

    2004-04-01

    Full Text Available Web-based collaboration calls for professional skills and competences to the benefit of the quality of the collaboration and its output. Within this framework, educational virtual environments may provide a means for training upon these skills and in particular the collaborative ones. On the basis of the existing technological means such training may be enhanced even more. Designing considerations towards this direction include the close follow-up of the collaborative activity and provision of support grounded upon a pedagogical background. To this vein, a fuzzy logic-based expert system, namely Collaboration/Reflection-Fuzzy Inference System (C/R-FIS, is presented in this paper. By means of interconnected FISs, the C/R-FIS expert system automatically evaluates the collaborative activity of two peers, during their asynchronous, written, web-based collaboration. This information is used for the provision of adaptive support to peers during their collaboration, towards equilibrium of their collaborative activity. In particular, this enhanced formative feedback aims at diminishing the possible dissonance between the individual collaborative skills by challenging self-adjustment procedures. The proposed model extents the evaluation system of a web-based collaborative tool namely Lin2k, which has served as a test-bed for the C/R-FIS experimental use. Results from its experimental use have proved the potentiality of the proposed model to significantly contribute to the enhancement of the collaborative activity and its transferability to other collaborative learning contexts, such as medicine, environmental engineering, law, and music education.

  10. Modelling of Reservoir Operations using Fuzzy Logic and ANNs

    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, , 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.

  11. A scene-based nonuniformity correction algorithm based on fuzzy logic

    Huang, Jun; Ma, Yong; Fan, Fan; Mei, Xiaoguang; Liu, Zhe

    2015-08-01

    Scene-based nonuniformity correction algorithms based on the LMS adaptive filter are quite efficient to reduce the fixed pattern noise in infrared images. They are famous for their low cost of computation and storage recourses. Unfortunately, ghosting artifacts can be easily introduced in edge areas when the inter-frame motion slows. In this paper, a gated scene-based nonuniformity correction algorithm is proposed. A novel low-pass filter based on the fuzzy logic is proposed to estimate the true scene radiation as the desired signal in the LMS adaptive filter. The fuzzy logic can also evaluate the probability that a pixel and its locals belong to edge areas. Then the update of the correction parameters for the pixels in edge areas can be gated. The experiment results show that our method is reliable and the ghosting artifacts are reduced.

  12. Indirect adaptive fuzzy control for a class of nonlinear discrete-time systems

    2008-01-01

    An indirect adaptive fuzzy control scheme is developed for a class of nonlinear discrete-time systems. In this method, two fuzzy logic systems are used to approximate the unknown functions, and the parameters of membership functions in fuzzy logic systems are adjusted according to adaptive laws for the purpose of controlling the plant to track a reference trajectory. It is proved that the scheme can not only guarantee the boundedness of the input and output of the closed-loop system, but also make the tracking error converge to a small neighborhood of the origin. Simulation results indicate the effectiveness of this scheme.

  13. Fuzzy Logic Water Quality Index and Importance of Water Quality Parameters

    Raman Bai. V

    2009-01-01

    Full Text Available Determination of status of water quality of a river or any other water sources is highly indeterminate. It is necessary to have a competent model to predict the status of water quality and to advice for type of water treatment for meeting different demands. One such model (UNIQ2007 is developed as an application software in water quality engineering. The unit operates in a fuzzy logic mode including a fuzzification engine receiving a plurality of input variables on its input and being adapted to compute membership function parameters. A processor engine connected downstream of the fuzzification unit will produce fuzzy set, based on fuzzy variable viz. DO, BOD, COD, AN, SS and pH. It has a defuzzification unit operative to translate the inference results into a discrete crisp value of WQI. The UNIQ2007 contains a first memory device connected to the fuzzification unit and containing the set of membership functions, a secondary memory device connected to the defuzzification unit and containing the set of crisp value which appear in the THEN part of the fuzzy rules and an additional memory device connected to the defuzzification unit. More advantageously, UINQ2007 is constructed with control elements having dynamic fuzzy logic properties wherein target non-linearity can be input to result in a perfect evaluation of water quality. The development of the fuzzy model with one river system is explained in this paper. Further the model has been evaluated with the data from few rivers in Malaysia, India and Thailand. This water quality assessor probe can provide better quality index or identify the status of river with 90% perfection. Presently, WQI in most of the countries is referring to physic-chemical parameters only due to great efforts needed to quantify the biological parameters. This study ensures a better method to include pathogens into WQI due to superior capabilities of fuzzy logic in dealing with non-linear, complex and uncertain systems.

  14. On language fragments of propositional fuzzy logics

    Haniková, Zuzana

    Helsinki: University of Helsinki, 2015 - (Nevalainen, I.; Virtanen, M.; Seppälä, P.). s. 679-679 [LC 2015. Logic Colloquium. 03.08.2015-08.08.2015, Helsinki] Institutional support: RVO:67985807 Subject RIV: BA - General Mathematics

  15. Fuzzy Logic Water Quality Index and Importance of Water Quality Parameters

    Raman Bai. V; Reinier Bouwmeester; Mohan S

    2009-01-01

    Determination of status of water quality of a river or any other water sources is highly indeterminate. It is necessary to have a competent model to predict the status of water quality and to advice for type of water treatment for meeting different demands. One such model (UNIQ2007) is developed as an application software in water quality engineering. The unit operates in a fuzzy logic mode including a fuzzification engine receiving a plurality of input variables on its input and being adapte...

  16. Fuzzy Logic Decoupled Lateral Control for General Aviation Airplanes

    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.

  17. Self-tuning fuzzy logic nuclear reactor controller

    We present a method for self-tuning of fuzzy logic controllers based on the estimation of the optimum value of the centroids of its output fuzzy set. The method can be implemented on-line and does not require modification of membership functions and control rules. The main features of this method are: the rules are left intact to retain the operator's expertise in the FLC rule base, and the parameters that require any adjustment are identifiable in advance and their number is kept at a minimum. Therefore, the use of this method preserves the control statements in the original form. Results of simulation and actual tests show that this tuning method improves the performance of fuzzy logic controllers in following the desired reactor power level trajectories. In addition, this method demonstrates a similar improvement for power up and power down experiments, based on both simulation and actual case studies. For these experiments, the control rules for the fuzzy logic controller were derived from control statements that expressed the relationships between error, rate of error change, and duration of direction of control rod movements

  18. Fuzzy logic based variable speed wind generation system

    Simoes, M.G. [Sao Paulo Univ., SP (Brazil). Escola Politecnica. PMC - Mecatronica; Bose, B.K. [Tennessee Univ., Knoxville, TN (United States). Dept. of Electrical Engineering; Spiegel, Ronal J. [Environmental Protection Agency, Research Triangle Park, NC (United States). Air and Energy Engineering Research Lab.

    1996-12-31

    This work demonstrates the successful application of fuzzy logic to enhance the performance and control of a variable speed wind generation system. A maximum power point tracker control is performed with three fuzzy controllers, without wind velocity measurement, and robust to wind vortex and turbine torque ripple. A squirrel cage induction generator feeds the power to a double-sided PWM converter system which pumps the power to a utility grid or supplies to an autonomous system. The fuzzy logic controller FLC-1 searches on-line the generator speed so that the aerodynamic efficiency of the wind turbine is optimized. A second fuzzy controller FLC-2 programs the machine flux by on-line search so as to optimize the machine-converter system wind vortex. Detailed analysis and simulation studies were performed for development of the control strategy and fuzzy algorithms, and a DSP TMS320C30 based hardware with C control software was built for the performance evaluation of a laboratory experimental set-up. The theoretical development was fully validated and the system is ready to be reproduced in a higher power installation. (author) 7 refs., 3 figs., 1 tab.

  19. Content-addressable-memory for the three key operations of fuzzy logic

    Jiang, Tao; Li, Yao

    1999-03-01

    Today, most fuzzy logic operations are performed via software means, which is inevitably slow. While searching for long term hardware solutions to realize analog fuzzy logic operations, the use of the well-developed Boolean logic hardware with analog to digital and digital to analog converters to implement the digitized fuzzy logic could provide an efficient solution. Similar to Boolean logic, digitized fuzzy logic operations can be written as a minimized sum-of-product term format, which can then be implemented based on programmable logic arrays. We address a fundamental issue of the computational complexity of this method. We derive the minimum number of the Boolean sum-of-product terms for some key fuzzy logic operations, such as Union, Intersection, and Complement operators. Our derivations provide ways to estimate the general computational complexity or memory capacity of using binary circuits, electronic or optoelectronic, to implement the digitized analog logic operations.

  20. Neural and Fuzzy Adaptive Control of Induction Motor Drives

    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

  1. ASSESSING THE SUSTAINABILITY OF AGRICULTURAL PRODUCTION SYSTEMS USING FUZZY LOGIC

    Moslem Sami

    2013-09-01

    Full Text Available First stage for attaining sustainability in a system is the measurement of current state of sustainability. Indicators are widely used as tools for measurement of sustainability. In this study, a comprehensive index was proposed to measure sustainability in agricultural production systems. This index takes advantage of fuzzy logic to combine all six indexes which were selected as the representative of three dimensions of sustainability. A set of models and sub-models based on the fuzzy inference system were employed to define the index. A case study conducted in two large production farms of maize and wheat, in Iran, proved the feasibility and usability of the model.

  2. Fuzzy logic estimator of rotor time constant in induction motors

    Alminoja, J. [Tampere University of Technology (Finland). Control Engineering Laboratory; Koivo, H. [Helsinki University of Technology, Otaniemi (Finland). Control Engineering Laboratory

    1997-12-31

    Vector control of AC machines is a well-known and widely used technique in induction machine control. It offers an exact method for speed control of induction motors, but it is also sensitive to the changes in machine parameters. E.g. rotor time constant has a strong dependence on temperature. In this paper a fuzzy logic estimator is developed, with which the rotor time constant can be estimated when the machine has a load. It is more simple than the estimators proposed in the literature. The fuzzy estimator is tested by simulation when step-wise abrupt changes and slow drifting occurs. (orig.) 7 refs.

  3. Fuzzy Logic Based Power System Contingency Ranking

    A. Y. Abdelaziz

    2013-02-01

    Full Text Available Voltage stability is a major concern in planning and operations of power systems. It is well known that voltage instability and collapse have led to major system failures. Modern transmission networks are more heavily loaded than ever before to meet the growing demand. One of the major consequences resulted from such a stressed system is voltage collapse or instability. This paper presents maximum loadability identification of a load bus in a power transmission network. In this study, Fast Voltage Stability Index (FVSI is utilized as the indicator of the maximum loadability termed as Qmax. In this technique, reactive power loading will be increased gradually at particular load bus until the FVSI reaches close to unity. Therefore, a critical value of FVSI was set as the maximum loadability point. This value ensures the system from entering voltage-collapse region. The main purpose in the maximum loadability assessment is to plan for the maximum allowable load value to avoid voltage collapse; which is important in power system planning risk assessment.The most important task in security analysis is the problem of identifying the critical contingencies from a large list of credible contingencies and ranks them according to their severity. The condition of voltage stability in a power system can be characterized by the use of voltage stability indices. This paper presents fuzzy approach for ranking the contingencies using composite-index based on parallel operated fuzzy inference engine. The Line Flow index (L.F and bus Voltage Magnitude (VM of the load buses are expressed in fuzzy set notation. Further, they are evaluated using Fuzzy rules to obtain overall Criticality Index. Contingencies are ranked based on decreasing order of Criticality Index and then provides the comparison of ranking obtained with FVSI method.

  4. LA LÓGICA DIFUSA COMPENSATORIA / THE COMPENSATORY FUZZY LOGIC

    Jesús Cejas-Montero

    2011-06-01

    Full Text Available

    La Lógica Difusa Compensatoria es un modelo lógico que permite la modelación simultánea de los procesos deductivos y de toma de decisiones. Sus características más importantes son: la flexibilidad, la tolerancia con la imprecisión, la capacidad para moldear problemas no-lineales y su fundamento en el lenguaje de sentido común. El artículo pretende llevar a la comunidad académico-empresarial las ideas fundamentales de la Lógica Difusa Compensatoria, ilustrándola en sus posibles campos de aplicación para lograr la competitividad de una organización.

    Abstract

    The Compensatory Fuzzy Logic is a logical model that allows the simultaneous modeling of the deductive and decision-making processes. The most important characteristics of Compensatory Fuzzy Logic are: the flexibility, the tolerance with the inaccuracy, the capacity to model no-lineal problems and its foundation in the language of common sense. The article seeks to bring the basic ideas of the Compensatory Fuzzy Logic to the academic–managerial community, illustrating it in its possible fields of application, in order to achieve the competitiveness of an organization.

  5. An Analysis Regarding the Possibility of Using Fuzzy Logic in Inventory Management

    Stoia, Claudiu-Leonardo

    2014-11-01

    The paper presents a brief state-of-the-art survey regarding the use of fuzzy logic in inventory management. Its goal is to motivate enthusiastic entrepreneurs to take into account the benefits of using fuzzy logic inventory control systems. It offers a guide to model an inventory system having a free fuzzy tool as starting point

  6. Fuzzy Logic Expert System-A Prescriptive Approach

    Wahid palash

    2015-08-01

    Full Text Available A membership value of a fuzzy set has been defined as the degree to which an element belongs to this fuzzy set. It is possible to give other interpretations to the membership degree like a certainty factor, a degree of truth, a degree of satisfaction and a degree of possibility. In 1978 Zadeh extended the fuzzy set theory to a possibility theory where the membership values are considered as degrees of possibility. Zadeh justies the possibility theory by the fact that the imprecision that is intrinsic in natural languages is, in the main, possibility rather than probabilistic in nature. In contrast to the statistical perspective of the information which is involved in the coding, the transmission and the reception of the data, the theory of possibility focuses on the meaning of the information. One of the reasons the scientific community took an interest in the fuzzy logic theory is the financial success of fuzzy control in home appliances in the Japanese industry. In 1990, the consumer products market using fuzzy controllers was estimated to 2 billion dollars. Interestingly enough L. A. Zadeh is a major contributor of the modern control theory. The control theory is a very precise and strict approach in order to model systems or phenomena.

  7. Fuzzy Logic Modeling For Peripheral End Milling Process

    Fuzzy logic has been deployed in this study to predict cutting speed and feed rate of peripheral end milling process at given hardness of material, radial depth of cut and cutter diameter. There were two types of fuzzy models had been designed and developed throughout in this study. The first developed fuzzy model (Model A) was two inputs with two outputs while the second developed model (Model B) was three inputs with two outputs. Hardness of material and radial depth of cut had been chosen as the inputs for the Model A. Cutter diameter then had been introduced as the third input besides material hardness and radial depth of cut for Model B. Two types of fuzzy model were designed to evaluate the effectiveness and efficiency of introducing cutter diameter as another input into the system. Both types of fuzzy model had been tested and validated with the recommended data obtained from Machining Data Handbook (MDH). The results showed a very good correlation between predicted data and the data from MDH. Model B had been chosen as the best fuzzy model to represent peripheral end milling process although Model A had performed better; 3.78% and 2.06% (Model A) compared to 3.81% and 2.27% (Model B) for cutting speed and feed percentage errors respectively. This is due to less development time and the ability to predict cutting speed and feed at any given cutting tool diameter.

  8. Introduction to Mathematical Fuzzy Logic. Tutorial

    Cintula, Petr; Noguera, Carles

    Haifa : Faculty of Social Sciences, 2014. s. 4-4. [ISRALOG 2014. Israeli Workshop on Non-Classical Logic s and Their Applications /2./. 29.09.2014-01.10.2014, Haifa] Institutional support: RVO:67985807 Subject RIV: BA - General Mathematics

  9. Answer Set Programming for Continuous Domains A Fuzzy Logic Approach

    Janssen, Jeroen; Vermeir, Dirk

    2012-01-01

    "Answer set programming (ASP)" is a declarative language tailored towards solving combinatorial optimization problems. It has been successfully applied to e.g. planning problems, configuration and verification of software, diagnosis and database repairs. However, ASP is not directly suitable for modeling problems with continuous domains. Such problems occur naturally in diverse fields such as the design of gas and electricity networks, computer vision and investment portfolios. To overcome this problem we study FASP, a combination of ASP with fuzzy logic - a class of manyvalued logic

  10. STATOR FLUX OPTIMIZATION ON DIRECT TORQUE CONTROL WITH FUZZY LOGIC

    Fatih Korkmaz

    2012-07-01

    Full Text Available The Direct Torque Control (DTC is well known as an effective control technique for high performance drives in a wide variety of industrial applications and conventional DTC technique uses two constant reference value: torque and stator flux. In this paper, fuzzy logic based stator flux optimization technique for DTC drives that has been proposed. The proposed fuzzy logic based stator flux optimizer self-regulates the stator flux reference using induction motor load situation without need of any motor parameters. Simulation studies have been carried out with Matlab/Simulink to compare the proposed system behaviors at vary load conditions. Simulation results show that the performance of the proposed DTC technique has been improved and especially at low-load conditions torque ripple are greatly reduced with respect to the conventional DTC.

  11. INJECTION PAINTING OPTIMIZATION WITH FUZZY LOGIC EXPERT SYSTEM.

    BEEBE-WANG,J.; TANG,J.

    2001-06-18

    Optimizing transverse particle distributions in the accumulator ring is one of most important factors to the future performance of the Spallation Neutron Source (SNS) [l]. This can only be achieved by optimizing the injection bumps that paint the beam in phase space. The process is complex due to the vague distribution inputs and the multiple optimization goals. Furthermore, the priority of the optimization criteria could change at different operational stages. We propose optimizing transverse phase space painting with fuzzy logic and present our initial studies toward that end. The focus of this paper is on how the problem can be solved with a Fuzzy Logic (FL) expert system through the creation of a set of rules that can be applied by the system. Various particle distributions, from computer simulations, are analyzed with FL and the results are compared and discussed. Finally, a run-time optimization control system is proposed.

  12. Use of Fuzzy Logic Systems for Assessment of Primary Faults

    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.

  13. ICD 10 Based Medical Expert System Using Fuzzy Temporal Logic

    Chinniah, P

    2010-01-01

    Medical diagnosis process involves many levels and considerable amount of time and money are invariably spent for the first level of diagnosis usually made by the physician for all the patients every time. Hence there is a need for a computer based system which not only asks relevant questions to the patients but also aids the physician by giving a set of possible diseases from the symptoms obtained using logic at inference. In this work, an ICD10 based Medical Expert System that provides advice, information and recommendation to the physician using fuzzy temporal logic. The knowledge base used in this system consists of facts of symptoms and rules on diseases. It also provides fuzzy severity scale and weight factor for symptom and disease and can vary with respect to time. The system generates the possible disease conditions based on modified Euclidean metric using Elders algorithm for effective clustering. The minimum similarity value is used as the decision parameter to identify a disease.

  14. INJECTION PAINTING OPTIMIZATION WITH FUZZY LOGIC EXPERT SYSTEM

    Optimizing transverse particle distributions in the accumulator ring is one of most important factors to the future performance of the Spallation Neutron Source (SNS) [l]. This can only be achieved by optimizing the injection bumps that paint the beam in phase space. The process is complex due to the vague distribution inputs and the multiple optimization goals. Furthermore, the priority of the optimization criteria could change at different operational stages. We propose optimizing transverse phase space painting with fuzzy logic and present our initial studies toward that end. The focus of this paper is on how the problem can be solved with a Fuzzy Logic (FL) expert system through the creation of a set of rules that can be applied by the system. Various particle distributions, from computer simulations, are analyzed with FL and the results are compared and discussed. Finally, a run-time optimization control system is proposed

  15. 8-Valent Fuzzy Logic for Iris Recognition and Biometry

    Popescu-Bodorin, N; Motoc, I M; 10.1109/ISCIII.2011.6069761

    2011-01-01

    This paper shows that maintaining logical consistency of an iris recognition system is a matter of finding a suitable partitioning of the input space in enrollable and unenrollable pairs by negotiating the user comfort and the safety of the biometric system. In other words, consistent enrollment is mandatory in order to preserve system consistency. A fuzzy 3-valued disambiguated model of iris recognition is proposed and analyzed in terms of completeness, consistency, user comfort and biometric safety. It is also shown here that the fuzzy 3-valued model of iris recognition is hosted by an 8-valued Boolean algebra of modulo 8 integers that represents the computational formalization in which a biometric system (a software agent) can achieve the artificial understanding of iris recognition in a logically consistent manner.

  16. Risk evaluation in Columbian electricity market using fuzzy logic

    Medina, S.; Moreno, J. [Universidad Nacional de Columbia, Medellin (Colombia)

    2007-09-15

    This article proposes a model based on Fuzzy Logic to evaluate the market risk that a trading agent faces in the electric power negotiation in Colombia, as part of a general model of negotiation. The proposed model considers single external factors as regulatory changes, social and political issues, and the condition of the national transmission net. Variables of the market associated to these risk factors were selected and some graphic and statistical analyses were made in order to check their relationship with the electricity prices and to determine why the experts consider these factors in their analyses. According to the obtained results a Mamdani Fuzzy Inference System which contains the expert knowledge was developed and it is presented in a fuzzy cognitive map. (author)

  17. Risk evaluation in Columbian electricity market using fuzzy logic

    This article proposes a model based on Fuzzy Logic to evaluate the market risk that a trading agent faces in the electric power negotiation in Colombia, as part of a general model of negotiation. The proposed model considers single external factors as regulatory changes, social and political issues, and the condition of the national transmission net. Variables of the market associated to these risk factors were selected and some graphic and statistical analyses were made in order to check their relationship with the electricity prices and to determine why the experts consider these factors in their analyses. According to the obtained results a Mamdani Fuzzy Inference System which contains the expert knowledge was developed and it is presented in a fuzzy cognitive map. (author)

  18. 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

  19. Spatially Adaptive Image Restoration Using Fuzzy Punctual Kriging

    Anwar M. Mirza; Asmatullah Chaudhry; Badre Munir

    2007-01-01

    We present a general formulation based on punctual kriging and fuzzy concepts for image restoration in spatial domain. Gray-level images degraded with Gaussian white noise have been considered. Based on the pixel local neighborhood, fuzzy logic has been employed intelligently to avoid unnecessary estimation of a pixel. The intensity estimation of the selected pixels is then carried out by employing punctual kriging in conjunction with the method of Lagrange multipliers and estimates of local semi-variances. Application of such a hybrid technique performing both selection and intensity estimation of a pixel demonstrates substantial improvement in the image quality as compared to the adaptive Wiener filter and existing fuzzy- kriging approaches. It has been found that these filters achieve noise reduction without loss of structural detail information, as indicated by their higher structure similarity indices, peak signal to noise ratios and the new variogram based quality measures.

  20. Turbulence for different background conditions using fuzzy logic and clustering

    K. Satheesan; Kirkwood, S

    2010-01-01

    Wind and turbulence estimated from MST radar observations in Kiruna, in Arctic Sweden are used to characterize turbulence in the free troposphere using data clustering and fuzzy logic. The root mean square velocity, νfca, a diagnostic of turbulence is clustered in terms of hourly wind speed, direction, vertical wind speed, and altitude of the radar observations, which are the predictors. The predictors are graded over an interval of zero to one through an input membership...

  1. Turbulence for different background conditions using fuzzy logic and clustering

    K. Satheesan; Kirkwood, S

    2010-01-01

    Wind and turbulence estimated from MST radar observations in Kiruna, in Arctic Sweden are used to characterize turbulence in the free troposphere using data clustering and fuzzy logic. The root mean square velocity, νfca, a diagnostic of turbulence is clustered in terms of hourly wind speed, direction, vertical wind speed, and altitude of the radar observations, which are the predictors. The predictors are graded over an interval of zero to one through an input m...

  2. Fuzzy logic in fire control systems for air defence

    Gelev, Saso; Gacovski, Zoran; Jiea-he, Xu; Yuan-wei, Jing; Deskovski, Stojce

    2007-01-01

    It is the necessity defense combat against modern offensive weapons from the air to apply the best and most efficient defense tactics and technology. The problems of shooting targets in air space are solved by appropriate design of a fire control system, and the latest developments employ computational intelligence models and techniques. In this paper, a fuzzy-logic knowledge-base system in the fire control system for missile based air defense has been investigated. The aim of this paper is t...

  3. Modeling noise annoyance caused by air traffic using fuzzy logic

    Sanchez Franco, Miriam

    2008-01-01

    The main goal of this project is the study and modeling of the noise annoyance caused by air traffic by using the fuzzy logic theory. Like many other environmental problems, air traffic noise, continues to grow and has become a serious problem in many countries. Millions of people living or working around airport areas can suffer from noise exposure effects as for instance hearing loss, interference with communication, stress, sleep disturbance, psychological effects as well...

  4. DESIGN MODEL OF FUZZY LOGIC MEDICAL DIAGNOSIS CONTROL SYSTEM

    Faran Baig,; Dr. M. Saleem Khan,; Yasir Noor,; Imran, M.

    2011-01-01

    This research work addresses the medical diagnosis regarding the normality of a human function in human brain and the diagnosis of hemorrhage and brain tumor. It enhances the control strategies in the medical field to diagnose a disease. This system using fuzzy logic design: fuzzifier, inference engine, rule base, and defuzzification is capable to be used for medical diagnosis giving the entries of five inputs: Protein , Red blood cells , Lymphocytes , Neutrophils and Eosinophils, and taking ...

  5. Switch Reluctance Motor Control Based on Fuzzy Logic System

    S. Aleksandrovsky

    2012-01-01

    Full Text Available Due to its intrinsic simplicity and reliability, the switched reluctance motor (SRM has now become a promising candidate for variable-speed drive applications as an alternative induction motor in various industrial application. However, the SRM has the disadvantage of nonlinear characteristic and control. It is suggested to use controller based on fuzzy logic system. Design of FLS controller and simulation model presented.

  6. Hybrid fuzzy/crisp-logic control of manufacturing systems

    Porter, B; Moi, H.

    1996-01-01

    In recent years, techniques such as dynamic programming, the maximum principle, linear programming, and genetic algorithms have been used to synthesise optimal control policies for manufacturing systems. However, such techniques are frequently rather opaque and often yield control policies that are implemented by open-loop rather than closed-loop control systems. In this paper, it is therefore shown that closed-loop systems incorporating hybrid fuzzy/crisp-logic controllers can be readily des...

  7. Implement Fuzzy Logic to Optimize Electronic Business Success

    Fahim Akhter

    2016-01-01

    Customers are realizing the importance and benefits of shopping online such as convenience, comparison, product research, larger selection, and lower prices. The dynamic nature of e-commerce evokes online businesses to make alterations in their business processes and decisions making to satisfy customers’ needs. Online businesses are adopting Business Intelligence (BI) tools and systems with the collaboration of fuzzy logic system to forecast the future of the e-commerce. With the aid of BI, ...

  8. Fuzzy logic control for energy saving in autonomous electric vehicles

    Al-Jazaeri, Ahmed O.; Samaranayake, Lilantha; Longo, Stefano; Auger, Daniel

    2015-01-01

    Limited battery capacity and excessive battery dimensions have been two major limiting factors in the rapid advancement of electric vehicles. An alternative to increasing battery capacities is to use better: intelligent control techniques which save energy on-board while preserving the performance that will extend the range with the same or even smaller battery capacity and dimensions. In this paper, we present a Type-2 Fuzzy Logic Controller (Type-2 FLC) as the speed controller, acting as th...

  9. Control of a gravity gradient stabilised satellite using fuzzy logic

    Aage Skullestad; Kjetil Olsen; Stein Rennehvammen; Håvard Fløystad

    2001-01-01

    This paper describes attitude control of a small gravity gradient stabilised satellite. A gravity gradient stabilised satellite has limited stability and pointing capabilities, and magnetic coils are added in order to improve the accuracy of the attitude control. The magnetic coils are controlled using a fuzzy logic controller, based on a combination of membership functions and rules. The control of the pitch axis is separated from the roll and azimuth axes and excellent pitch angle accuracy ...

  10. Interaction Analysis in Smart Work Environments through Fuzzy Temporal Logic

    IJsselmuiden, Joris

    2014-01-01

    Interaction analysis is defined as the generation of situation descriptions from machine perception. World models created through machine perception are used by a reasoning engine based on fuzzy metric temporal logic and situation graph trees, with optional parameter learning and clustering as preprocessing, to deduce knowledge about the observed scene. The system is evaluated in a case study on automatic behavior report generation for staff training purposes in crisis response control rooms.

  11. Fuzzy Logic and Mechanical Ventilation of COPD Patients

    Hatzakis, George; Olivenstein, Ronald; Bates, Jason H. T.

    2001-01-01

    Weaning from mechanical ventilation typically follows a course determined by the experience of the attending physician. However, despite the currently subjective nature of the weaning process and the many factors involved in its success, there is a wide consensus that this procedure could be automated somehow. We have developed a fuzzy logic based controller of pressure support mechanical ventilation (AJRCCM, 1999 Aug 160:2 550-6) and are now evaluating its performance in a prospective trial ...

  12. ICD 10 Based Medical Expert System Using Fuzzy Temporal Logic

    P.Chinniah

    2009-12-01

    Full Text Available Medical diagnosis process involves many levels and considerable amount of time and money are invariably spent for the first level of diagnosis usually made by the physician for all the patients every time. Hence there is a need for a computer based system which not only asks relevant questions to the patients but also aids the physician by giving a set of possible diseases from the symptoms obtained using logic at inference. In this work, an ICD10 based Medical Expert System that provides advice, information and recommendation to the physician using fuzzy temporal logic. The knowledge base used in this system consists of facts of symptoms and rules on diseases. It also provides fuzzy severity scale and weight factor for symptom and disease and can vary with respect to time. The system generates the possible disease conditions based on modified Euclidean metric using Elder’s algorithm for effective clustering. The minimum similarity value is used as the decision parameter to identify a disease. Keywords -Fuzzy clustering, symptoms, fuzzy severity scale, weight factor, Minkowski distance, ICD, WHO, Rules Base, TSQL

  13. Fuzzy logic controllers and chaotic natural convection loops

    The study of natural circulation loops is a subject of special concern for the engineering design of advanced nuclear reactors, as natural convection provides an efficient and completely passive heat removal system. However, under certain circumstances thermal-fluid-dynamical instabilities may appear, threatening the reactor safety as a whole.On the other hand, fuzzy logic controllers provide an ideal framework to approach highly non-linear control problems. In the present work, we develop a software-based fuzzy logic controller and study its application to chaotic natural convection loops.We numerically analyse the linguistic control of the loop known as the Welander problem in such conditions that, if the controller were not present, the circulation flow would be non-periodic unstable.We also design a Taka gi-Sugeno fuzzy controller based on a fuzzy model of a natural convection loop with a toroidal geometry, in order to stabilize a Lorenz-chaotic behaviour.Finally, we show experimental results obtained in a rectangular natural circulation loop

  14. Optimized Reactive Power Compensation Using Fuzzy Logic Controller

    George, S.; Mini, K. N.; Supriya, K.

    2015-03-01

    Reactive power flow in a long transmission line plays a vital role in power transfer capability and voltage stability in power system. Traditionally, shunt connected compensators are used to control reactive power in long transmission line. Thyristor controlled reactor is used to control reactive power under lightly loaded condition. By controlling firing angle of thyristor, it is possible to control reactive power in the transmission lines. However, thyristor controlled reactor will inject harmonic current into the system. An attempt to reduce reactive power injection will increase harmonic distortion in the line current and vice versa. Thus, there is a trade-off between reactive power injection and harmonics in current. By optimally controlling the reactive power injection, harmonics in current can be brought within the specified limit. In this paper, a Fuzzy Logic Controller is implemented to obtain optimal control of reactive power of the compensator to maintain voltage and harmonic in current within the limits. An algorithm which optimizes the firing angle in each fuzzy subset by calculating the rank of feasible firing angles is proposed for the construction of rules in Fuzzy Logic Controller. The novelty of the algorithm is that it uses a simple error formula for the calculation of the rank of the feasible firing angles in each fuzzy subset.

  15. A fuzzy logic intelligent diagnostic system for spacecraft integrated vehicle health management

    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.

  16. Implement Fuzzy Logic to Optimize Electronic Business Success

    Fahim Akhter

    2016-03-01

    Full Text Available Customers are realizing the importance and benefits of shopping online such as convenience, comparison, product research, larger selection, and lower prices. The dynamic nature of e-commerce evokes online businesses to make alterations in their business processes and decisions making to satisfy customers’ needs. Online businesses are adopting Business Intelligence (BI tools and systems with the collaboration of fuzzy logic system to forecast the future of the e-commerce. With the aid of BI, businesses have more possibilities to choose types and structures of required information to serve customers. The fuzzy logic system and BI capabilities would allow both customers and vendors to make right decisions about online shopping. Many experts believe that trust and security are critical risk factors for the embracement of e-commerce. Online trust may be influenced by factors such as usability, familiarity and conducting business with unknown parties. This paper discusses fuzzy logic and BI approach to gauge the level of trust and security in online transactions. The paper further addresses the issues and concerns related to the equilibrium of trust, security, and usability in online shopping.

  17. Fuzzy Logic Decoupled Longitudinal Control for General Aviation Airplanes

    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.

  18. Fuzzy logic control of 15 N separation plant

    The process of 15 N separation by chemical exchange in Nitrox system is automatically maintained in the optimal operation conditions using a computerized control. The automatic control leads to a maximum production of 15 N with a minimum of raw materials and energy consumption.. The control objective was achieved by considering two forms of knowledge: 1. objective knowledge, which uses the control engineering based on mathematical model of the separation process; 2. subjective knowledge, which represents linguistic information, very difficult to quantify using classical mathematics - e.g., the rule of HNO3 solution and SO2 flow rates adjustment in order to maintain a proper height and position of chemical reaction zone in the product refluxer. The above mentioned two types of knowledge were coordinated in a logical way using fuzzy logic control system which has the possibility to handle simultaneously numerical data and linguistic knowledge. In order to map input data vector into a scalar output, i.e., numbers to numbers a front-end 'fuzzifier' and a rear-end 'defuzzifier' was added to the usual fuzzy logic model. The inference engine of the control system maps the input fuzzy set into the output one. The inferential procedure maintains the isotope separation process in the optimal operation conditions. (author)

  19. MANAGE OF CONCRETE BRIDGES’ ELEMENTS ON THE BASIS OF FUZZY LOGIC MODELS

    L. P. Bodnar

    2010-03-01

    Full Text Available The refinement of estimation of operational state of the ferro-concrete bridge elements and the direction of substantiation of the bridge service levels on the basis of fuzzy set theory and fuzzy logic are offered.

  20. Fuzzy Logic: A New Tool for the Analysis and Organization of International Business Communications.

    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…

  1. Searching arousals: A fuzzy logic approach.

    Chaparro-Vargas, Ramiro; Ahmed, Beena; Penzel, Thomas; Cvetkovic, Dean

    2015-08-01

    This paper presents a computational approach to detect spontaneous, chin tension and limb movement-related arousals by estimating neuronal and muscular activity. Features extraction is carried out by Time Varying Autoregressive Moving Average (TVARMA) models and recursive particle filtering. Classification is performed by a fuzzy inference system with rule-based decision scheme based upon the AASM scoring rules. Our approach yielded two metrics: arousal density and arousal index to comply with standardised clinical benchmarking. The obtained statistics achieved error deviation around ±1.5 to ±30. These results showed that our system can differentiate amongst 3 different types of arousals, subject to inter-subject variability and up-to-date scoring references. PMID:26736862

  2. Distributed traffic signal control using fuzzy logic

    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.

  3. Model Reduction of Fuzzy Logic Systems

    Zhandong Yu

    2014-01-01

    Full Text Available This paper deals with the problem of ℒ2-ℒ∞ model reduction for continuous-time nonlinear uncertain systems. The approach of the construction of a reduced-order model is presented for high-order nonlinear uncertain systems described by the T-S fuzzy systems, which not only approximates the original high-order system well with an ℒ2-ℒ∞ error performance level γ but also translates it into a linear lower-dimensional system. Then, the model approximation is converted into a convex optimization problem by using a linearization procedure. Finally, a numerical example is presented to show the effectiveness of the proposed method.

  4. Maximizing Strength of Digital Watermarks using Fuzzy Logic

    Oueslati, Sameh; Solaiman, Bassel

    2011-01-01

    In this paper, we propose a novel digital watermarking scheme in DCT domain based fuzzy inference system and the human visual system to adapt the embedding strength of different blocks. Firstly, the original image is divided into some 8 \\times 8 blocks, and then fuzzy inference system according to different textural features and luminance of each block decide adaptively different embedding strengths. The watermark detection adopts correlation technology. Experimental results show that the proposed scheme has good imperceptibility and high robustness to common image processing operators.

  5. Maximizing Strength of Digital Watermarks Using Fuzzy Logic

    Sameh Oueslati

    2011-02-01

    Full Text Available In this paper, we propose a novel digital watermarking scheme in DCT domain based fuzzy inferencesystem and the human visual system to adapt the embedding strength of different blocks. Firstly, theoriginal image is divided into some 8×8 blocks, and then fuzzy inference system according to differenttextural features and luminance of each block decide adaptively different embedding strengths. Thewatermark detection adopts correlation technology. Experimental results show that the proposed schemehas good imperceptibility and high robustness to common image processing operators.

  6. Research on fault diagnosis of nuclear power plants based on genetic algorithms and fuzzy logic

    Based on genetic algorithms and fuzzy logic and using expert knowledge, mini-knowledge tree model and standard signals from simulator, a new fuzzy-genetic method is developed to fault diagnosis in nuclear power plants. A new replacement method of genetic algorithms is adopted. Fuzzy logic is used to calculate the fitness of the strings in genetic algorithms. Experiments on the simulator show it can deal with the uncertainty and the fuzzy factor

  7. Computational intelligence synergies of fuzzy logic, neural networks and evolutionary computing

    Siddique, Nazmul

    2013-01-01

    Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspect

  8. An Advanced Certain Trust Model Using Fuzzy Logic and Probabilistic Logic theory

    Kawser Wazed Naf

    2013-01-01

    Full Text Available Trustworthiness especially for service oriented system is very important topic now a day in IT field of the whole world. Certain Trust Model depends on some certain values given by experts and developers. Here, main parameters for calculating trust are certainty and average rating. In this paper we have proposed an Extension of Certain Trust Model, mainly the representation portion based on probabilistic logic and fuzzy logic. This extended model can be applied in a system like cloud computing, internet, website, e-commerce, etc. to ensure trustworthiness of these platforms. The model uses the concept of fuzzy logic to add fuzziness with certainty and average rating to calculate the trustworthiness of a system more accurately. We have proposed two new parameters - trust T and behavioral probability P, which will help both the users and the developers of the system to understand its present condition easily. The linguistic variables are defined for both T and P and then these variables are implemented in our laboratory to verify the proposed trust model. We represent the trustworthiness of test system for two cases of evidence value using Fuzzy Associative Memory (FAM. We use inference rules and defuzzification method for verifying the model.

  9. Fuzzy Logic and Piecewise-Linear Regression

    Fröhlich, J.; Holeňa, Martin

    Košice: Prírodovedecká fakulta, Univerzita P.J. Šafárika, 2008 - (Vojtáš, P.), s. 35-38 ISBN 978-80-969184-8-5. [ITAT 2008. Conference on Theory and Practice of Information Theory . Hrebienok (SK), 22.09.2008-26.09.2008] R&D Projects: GA ČR GA201/08/0802; GA ČR GEICC/08/E018 Institutional research plan: CEZ:AV0Z10300504 Keywords : Lukasiewicz propositional logic * piecewise-linear regression Subject RIV: IN - Informatics, Computer Science

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

    Ning Li; José-Fernán Martínez; Vicente Hernández Díaz

    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 paramete...

  11. Realization of Fuzzy Logic Controlled Brushless DC Motor Drives Using Matlab/Simulink

    Çunkas, Mehmet; Aydoğdu, Omer

    2010-01-01

    In this paper, an efficient simulation model for fuzzy logic controlled brushless direct current motor drives using Matlab/Simulink is presented. The brushless direct current (BLDC) motor is efficiently controlled by Fuzzy logic controller (FLC). The control algorithms, fuzzy logic and PID are compared. Also, the dynamic characteristics of the BLDC motor (i.e. speed and torque) and as well as currents and voltages of the inverter components are easily observed and analyzed by using the develo...

  12. Development of Fuzzy-Logic-Based Self Tuning PI Controller for Servomotor

    Saad, Nordin; Wahyunggoro, Oyas

    2010-01-01

    This work discusses the modeling of a DC servomotor from gray box identification and performance evaluations of real time experiment using a fuzzy-logic-based self tuning PI controller as compared to fuzzy-logic-based self tuning PID controller, fuzzy logic controller, PID controller and PI controller on the DC servomotor system. Here, the s-model transfer function of a DC servomotor is identified as a third order transfer function without

  13. Noise-based logic: Binary, multi-valued, or fuzzy, with optional superposition of logic states

    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

  14. Optimal design and robustification of fuzzy-logic controllers for robotic manipulators using genetic algorithms

    In this paper, genetic algorithms are used in the design and robustification various mo el-ba ed/non-model-based fuzzy-logic controllers for robotic manipulators. It is demonstrated that genetic algorithms provide effective means of designing the optimal set of fuzzy rules as well as the optimal domains of associated fuzzy sets in a new class of model-based-fuzzy-logic controllers. Furthermore, it is shown that genetic algorithms are very effective in the optimal design and robustification of non-model-based multivariable fuzzy-logic controllers for robotic manipulators

  15. Chattering-free fuzzy adaptive robust sliding-mode vibration control of a smart flexible beam

    Chattering is an undesired phenomenon associated with classical sliding-mode control. The discontinuous bang–bang robust controller causes chattering near the equilibrium. To attenuate the chattering, in this paper, a fuzzy logic smooth switch system is integrated with the adaptive robust sliding-mode control to form a fuzzy adaptive robust sliding-mode control for the active vibration control of a smart flexible beam integrated with piezoceramic actuators and sensors. The asymptotical stability proof of the proposed fuzzy adaptive robust sliding-mode controller is provided by Lyapunov's direct method. The experimental results show that the proposed fuzzy adaptive robust sliding-mode controller quickly suppresses the vibration. Additionally, with the fuzzy switch system, the chattering is successfully attenuated

  16. Fuzzy logic based controller and its application to SVC for an industrial power system

    The paper present a simple yet powerful fuzzy logic based static VAR compensator applied to an industrial power system consisting of three phase induction motors and static loads. In the proposed fuzzy logic controller, the speed and acceleration variations of a specific machine are taken as the inputs. To demonstrate the effectiveness and capabilities of the employed fuzzy logic based static VAR compensator, some extensive and comparative non-linear time domain digital simulation tests are performed. The results show that over a wide range of operating conditions and disturbances, the fuzzy logic based static VAR compensator remarkably improves the voltage profile and the overall dynamic performance

  17. Temporal Difference based Tuning of Fuzzy Logic Controller through Reinforcement Learning to Control an Inverted Pendulum

    Raj kumar

    2012-08-01

    Full Text Available This paper presents a self-tuning method of fuzzy logic controllers. The consequence part of the fuzzy logic controller is self-tuned through the Q-learning algorithm of reinforcement learning. The off policy temporal difference algorithm is used for tuning which directly approximate the action value function which gives the maximum reward. In this way, the Q-learning algorithm is used for the continuous time environment. The approach considered is having the advantage of fuzzy logic controller in a way that it is robust under the environmental uncertainties and no expert knowledge is required to design the rule base of the fuzzy logic controller.

  18. Number-free Mathematics Based on T-norm Fuzzy Logic

    Běhounek, Libor

    Granada: EUSFLAT, 2009 - (Carvalho, J.; Dubois, D.; Kaymak, U.; Sousa, J.), s. 449-454 ISBN 978-989-95079-6-8. [IFSA - EUSFLAT 2009. International Fuzzy Systems Association World Congress 2009, European Society for Fuzzy Logic and Technology Conference 2009. Lisabon (PT), 20.07.2009-24.07.2009] R&D Projects: GA AV ČR IAA100300503 Institutional research plan: CEZ:AV0Z10300504 Keywords : Fuzzy Class Theory * fuzzy mathematics * fuzzy set * real -valued function * similarity-based limit * t-norm fuzzy logic Subject RIV: BA - General Mathematics

  19. Car Safety System Using Fuzzy Logic

    M. Al-Hadidi

    2008-01-01

    Full Text Available Nowadays everybody can recognize the huge increasing of car numbers on roads. Day after day, this increasing may be an indicator for changing and development. This put a lot of challenges on people and governments. One of these challenges is car accidents and there bad effects. The main aim of our proposal is to help our society to decrease car accidents by designing a system which makes the drivers pay more attention and worn them before an accident takes place. The designed system consists basically of three general circuits, which complete each others. The first circuit consists of microcontroller, power source, speaker and switches needed. The second circuit, the ultrasonic transmitter and receiver circuit which measures the distance between the car and any thing in front of it. Finally, the third circuit, the opto-coupler circuit, its function to determine the current speed of the car. The characteristics of this system include flexibility and effectiveness of implementation. It’s cheap and it has low power consumption with a small size. The system records all the changes surrounded the driver environment, then process them using a fuzzy microcontroller which worn the driver of expected dangers through an output devices which lead the driver to take all necessary measures to avoid the accident.

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

    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

  1. A Temporal Fuzzy Logic Formalism for Knowledge Based Systems

    Vasile MAZILESCU

    2012-11-01

    Full Text Available This paper shows that the influence of knowledge on new forms of work organisation can be described as mutual relationships. Different changes in work organisation also have a strong influence on the increasing importance of knowledge of different individual and collective actors in working situations. After that, we characterize a piece of basic formal system, an Extended Fuzzy Logic System (EFLS with temporal attributes, to conceptualize future DKMSs based on human imprecise for distributed just in time decisions. The approximate reasoning is perceived as a derivation of new formulas with the corresponding temporal attributes, within a fuzzy theory defined by the fuzzy set of special axioms. In a management application, the reasoning is evolutionary because of unexpected events which may change the state of the DKMS. In this kind of situations it is necessary to elaborate certain mechanisms in order to maintain the coherence of the obtained conclusions, to figure out their degree of reliability and the time domain for which these are true. These last aspects stand as possible further directions of development at a basic logic level for future technologies that must automate knowledge organizational processes.

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

    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.

  3. An adaptive model-free fuzzy controller

    In this paper, we present an adaptive, stable fuzzy controller whose parameters are optimized via a genetic algorithm. The controller model is capable of building itself on the basis of measured plant data and then of adapting to new dynamics. The stability of the overall system, made up of the plant and the controller, is guaranteed by Lyapunov's theory. As a case study, the stable adaptive fuzzy controller is employed to drive the narrow water level of a simulated Steam Generator (SG) to a desired reference trajectory. The numerical results confirm that the controller bears good performances in terms of small oscillations and fast settling time even in presence of external disturbances. (authors)

  4. Optimization of adaptive fuzzy processor design

    Baturone, I.; Sánchez-Solano, Santiago; Barriga, Angel; Huertas-Díaz, J. L.

    1998-01-01

    A fuzzy processor is programmed to provide anoptimum output for solving a given problem. It could theoretically solve any problem (from a static point of view) if it is an universal approximator. This paper addresses the design of fuzzy processors aiming at a twofold objective: efficient adaptive approximation of different and even dynamically changing surfaces and hardware simplicity. Adequate programmable parameters and a fully-parallel architecture are selected. Mixed-signal blocks b...

  5. Fuzzy logic of quasi-truth an algebraic treatment

    Di Nola, Antonio; Turunen, Esko

    2016-01-01

    This book presents the first algebraic treatment of quasi-truth fuzzy logic and covers the algebraic foundations of many-valued logic. It offers a comprehensive account of basic techniques and reports on important results showing the pivotal role played by perfect many-valued algebras (MV-algebras). It is well known that the first-order predicate Łukasiewicz logic is not complete with respect to the canonical set of truth values. However, it is complete with respect to all linearly ordered MV –algebras. As there are no simple linearly ordered MV-algebras in this case, infinitesimal elements of an MV-algebra are allowed to be truth values. The book presents perfect algebras as an interesting subclass of local MV-algebras and provides readers with the necessary knowledge and tools for formalizing the fuzzy concept of quasi true and quasi false. All basic concepts are introduced in detail to promote a better understanding of the more complex ones. It is an advanced and inspiring reference-guide for graduate s...

  6. Turbulence for different background conditions using fuzzy logic and clustering

    K. Satheesan

    2010-08-01

    Full Text Available Wind and turbulence estimated from MST radar observations in Kiruna, in Arctic Sweden are used to characterize turbulence in the free troposphere using data clustering and fuzzy logic. The root mean square velocity, νfca, a diagnostic of turbulence is clustered in terms of hourly wind speed, direction, vertical wind speed, and altitude of the radar observations, which are the predictors. The predictors are graded over an interval of zero to one through an input membership function. Subtractive data clustering has been applied to classify νfca depending on its homogeneity. Fuzzy rules are applied to the clustered dataset to establish a relationship between predictors and the predictant. The accuracy of the predicted turbulence shows that this method gives very good prediction of turbulence in the troposphere. Using this method, the behaviour of νfca for different wind conditions at different altitudes is studied.

  7. Determination of coal mine mechanization using fuzzy logic

    Ataei M; Khalokakaei R; Hossieni M

    2009-01-01

    Two of the most important tasks in coal mines are to improve efficiency and to increase production besides keeping safety constantly in mind. In order to obtain these goals, mine mechanization is required. Mine mechanization needs high levels of investment and should therefore be studied carefully before final decisions about mechanization are made. When analysizing the potential for mechanization the following, rather imprecise, factors should be considered: seam inclination and thickness, geologi-cal disturbances, seam floor conditions, roof conditions, water at the working face and the extension of seams. In our study we have used fuzzy logic, membership functions and created fuzzy rule - based methods and to considered the ultimate objective: mechanization of mining. As a case study, the mechanization of the Takht coal seams in Iran was investigated. The results show a high potential for mechanization in most of the Takht coal seams.

  8. DESIGN MODEL OF FUZZY LOGIC MEDICAL DIAGNOSIS CONTROL SYSTEM

    Faran Baig,

    2011-05-01

    Full Text Available This research work addresses the medical diagnosis regarding the normality of a human function in human brain and the diagnosis of hemorrhage and brain tumor. It enhances the control strategies in the medical field to diagnose a disease. This system using fuzzy logic design: fuzzifier, inference engine, rule base, and defuzzification is capable to be used for medical diagnosis giving the entries of five inputs: Protein , Red blood cells , Lymphocytes , Neutrophils and Eosinophils, and taking three outputs: Normal, Hemorrhage and Brain Tumor. The medical diagnosis fuzzy rules are formulated and applied using MATLAB simulation for the system. The simulation results are found in agreement with the design based calculated results. This research work proposes to develop a control system toenhance the efficiency to diagnose a disease related to human brain.

  9. Self-tuning fuzzy logic nuclear reactor controller

    A method for self-timing of a fuzzy logic controller (FLC) based on the estimation of the optimum value of the centroids of the its output fuzzy sets is proposed. The method can be implemented on-line and does not modify the membership function and the control rules, thus preserving the description of control statements in their original forms. Results of simulation and actual tests show that the tuning method improves the FLCs performance in following desired reactor power level trajectories (simulation tests) and simple power up and power down experiments (simulation and actual tests). The FLC control rules were derived from control statements expressing the relations between error, rate of error change, and control rod duration and direction of movements

  10. Application of fuzzy logic for determining of coal mine mechanization

    HOSSEINI SAA; ATAEI M; HOSSEINI S M; AKHYANI M

    2012-01-01

    The fundamental task of mining engineers is to produce more coal at a given level of labour input and material costs,for optimum quality and maximum efficiency.To achieve these goals,it is necessary to automate and mechanize mining operations.Mechanization is an objective that can result in significant cost reduction and higher levels of profitability for underground mines.To analyze the potential of mechanization,some important factors such as seam inclination and thickness,geological disturbances,seam floor conditions and roof conditions should be considered.In this study we have used fuzzy logic,membership functions and created fuzzy rule-based methods and considered the ultimate objective:mechanization of mining.As a case study,the mechanization of the Tazare coal seams in Shahroud area of Iran was investigated.The results show a low potential for mechanization in most of the Tazare coal seams.

  11. A robot arm controller with obstacle avoidance using fuzzy logic

    This study is devoted to the development of a lower level robot arm control system, which allows obstacles to be avoided using a relatively rough path and without precise map information. To achieve autonomous arm movements, the overall arm motion is divided into an obstacle avoidance component and a target point adjustment component, and movement is selected with regard to the distance between the arm and the obstacle by a higher control unit. A fuzzy rule is used for mode selection in order to maintain continuous change of control mode. The control method applied to two degrees of freedom in two dimensional space was examined by numerical simulations. The simulations verified the autonomous obstacle avoidance capability and the ability to continuously control joint angles using fuzzy logic. (author)

  12. A fuzzy logic based navigation for mobile robot

    The main issue of intelligent robot is how to reach its goal safely in real time when it moves in unknown environment. The navigational planning is becoming the central issue in development of real-time autonomous mobile robots. Behaviour based robots have been successful in reacting with dynamic environment but still there are some complexity and challenging problems. Fuzzy based behaviours present as powerful method to solve the real time reactive navigation problems in unknown environment. We shall classify the navigation generation methods, five some characteristics of these methods, explain why fuzzy logic is suitable for the navigation of mobile robot and automated guided vehicle, and describe a reactive navigation that is flexible to react through their behaviours to the change of the environment. Some simulation results will be presented to show the navigation of the robot. (Author)

  13. A Note on Axiomatizations of Pavelka-style Complete Fuzzy Logics

    Cintula, Petr

    2016-01-01

    Roč. 292, 1 June (2016), s. 160-174. ISSN 0165-0114 R&D Projects: GA ČR GAP202/10/1826 Institutional support: RVO:67985807 Keywords : mathematical fuzzy logic * Pavelka-style completeness * MTL logic * Lukasiewicz logics * Product Logic * truth constants * Monteiro–Baaz delta Subject RIV: BA - General Mathematics Impact factor: 1.986, year: 2014

  14. Logic reliability analysis of adaptive control strategies

    An approach is developed for the evaluation of the reliability of logic of adaptive control strategies, taking into account logic structural complexity and potential failure of programming modules. Flaws in the control system algorithm may not be discovered during debugging or initial testing and may only affect the performance under abnormal situations although the system may appear reliable in normal operations. Considering an adaptive control system designed for use in control of equipment employed in nuclear power stations, logic reliability evaluation is demonstrated. The approach given is applicable to any other designs and may be used to compare different control system logic structures from the reliability viewpoint. Evaluation of the reliability of control systems is essential to automated operation of equipment used in nuclear power plants. (author)

  15. Fuzzy Logic Approach to Diagnosis of Feedwater Heater Performance Degradation

    Since failure in, damage to, and performance degradation of power generation components in operation under harsh environment of high pressure and high temperature may cause both economic and human loss at power plants, highly reliable operation and control of these components are necessary. Therefore, a systematic method of diagnosing the condition of these components in its early stages is required. There have been many researches related to the diagnosis of these components, but our group developed an approach using a regression model and diagnosis table, specializing in diagnosis relating to thermal efficiency degradation of power plant. However, there was a difficulty in applying the method using the regression model to power plants with different operating conditions because the model was sensitive to value. In case of the method that uses diagnosis table, it was difficult to find the level at which each performance degradation factor had an effect on the components. Therefore, fuzzy logic was introduced in order to diagnose performance degradation using both qualitative and quantitative results obtained from the components' operation data. The model makes performance degradation assessment using various performance degradation variables according to the input rule constructed based on fuzzy logic. The purpose of the model is to help the operator diagnose performance degradation of components of power plants. This paper makes an analysis of power plant feedwater heater by using fuzzy logic. Feedwater heater is one of the core components that regulate life-cycle of a power plant. Performance degradation has a direct effect on power generation efficiency. It is not easy to observe performance degradation of feedwater heater. However, on the other hand, troubles such as tube leakage may bring simultaneous damage to the tube bundle and therefore it is the object of concern in economic aspect. This study explains the process of diagnosing and verifying typical

  16. Fuzzy Logic Controller for Automatic Nuclear Power Plant

    Reactor output power stabilization is the desired goal for any reactor. During the operation of the reactor, different changes in its operating conditions occur.Therefore, an automatic reactor power control is required to compensate the reactivity changes. To achieve the optimal stabilization of reactor output power, Proportional-Integral-Derivative (PID) and Fuzzy Logic Controller (FLC) approaches are developed. Evaluation of each approach is discussed. A developed reactor power plant model is suggested to analyze and compare PID and FLC controller approaches. The simulation results show that FLC controller is a good approach for automatic reactor output power control

  17. A Semantics for Counterfactuals Based on Fuzzy Logic

    Běhounek, Libor; Majer, Ondrej

    London : College Publications, 2011 - (Peliš, M.; Punčochář, V.), s. 25-41 ISBN 978-1-84890-038-7. [ LOGICA 2010. Hejnice (CZ), 21.06.2010-25.06.2010] R&D Projects: GA AV ČR IAA900090703; GA ČR GAP202/10/1826; GA ČR GEICC/08/E018 Institutional research plan: CEZ:AV0Z10300504; CEZ:AV0Z90090514 Keywords : counterfactual conditional * mathematical fuzzy logic * Lewis-Stalnaker semantics * similarity relation Subject RIV: BA - General Mathematics

  18. Fuzzy Logic Trajectory Tracking Controller for a Tanker

    Dur Muhammad Pathan

    2012-04-01

    Full Text Available This paper proposes a fuzzy logic controller for design of autopilot of a ship. Triangular membership functions have been use for fuzzification and the centroid method for defuzzification. A nonlinear mathematical model of an oil tanker has been considered whose parameters vary with the depth of water. The performance of proposed controller has been tested under both course changing and trajectory keeping mode of operations. It has been demonstrated that the performance is robust in shallow as well as deep waters.

  19. Fuzzy Logic Based Controller for Brushless DC Motor

    Trinayani Chittajallu

    2012-01-01

    In this paper, a model of a three phase star – connected brushless direct current (BLDC) motor is presented. The state-space model for a BLDC motor is derived and is implemented using Matlab/Simulink. Torque and Speed control is applied using hysteresis band control and variable DC-link voltage control. The different control strategies are tested on the BLDC motor and their performance is evaluated. A Fuzzy Logic Controller(FLC) is also developed to control the torque and speed of BLDC motor ...

  20. Genetic algorithms and fuzzy logic systems soft computing perspectives

    Sanchez, Elie; Zadeh, Lotfi A

    1997-01-01

    Ever since fuzzy logic was introduced by Lotfi Zadeh in the mid-sixties and genetic algorithms by John Holland in the early seventies, these two fields widely been subjects of academic research the world over. During the last few years, they have been experiencing extremely rapid growth in the industrial world, where they have been shown to be very effective in solving real-world problems. These two substantial fields, together with neurocomputing techniques, are recognized as major parts of soft computing: a set of computing technologies already riding the waves of the next century to produce

  1. Fuzzy Logic Approach to Diagnosis of Feedwater Heater Performance Degradation

    Kang, Yeon Kwan; Kim, Hyeon Min; Heo, Gyun Young [Kyung Hee University, Yongin (Korea, Republic of); Sang, Seok Yoon [Engineering and Technical Center, Korea Hydro, Daejeon (Korea, Republic of)

    2014-08-15

    Since failure in, damage to, and performance degradation of power generation components in operation under harsh environment of high pressure and high temperature may cause both economic and human loss at power plants, highly reliable operation and control of these components are necessary. Therefore, a systematic method of diagnosing the condition of these components in its early stages is required. There have been many researches related to the diagnosis of these components, but our group developed an approach using a regression model and diagnosis table, specializing in diagnosis relating to thermal efficiency degradation of power plant. However, there was a difficulty in applying the method using the regression model to power plants with different operating conditions because the model was sensitive to value. In case of the method that uses diagnosis table, it was difficult to find the level at which each performance degradation factor had an effect on the components. Therefore, fuzzy logic was introduced in order to diagnose performance degradation using both qualitative and quantitative results obtained from the components' operation data. The model makes performance degradation assessment using various performance degradation variables according to the input rule constructed based on fuzzy logic. The purpose of the model is to help the operator diagnose performance degradation of components of power plants. This paper makes an analysis of power plant feedwater heater by using fuzzy logic. Feedwater heater is one of the core components that regulate life-cycle of a power plant. Performance degradation has a direct effect on power generation efficiency. It is not easy to observe performance degradation of feedwater heater. However, on the other hand, troubles such as tube leakage may bring simultaneous damage to the tube bundle and therefore it is the object of concern in economic aspect. This study explains the process of diagnosing and verifying typical

  2. 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...

  3. Application of fuzzy logic control system for reactor feed-water control

    The successful actual application of a fuzzy logic control system to the a nuclear Fugen nuclear power reactor is described. Fugen is a heavy-water moderated, light-water cooled reactor. The introduction of fuzzy logic control system has enabled operators to control the steam drum water level more effectively in comparison to a conventional proportional-integral (PI) control system

  4. Fuzzy Logic Control of Wind Turbine System Connection to PM Synchronous Generator for Maximum Power Point Tracking

    Hadi Sefidgar; S.Asghar Gholamian

    2014-01-01

    in this paper, a fuzzy logic control (FLC) is proposed for maximum power point tracking (MPPT) in wind turbine connection to Permanent Magnet Synchronous Generator (PMSG). The proposed fuzzy logic controller tracks the maximum power point (MPP) by measurements the load voltage and current. This controller calculates the load power and sent through the fuzzy logic system. The main goal of this paper is design of the fuzzy logic controller in the model of DC-DC converter (boost converter). This...

  5. Direct Torque Control System for a Three Phase Induction Motor With Fuzzy Logic Based Speed Controller

    Turki Y. Abdalla

    2010-12-01

    Full Text Available This paper presents a method for improving the speed profile of a three phase induction motor in direct torque control (DTC drive system using a proposed fuzzy logic based speed controller. A complete simulation of the conventional DTC and closed-loop for speed control of three phase induction motor was tested using well known Matlab/Simulink software package. The speed control of the induction motor is done by using the conventional proportional integral (PI controller and the proposed fuzzy logic based controller. The proposed fuzzy logic controller has a nature of (PI to determine the torque reference for the motor. The dynamic response has been clearly tested for both conventional and the proposed fuzzy logic based speed controllers. The simulation results showed a better dynamic performance of the induction motor when using the proposed fuzzy logic based speed controller compared with the conventional type with a fixed (PI controller.

  6. The universal fuzzy logical framework of neural circuits and its application in modeling primary visual cortex

    2008-01-01

    Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells’ dynamical equations. Al- though there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.

  7. Fuzzy-Based Adaptive Hybrid Burst Assembly Technique for Optical Burst Switched Networks

    Abubakar Muhammad Umaru; Muhammad Shafie Abd Latiff; Yahaya Coulibaly

    2014-01-01

    The optical burst switching (OBS) paradigm is perceived as an intermediate switching technology for future all-optical networks. Burst assembly that is the first process in OBS is the focus of this paper. In this paper, an intelligent hybrid burst assembly algorithm that is based on fuzzy logic is proposed. The new algorithm is evaluated against the traditional hybrid burst assembly algorithm and the fuzzy adaptive threshold (FAT) burst assembly algorithm via simulation. Simulation results sh...

  8. Quantitative high resolution electron microscopy of III-V compounds: A fuzzy logic approach

    Hillebrand, R.; Hofmeister, H.; Werner, P.; Gösele, U.

    1995-09-01

    In the study of interdiffusion phenomena in layered structures of III-V compounds by high resolution electron microscopy, contrast features in the micrographs can be correlated with the variation of the chemical composition of the crystals. For quantitative interpretation of the micrographs a fuzzy logic approach is adapted to extract chemical information. The linguistic variable ``similarity of images'' is derived from the standard deviation (SD) of their difference patterns, which proved to be an appropriate measure. The approach developed is used to analyze simulated contrast tableaus of GaAs/P (As/P variation) and experimental micrographs of Al/GaAs (Al/Ga variation).

  9. Power-Constrained Fuzzy Logic Control of Video Streaming over a Wireless Interconnect

    Mohammed Ghanbari

    2008-06-01

    Full Text Available Wireless communication of video, with Bluetooth as an example, represents a compromise between channel conditions, display and decode deadlines, and energy constraints. This paper proposes fuzzy logic control (FLC of automatic repeat request (ARQ as a way of reconciling these factors, with a 40% saving in power in the worst channel conditions from economizing on transmissions when channel errors occur. Whatever the channel conditions are, FLC is shown to outperform the default Bluetooth scheme and an alternative Bluetooth-adaptive ARQ scheme in terms of reduced packet loss and delay, as well as improved video quality.

  10. Adaptive Neuro-fuzzy approach in friction identification

    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.

  11. Controlling Smart Green House Using Fuzzy Logic Method

    Rafiuddin Syam

    2015-10-01

    Full Text Available To increase agricultural output it is needed a system that can help the environmental conditions for optimum plant growth. Smart greenhouse allows for plants to grow optimally, because the temperature and humidity can be controlled so that no drastic changes. It is necessary for optimal smart greenhouse needed a system to manipulate the environment in accordance with the needs of the plant. In this case the setting temperature and humidity in the greenhouse according to the needs of the plant. So using an automated system for keeping such environmental condition is important. In this study, the authors use fuzzy logic to make the duration of watering the plants more dynamic in accordance with the input temperature and humidity so that the temperature and humidity in the green house plants maintained in accordance to the reference condition. Based on the experimental results using fuzzy logic method is effective to control the duration of watering and to maintain the optimum temperature and humidity inside the greenhouse

  12. Fuzzy logic control of vehicle suspensions with dry friction nonlinearity

    L Emir Sakman; Rahmi Guclu; Nurkan Yagiz

    2005-10-01

    We design and investigate the performance of fuzzy logic-controlled (FLC) active suspensions on a nonlinear vehicle model with four degrees of freedom, without causing any degeneration in suspension working limits. Force actuators were mounted parallel to the suspensions. In this new approach, linear combinations of the vertical velocities of the suspension ends and accelerations of the points of connection of the suspension to the body have been used as input variables. The study clearly demonstrates the effectiveness of the fuzzy logic controller for active suspension systems. Suspension working space degeneration is the most important problem in various applications. Decreasing the amplitudes of vehicle body vibrations improves ride comfort. Body bounce and pitch motion of the vehicle are presented both in time domain when travelling over a ramp-step road profile and in frequency domain. The results are compared with those of uncontrolled systems. At the end of this study, the performance and the advantage of the suggested approach and the improvement in ride comfort are discussed.

  13. Sensor Network Self-Localization Using Fuzzy Logic

    Arash Dana

    2007-12-01

    Full Text Available Location awareness is an important capability for a series of enhanced wireless businesses. sensor networks are dense wireless networks of small low cost sensors, which collect and disseminate environmental data, for monitoring, military application and so on. Localization is an unconstrained optimization problem. position estimation is based on various, distance / path measures, which include anchor and non-anchor nodes. Anchor positions, have been predetermined to help us localize other nodes. This study proposes using a combination of fuzzy techniques, and advanced APS method, to estimate unknown nodes. In a network with twenty hundred nodes of which twenty percent operates as anchors. These nodes localize the other one hundred and sixties. It is necessary to select the best four anchors for localizing. We suppose that the anchors neighbor to unknown nodes are the best. It is time-consuming to find the distance of unknown anchors in such a widespread network. Using the fuzzy logic, putting the limitation of distance, and selecting the nearest anchor to the unknown node, the nearest four anchoress can be selected. In this case the rate of localization error will be decreased due to selecting neighbor anchors. Therefore, we can localize nodes by using ad-hoc positioning system. Fuzzy rules help us to estimate position in less than 2.4 seconds with mean normal positioning deviation of z =0.4597.

  14. Software Operational Profile Based Test Case Allocation Using Fuzzy Logic

    2007-01-01

    Software operational profile (SOP) is used in software reliability prediction, software quality assessment, performance analysis of software, test case allocation, determination of "when to stop testing," etc. Due to the limited data resources and large efforts required to collect and convert the gathered data into point estimates, reluctance is observed by the software professionals to develop the SOP. A framework is proposed to develop SOP using fuzzy logic, which requires usage data in the form of linguistics. The resulting profile is named fuzzy software operational profile (FSOP). Based on this work, this paper proposes a generalized approach for the allocation of test cases, in which occurrence probability of operations obtained from FSOP are combined with the criticality of the operations using fuzzy inference system (FIS). Traditional methods for the allocation of test cases do not consider the application in which software operates. This is intuitively incorrect. To solve this problem, allocation of test cases with respect to software application using the FIS model is also proposed in this paper.

  15. Fuzzy Logic-Based Secure and Fault Tolerant Job Scheduling in Grid

    WANG Cheng; JIANG Congfeng; LIU Xiaohu

    2007-01-01

    The uncertainties of grid sites security are main hurdle to make the job scheduling secure, reliable and fault-tolerant. Most existing scheduling algorithms use fixed-number job replications to provide fault tolerant ability and high scheduling success rate, which consume excessive resources or can not provide sufficient fault tolerant functions when grid security conditions change. In this paper a fuzzy-logic-based self-adaptive replication scheduling (FSARS) algorithm is proposed to handle the fuzziness or uncertainties of job replication number which is highly related to trust factors behind grid sites and user jobs. Remote sens-ing-based soil moisture extraction (RSBSME) workload experiments in real grid environment are performed to evaluate the proposed approach and the results show that high scheduling success rate of up to 95% and less grid resource utilization can be achieved through FSARS. Extensive experiments show that FSARS scales well when user jobs and grid sites increase.

  16. Hot metal temperature prediction and simulation by fuzzy logic in a blast furnace

    This work describes the development and further validation of a model devoted to blast furnace hot metal temperature forecast, based on Fuzzy logic principles. The model employs as input variables, the control variables of an actual blast furnace: Blast volume, moisture, coal injection, oxygen addition, etc. and it yields as a result the hot metal temperature with a forecast horizon of forty minutes. As far as the variables used to develop the model have been obtained from data supplied by an actual blast furnaces sensors, it is necessary to properly analyse and handle such data. Especial attention was paid to data temporal correlation, fitting by interpolation the different sampling rates. In the training stage of the model the ANFIS (Adaptive Neuro-Fuzzy Inference System) and the Subtractive Clustering algorithms have been used. (Author) 9 refs

  17. Comparative Study of Fuzzy Logic Based Speed Control of Multilevel Inverter fed Brushless DC Motor Drive

    Pritha Agrawal

    2014-02-01

    Full Text Available This paper presents a comparative analysis of speed control of brushless DC motor (BLDC drive fed with conventional two-level, three and five level diode clamped multilevel inverter (DC-MLI. The performance of the drive system is successfully evaluated using Fuzzy Logic (FL based speed controller. The control structure of the proposed drive system is described. The speed and torque characteristic of conventional two-level inverter is compared with the three and five-level multilevel inverter (MLI for various operating conditions. The three and five level diode clamped multilevel inverters are simulated using IGBT’s and the mathematical model of BLDC motor has been developed in MATLAB/SIMULINK environment. The simulation results show that the Fuzzy based speed controller eliminate torque ripples and provides fast speed response. The developed Fuzzy Logic model has the ability to learn instantaneously and adapt its own controller parameters based on disturbances with minimum steady state error, overshoot and rise time of the output voltage.

  18. Fuzzy logic and its possibility using in automation of small-scale hydroelectric power plants regulation

    The paper explains how can computer understand and process inaccurate (indefinite) information. It is processing of terms like e.g. 'around in the middle of month' or 'not too big'. Fuzzy logic, fuzzy sets, operations with them, fuzzy rules and using of linguistics variables are explained. The possibilities of application of fuzzy systems in automation of regulation of small-scale hydro power plants are discussed. (author)

  19. Automatic detection of cardiac contours on MR images using fuzzy logic and dynamic programming.

    Lalande, A; Legrand, L; Walker, P M; Jaulent, M. C.; Guy, F.; Cottin, Y; Brunotte, F

    1997-01-01

    This paper deals with the use of fuzzy logic and dynamic programming in the detection of cardiac contours in MR Images. The definition of two parameters for each pixel allows the construction of the fuzzy set of the cardiac contour points. The first parameter takes into account the grey level, and the second the presence of an edge. A corresponding fuzzy matrix is derived from the initial image. Finally, a dynamic programming with graph searching is performed on this fuzzy matrix. The method ...

  20. Application of the fuzzy logic in content-based image retrieval

    Xiaoling, Wang; Kanglin, Xie

    2005-01-01

    This paper imports the fuzzy logic into image retrieval to deal with the vagueness and ambiguity of human judgment of image similarity. Our retrieval system has the following properties: firstly adopting the fuzzy language variables to describe the similarity degree of image features, not the features themselves; secondly making use of the fuzzy inference to instruct the weights assignment among various image features; thirdly expressing the subjectivity of human perceptions by fuzzy rules im...

  1. A Fuzzy Logic-Controlled Superconducting Magnetic Energy Storage (SMES) for Transient Stability Augmentation

    Ali, Mohd.Hasan; MURATA, Toshiaki; Tamura, Junji

    2007-01-01

    This paper presents a fuzzy logic-controlled superconducting magnetic energy storage (SMES) to improve the transient stability of an electric power system. In order to see how effective the proposed fuzzy controlled SMES in improving the transient stability is, its performance is compared to that of a conventional proportional-integral (PI) controlled SMES. Furthermore, a comparative study between the fuzzy controlled SMES and fuzzy controlled braking resistor (BR) is carried out. Simulation ...

  2. Application of a PID controller based on fuzzy logic to reduce variations in the control parameters in PWR reactors

    Nuclear reactors are in nature nonlinear systems and their parameters vary with time as a function of power level. These characteristics must be considered if large power variations occur in power plant operational regimes, such as in load-following conditions. A PWR reactor has a component called pressurizer, whose function is to supply the necessary high pressure for its operation and to contain pressure variations in the primary cooling system. The use of control systems capable of reducing fast variations of the operation variables and to maintain the stability of this system is of fundamental importance. The best-known controllers used in industrial control processes are proportional-integral-derivative (PID) controllers due to their simple structure and robust performance in a wide range of operating conditions. However, designing a fuzzy controller is seen to be a much less difficult task. Once a Fuzzy Logic controller is designed for a particular set of parameters of the nonlinear element, it yields satisfactory performance for a range of these parameters. The objective of this work is to develop fuzzy proportional-integral-derivative (fuzzy-PID) control strategies to control the level of water in the reactor. In the study of the pressurizer, several computer codes are used to simulate its dynamic behavior. At the fuzzy-PID control strategy, the fuzzy logic controller is exploited to extend the finite sets of PID gains to the possible combinations of PID gains in stable region. Thus the fuzzy logic controller tunes the gain of PID controller to adapt the model with changes in the water level of reactor. The simulation results showed a favorable performance with the use to fuzzy-PID controllers. (author)

  3. Comments on Fuzzy Logic and Higher-Order Vagueness by Nicholas J. J. Smith

    Běhounek, Libor

    London : College Publications, 2011 - (Cintula, P.; Fermüller, G.; Godo, L.; Hájek, P.), s. 21-28 ISBN 978-1-84890-037-0. - (Studies in Logic. 36) R&D Projects: GA ČR GEICC/08/E018 Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy plurivaluationism * vagueness * mathematical fuzzy logic * semantic indeterminacy Subject RIV: BA - General Mathematics http://www.logic.at/lomorevi/vaguebook/behounek-on-smith.pdf

  4. Force control of a tri-layer conducting polymer actuator using optimized fuzzy logic control

    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. (paper)

  5. Adaptive Fuzzy Output-Feedback Method Applied to Fin Control for Time-Delay Ship Roll Stabilization

    Rui Bai

    2014-01-01

    Full Text Available The ship roll stabilization by fin control system is considered in this paper. Assuming that angular velocity in roll cannot be measured, an adaptive fuzzy output-feedback control is investigated. The fuzzy logic system is used to approximate the uncertain term of the controlled system, and a fuzzy state observer is designed to estimate the unmeasured states. By utilizing the fuzzy state observer and combining the adaptive backstepping technique with adaptive fuzzy control design, an observer-based adaptive fuzzy output-feedback control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB, and the control strategy is effective to decrease the roll motion. Simulation results are included to illustrate the effectiveness of the proposed approach.

  6. Fuzzy logics acquisition and simulation modules for expert systems to assist operator's decision for nuclear power stations

    A new type of fuzzy expert system for assisting the operator's decisions in nuclear power plant system in non-standard situations is proposed. This expert system is based on new approaches to fuzzy logics acquisition and to fuzzy logics testing. Fuzzy logics can be generated by a T-norms axiomatic system to choose the most suitable to operator's way of thinking. Then the chosen fuzzy logic is tested by simulation of inference process in expert system. The designed logic is the input of inference module of expert system

  7. Adaptive fuzzy multivariable controller design based on genetic algorithm for an air handling unit

    In HVAC (Heating, Ventilation and Air Conditioning systems, effective thermal management is required because energy and operation costs of buildings are directly influenced by how well an air-conditioning system performs. HVAC systems are typically nonlinear time varying with disturbances, where conventional PID controllers may trade-off between stability and rise time. To overcome this limitation, a Genetic Algorithm based AFLC (Adaptive Fuzzy Logic Controller design has been proposed for the multivariable control of temperature and humidity of a typical AHU (air handling unit by manipulating valve positions to adjust the water and steam flow rates. Modulating equal percentage Globe valves for chilled water and steam have been modeled according to exact flow rates of water and steam. A novel method for the adaptation of FLC (Fuzzy Logic Controller by modifying FRM (Fuzzy Rule Matrix based on GA (genetic algorithm) has been proposed. This requires re-designing the complete FLC in MATLAB/Simulink whose procedure has also been proposed. The proposed adaptive controller outperforms the existing fuzzy controller in terms of steady state error, rise time and settling time. - Highlights: • GA based Adaptive Fuzzy Logic Controller to improve performance of HVAC system. • Multivariable control of an air handling unit to adjust the water and steam flow rates. • Significant improvement in steady state error, rise time and settling time of the control system

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

    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.

  9. First-Order ARMA Type Fuzzy Time Series Method Based on Fuzzy Logic Relation Tables

    Cem Kocak

    2013-01-01

    Full Text Available Fuzzy time series approaches have an important deficiency according to classical time series approaches. This deficiency comes from the fact that all of the fuzzy time series models developed in the literature use autoregressive (AR variables, without any studies that also make use of moving averages (MAs variables with the exception of only one study (Egrioglu et al. (2013. In order to eliminate this deficiency, it is necessary to have many of daily life time series be expressed with Autoregressive Moving Averages (ARMAs models that are based not only on the lagged values of the time series (AR variables but also on the lagged values of the error series (MA variables. To that end, a new first-order fuzzy ARMA(1,1 time series forecasting method solution algorithm based on fuzzy logic group relation tables has been developed. The new method proposed has been compared against some methods in the literature by applying them on Istanbul Stock Exchange national 100 index (IMKB and Gold Prices time series in regards to forecasting performance.

  10. Robust observer-based adaptive fuzzy sliding mode controller

    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.

  11. A New Approach for Lossless Image Compression Based on Fuzzy Adaptive Prediction

    Wu Yingqian(吴颖谦); Fang Tao; Shi Pengfei

    2004-01-01

    This paper proposes a novel approach for image lossless compression based on fuzzy logic and adaptive prediction. By a flexible strategy, the method can acquire a set of original predictors describing the more detail characteristic. Using a neural network, the proposed method can more efficiently organize the training of original predictors and implement adaptive prediction in fuzzy style. In entropy coding phase, the context-based conditional adaptive arithmetic encoding is adopted. The experiments demonstrate the characteristics make the approach achieve good tradeoff between computational complexity and efficiency of prediction and good performance for lossless compression.

  12. Cheap diagnosis using structural modelling and fuzzy-logic based detection

    Izadi-Zamanabadi, Roozbeh; Blanke, Mogens; Katebi, Serajeddin

    2003-01-01

    relations for linear or non-linear dynamic behaviour, and combine this with fuzzy output observer design to provide an effective diagnostic approach. An adaptive neuro-fuzzy inference method is used. A fuzzy adaptive threshold is employed to cope with practical uncertainty. The methods are demonstrated...

  13. Adaptive fuzzy logic control for solar buildings

    El-Deen, M. M. G. Naser

    2002-01-01

    Significant progress has been made on maximising passive solar heating loads through the careful selection of glazing, orientation and internal mass within building spaces. Control of space heating in buildings of this type has become a complex problem. Additionally, and in common with most building control applications, there is a need to develop control solutions that permit simple and transparent set up and commissioning procedures. This work concerns the development and testing of an adap...

  14. Fuzzy logic for identifying pigments studied by Raman spectroscopy.

    Ramos, Pablo Manuel; Ferré, Joan; Ruisánchez, Itziar; Andrikopoulos, Konstantinos S

    2004-07-01

    Fuzzy logic and linguistic variables are used for the automatic interpretation of Raman spectra obtained from pigments found in cultural heritage art objects. Featured bands are extracted from a Raman spectrum of a reference pigment and the methodology for constructing the library is illustrated. An unknown spectrum is then interpreted automatically and a process for identifying the corresponding pigment is described. A reference library consisting of 32 pigments was built and the effectiveness of the algorithm was tested by the Raman spectroscopic analysis of 10 pigments that are known to have been extensively used in Byzantine hagiography. Binary mixtures of these pigments were also tested. The algorithm's level of identification was good even though extra peaks, noise, and background signals were encountered in the spectra. PMID:15282052

  15. Pneumatic motor speed control by trajectory tracking fuzzy logic controller

    Cengiz Safak; Vedat Topuz; A Fevzi Baba

    2010-02-01

    In this study, trajectory tracking fuzzy logic controller (TTFLC) is proposed for the speed control of a pneumatic motor (PM). A third order trajectory is defined to determine the trajectory function that has to be tracked by the PM speed. Genetic algorithm (GA) is used to find the TTFLC boundary values of membership functions (MF) and weights of control rules. In addition, artificial neural networks (ANN) modelled dynamic behaviour of PM is given. This ANN model is used to find the optimal TTFLC parameters by offline GA approach. The experimental results show that designed TTFLC successfully enables the PM speed track the given trajectory under various working conditions. The proposed approach is superior to PID controller. It also provides simple and easy design procedure for the PM speed control problem.

  16. Control of a gravity gradient stabilised satellite using fuzzy logic

    Aage Skullestad

    2001-07-01

    Full Text Available This paper describes attitude control of a small gravity gradient stabilised satellite. A gravity gradient stabilised satellite has limited stability and pointing capabilities, and magnetic coils are added in order to improve the accuracy of the attitude control. The magnetic coils are controlled using a fuzzy logic controller, based on a combination of membership functions and rules. The control of the pitch axis is separated from the roll and azimuth axes and excellent pitch angle accuracy is achieved. The roll and azimuth axes are controlled using a common magnetic coil, that has a non-linear and time-varying torque characteristic and, therefore, accurate roll and azimuth angular control become much more difficult to achieve. However, combining one roll controller and two azimuth controllers result in an acceptable roll and azimuth angular accuracy after a few orbital periods.

  17. Fuzzy Logic Based Rotor Health Index of Induction Motor

    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.

  18. Fuzzy logic controller for automatic nuclear power plant

    In Nuclear power reactors, power stabilization is the desired goal for any reactor. During the normal operation of the reactor, different changes in its operating conditions occur such as: fuel bum-up, xenon isotope production, temperature and/or environmental changes. Therefore, an automatic nuclear power reactor control is required to compensate the reactivity changes produced by such variations. proportional-integral-derivative controller (PID), and fuzzy logic controller (FLC) schemes are discussed to achieve the optimal stabilization of power. Both PID and FLC controllers were developed as well as the reactor power plant model in order to analyze their performance. The simulation results show that FLC controller gives faster and better response. 6-16 figs., 12 refs

  19. Different control applications on a vehicle using fuzzy logic control

    Nurkan Yagiz; L Emir Sakman; Rahmi Guclu

    2008-02-01

    In this paper, the active suspension control of a vehicle model that has five degrees of freedom with a passenger seat using a fuzzy logic controller is studied. Three cases are taken into account as different control applications. In the first case, the vehicle model having passive suspensions with an active passenger seat is controlled. In the second case, active suspensions with passive passenger seat combination are controlled. In the third case, both the passenger seat and suspensions have active controllers. Vibrations of the passenger seat in the three cases due to road bump input are simulated. At the end of the study, the results are compared in order to select the combination that supplies the best ride comfort.

  20. Transient Stability Assessment using Decision Trees and Fuzzy Logic Techniques

    A. Y. Abdelaziz

    2013-09-01

    Full Text Available Many techniques are used for Transient Stability assessment (TSA of synchronous generators encompassing traditional time domain state numerical integration, Lyapunov based methods, probabilistic approaches and Artificial Intelligence (AI techniques like pattern recognition and artificial neural networks.This paper examines another two proposed artificial intelligence techniques to tackle the transient stability problem. The first technique is based on the Inductive Inference Reasoning (IIR approach which belongs to a particular family of machine learning from examples. The second presents a simple fuzzy logic classifier system for TSA. Not only steady state but transient attributes are used for transient stability estimation so as to reflect machine dynamics and network changes due to faults.The two techniques are tested on a standard test power system. The performance evaluation demonstrated satisfactory results in early detection of machine instability. The advantage of the two techniques is that they are straightforward and simple for on-line implementation.

  1. Forest fire autonomous decision system based on fuzzy logic

    Lei, Z.; Lu, Jianhua

    2010-11-01

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

  2. A framework for analysis of extended fuzzy logic

    Farnaz SABAHI; M.-R.AKBARZADEH-T

    2014-01-01

    We address a framework for the analysis of extended fuzzy logic (FLe) and elaborate mainly the key characteris-tics of FLe by proving several qualification theorems and proposing a new mathematical tool named the A-granule. Specifically, we reveal that within FLe a solution in the presence of incomplete information approaches the one gained by complete infor-mation. It is also proved that the answers and their validities have a structural isomorphism within the same context. This rela-tionship is then used to prove the representation theorem that addresses the rationality of FLe-based reasoning. As a conse-quence of the developed theoretical description of FLe, we assert that in order to solve a problem, having complete information is not a critical need; however, with more information, the answers achieved become more specific. Furthermore, reasoning based on FLe has the advantage of being computationally less expensive in the analysis of a given problem and is faster.

  3. Assessment of nuclear energy sustainability index using fuzzy logic

    Nuclear energy is increasingly perceived as an attractive mature energy generation technology that can deliver an answer to the worldwide increasing energy demand while respecting environmental concerns as well as contributing to a reduced dependence on fossil fuel. Advancing nuclear energy deployment demands an assessment of nuclear energy with respect to all sustainability dimensions. In this paper, the nuclear energy, whose sustainability will be assessed, is governed by the dynamics of three subsystems: environmental, economic, and sociopolitical. The overall sustainability is then a non-linear function of the individual sustainabilities. Each subsystem is evaluated by means of many components (pressure, status, and response). The combination of each group of indicators by means of fuzzy logic provides a measurement of sustainability for each subsystem.

  4. Efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control

    Allaoua Boumediene

    2008-01-01

    Full Text Available This paper presents the application of Fuzzy Logic for DC motor speed control using Particle Swarm Optimization (PSO. Firstly, the controller designed according to Fuzzy Logic rules is such that the systems are fundamentally robust. Secondly, the Fuzzy Logic controller (FLC used earlier was optimized with PSO so as to obtain optimal adjustment of the membership functions only. Finally, the FLC is completely optimized by Swarm Intelligence Algorithms. Digital simulation results demonstrate that in comparison with the FLC the designed FLC-PSO speed controller obtains better dynamic behavior and superior performance of the DC motor, as well as perfect speed tracking with no overshoot.

  5. A study on the application of the fuzzy logic controllers(III)

    In this paper, we first present mathematical analysis of what causes the fuzzy logic controllers perform better than PI controllers and an experimental verifications of it by using a model nuclear steam generator. Next, we developed a fuzzy algorithm for tuning of a PI controller and confirmed that the fuzzy logic can turn out to be more useful than Boolean logic. Third, we developed a one-node model for the prediction of the steam generator water level during its swell and shrink due to the steam dump valve operations. 30 figs, 9 tabs, 12 refs. (Author)

  6. Optimal Capacitor For Maximum Output Power Tracking Of Self Excited Induction Generator Using Fuzzy Logic Approach

    Mr.M.Senthilkumar

    2010-08-01

    Full Text Available This paper aims to determine the optimal capacitors required for maximum output power of a single phase self excited induction generator (SEIG. This paper deals with theoretical, fuzzy logic and practical approach in order to extract the values of optimal capacitor for maximum output power .To find this capacitor value, nonlinear equations have to be solved from the equivalent circuit of SEIG. The advantages of using fuzzy logic approach are universal control algorithm, fast converging, accepting of noise and inaccurate signals. At the end of the paper the theoretical and fuzzy logic results are verified with experimental values.

  7. Design of Sliding Mode Controller Enhanced by Fuzzy Logic Algorithm for Industrial Robot

    Vijay Tiwari

    2013-11-01

    Full Text Available In this paper a sliding mode control enhanced by fuzzy logic algorithm method is proposed for the robust tracking control of industrial robot manipulator. The proposed controller ensures the advantage of fuzzy logic algorithm and sliding mode control. There are two parts of the proposed method: first the design of sliding mode control for robust stability and second the development of fuzzy logic algorithms to reduce chattering effectively. The stability of control is proven by Lyapunov stability method and the performance of tracking error is shown in a table by using RMS value.

  8. TORQUE RIPPLE MINIMIZATION IN SWITCHED RELUCTANCE MOTORS USING PID FUZZY LOGIC CONTROLLER

    K. Deepak; Nagarajan, G.

    2014-01-01

    The main objective of this paper is to design a system which will have small speed ripple and also produces fast response of switch reluctance motor (SRM) for various speeds , magnetic flux and current by means of PID fuzzy logic controller. The speed of motor is get increased by means of reducing the torque value and also by means of ripple content. The SRM will haves a PI fuzzy logic controller and a derivative part. The developed novel PID-like fuzzy logic controller (FLC) ...

  9. Control of vehicle active suspensions by using PD+PI type fuzzy logic with sliding surface

    A PD+PI type fuzzy logic controller with sliding surface is presented in this study. This controller consists of two parts which are PD type and PI type fuzzy logic units. Inputs to those fuzzy logic units are the sliding surface functions and their derivatives. The integrated controller is applied to two degrees of freedom vehicle active suspension model. Both time and frequency domain analysis are evaluated. Numerical results demonstrate that the proposed controller improves the vibration isolation of the vehicle body, without causing a suspension degeneration problem and without degrading road holding very much.

  10. Identification of Optimal Operating Point Of PV Modules Using Fuzzy Logic Control

    Hadi nabizadeh

    2013-11-01

    Full Text Available This paper introduces an intelligent control method for maximum power point tracking in solar array in dealing with the rapid variations in temperature and radiation. Fuzzy logic controller and DC/DC boost converter are the most important components of this system. The simulation results of fuzzy logic controller are compared with simulation results of PI controller in both cases without noise and with Gaussian noise in solar cell voltage. The results show that fuzzy logic controller performance is better than PI controller especially in the presence of noise.

  11. Using fuzzy logic for automatic control: Case study of a problem of cereals samples classification

    Lakhoua Najeh Mohamed

    2009-01-01

    Full Text Available The aim of this paper is to present the use of fuzzy logic for automatic control of industrial systems particularly the way to approach a problem of classification. We present a case study of a grading system of cereals that allows us to determine the price of transactions of cereals in Tunisia. Our contribution in this work consists in proposing not only an application of the fuzzy logic on the grading system of cereals but also a methodology enabling the proposing of a new grading system based on the concept of 'Grade' while using the fuzzy logic techniques. .

  12. Virtual Reality Simulation of Fuzzy-logic Control during Underwater Dynamic Positioning

    Midhin Das Thekkedan; Cheng Siong Chin; Wai Lok Woo

    2015-01-01

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

  13. Using fuzzy logic to enhance stereo matching in multiresolution images.

    Medeiros, Marcos D; Gonçalves, Luiz Marcos G; Frery, Alejandro C

    2010-01-01

    Stereo matching is an open problem in computer vision, for which local features are extracted to identify corresponding points in pairs of images. The results are heavily dependent on the initial steps. We apply image decomposition in multiresolution levels, for reducing the search space, computational time, and errors. We propose a solution to the problem of how deep (coarse) should the stereo measures start, trading between error minimization and time consumption, by starting stereo calculation at varying resolution levels, for each pixel, according to fuzzy decisions. Our heuristic enhances the overall execution time since it only employs deeper resolution levels when strictly necessary. It also reduces errors because it measures similarity between windows with enough details. We also compare our algorithm with a very fast multi-resolution approach, and one based on fuzzy logic. Our algorithm performs faster and/or better than all those approaches, becoming, thus, a good candidate for robotic vision applications. We also discuss the system architecture that efficiently implements our solution. PMID:22205859

  14. Using Fuzzy Logic to Enhance Stereo Matching in Multiresolution Images

    Marcos D. Medeiros

    2010-01-01

    Full Text Available Stereo matching is an open problem in Computer Vision, for which local features are extracted to identify corresponding points in pairs of images. The results are heavily dependent on the initial steps. We apply image decomposition in multiresolution levels, for reducing the search space, computational time, and errors. We propose a solution to the problem of how deep (coarse should the stereo measures start, trading between error minimization and time consumption, by starting stereo calculation at varying resolution levels, for each pixel, according to fuzzy decisions. Our heuristic enhances the overall execution time since it only employs deeper resolution levels when strictly necessary. It also reduces errors because it measures similarity between windows with enough details. We also compare our algorithm with a very fast multi-resolution approach, and one based on fuzzy logic. Our algorithm performs faster and/or better than all those approaches, becoming, thus, a good candidate for robotic vision applications. We also discuss the system architecture that efficiently implements our solution.

  15. A Fuzzy Logic Framework for Integrating Multiple Learned Models

    Bobi Kai Den Hartog

    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.

  16. Fuzzy adaptive synchronization of uncertain chaotic systems

    This Letter presents an adaptive approach for synchronization of Takagi-Sugeno (T-S) fuzzy chaotic systems. Since the parameters of chaotic system are assumed unknown, the adaptive law is derived to estimate the unknown parameters and its stability is guaranteed by Lyapunov stability theory. The control law to be designed consists of two parts: one part that can stabilize the synchronization error dynamics and the other part that estimates the unknown parameters. Numerical examples are given to demonstrate the validity of the proposed adaptive synchronization approach

  17. Vrednovanje lokacija za uspostavljanje mosnog mesta prelaska preko vodenih prepreka primenom fuzzy logike / Evaluating locations for river crossing using fuzzy logic

    Darko I. Božanić

    2010-01-01

    pontoon bridge location for the purpose of overcoming water obstacles. The decision making process includes a higher or lower level of indefiniteness of criteria needed for making a relevant decision. Since the fuzzy logic is very suitable for expressing indefiniteness and uncertainty, the decision making process using a fuzzy logic approach is shown in the paper. Characteristics of multi-criteria methods and selection of methods for evaluation With the development of the evaluation theory, evaluation models were being developed as well. Different objectives of evaluation and other differences in the whole procedure had an impact on the development of the majority of evaluation models adapted to different requests. The main objective of multi-criteria methods is to define the priority between particular variants or criteria in the situation with a large number of decision makers and a large number of decision making criteria in repeated periods of time. Main notions of fuzzy logic and fuzzy sets In a larger sense, the fuzzy logic is a synonym for the fuzzy sets theory which refers to the class of objects with unclear borders the membership of which is measured by certain value. It is important to realize that the essence of the fuzzy logic is different from the essence of the traditional logic system. This logic, based on clear and precisely defined rules, has its foundation in the set theory. An element can or cannot be a part of a set, which means that sets have clearly determined borders. Contrary to the conventional logic, the fuzzy logic does not define precisely the membership of an element to a set. The membership value is expressed in percentage, for example. The fuzzy logic is very close to human perception. Fuzzy system modeling for evaluation of selected locations The fuzzy logic is usually used for complex system modeling, when it is difficult to define interdependences between certain variables by other methods. The criteria for the selection of locations for

  18. Performance analysis of PM synchronous motor using fuzzy logic and self tuning fuzzy PI speed controls

    Permanent Magnet Synchronous Motors have nonlinear characteristics whose dynamics changes with time. In spite of this structure the permanent magnet synchronous motor has answered engineering problems in industry such as motion control which need high torque values. This paper obtains a nonlinear mathematical model for Permanent Magnet Synchronous Motor and realizes stimulation of the obtained model in the Matlab/Simulink program. Motor parameters are determined by an experimental set-up and they are used in the motor model. Speed control of motor model is made with Fuzzy Logic and Self Tuning logic PI controllers. Using the speed graphs obtained, rise time, overshoot, steady-state error and settling time are analyzed and controller performances are compared. (author)

  19. Preventive Maintenance Prioritization by Fuzzy Logic for Seamless Hydro Power Generation

    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.

  20. 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. PMID:26915095

  1. 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. PMID:26774211

  2. Neuro-fuzzy Logic Control of Single Phase Matrix Converter Fed Induction Heating System

    P. Umasankar

    2015-02-01

    Full Text Available This study presents a design and simulation of Neuro-Fuzzy Logic Controlled (NFLC Single Phase Matrix Converter (SPMC fed Induction Heating (IH system. Single phase matrix converter system is an AC-AC converter which eliminates the usage of reactive storage elements and its performance over varying operating frequencies can be controlled by varying the Pulse Width Modulation (PWM signal fed to the switches of single phase matrix converter. In the existing system a Fuzzy Logic Controller (FLC was designed to control the matrix converter which yielded low Total Harmonic Distortion (THD values when compared to previous systems. In this study a Neuro-Fuzzy Logic Controller was designed to control the single phase matrix converter and the results obtained prove its advantage over the existing Fuzzy Logic based control system.

  3. Smart handover based on fuzzy logic trend in IEEE802.11 mobile IPv6 networks

    Lim, Joanne Mun-Yee

    2012-01-01

    A properly designed handoff algorithm is essential in reducing the connection quality deterioration when a mobile node moves across the cell boundaries. Therefore, to improve communication quality, we identified three goals in our paper. The first goal is to minimize unnecessary handovers and increase communication quality by reducing misrepresentations of RSSI readings due to multipath and shadow effect with the use of additional parameters. The second goal is to control the handover decisions depending on the users' mobility by utilizing location factors as one of the input parameters in a fuzzy logic handover algorithm. The third goal is to minimize false handover alarms caused by sudden fluctuations of parameters by monitoring the trend of fuzzy logic outputs for a period of time before making handover decision. In this paper, we use RSSI, speed and distance as the input decision criteria of a handover trigger algorithm by means of fuzzy logic. The fuzzy logic output trend is monitored for a period of tim...

  4. Control of Single Axis Magnetic Levitation System Using Fuzzy Logic Control

    Tania Tariq Salim

    2013-12-01

    Full Text Available This paper presents a fuzzy logic controller design for the stabilization of magnetic levitation system (Maglev 's.Additionally, the investigation on Linear Quadratic Regulator Controller (LQRC also mentioned here. This paper presents the difference between the performance of fuzzy logic control (FLC and LQRC for the same linear model of magnetic levitation system .A magnetic levitation is a nonlinear unstable system and the fuzzy logic controller brings the magnetic levitation system to a stable region by keeping a magnetic ball suspended in the air. The modeling of the system is simulated using Matlab Simulink and connected to Hilink platform and the maglev model of Zeltom company. This paper presents a comparison for both LQRC and FLC to control a ball suspended on the air. The performance results of simulation shows that the fuzzy logic controller had better performance than the LQR control.

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

    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

  6. Prescribed Performance Fuzzy Adaptive Output-Feedback Control for Nonlinear Stochastic Systems

    Lili Zhang

    2014-01-01

    Full Text Available A prescribed performance fuzzy adaptive output-feedback control approach is proposed for a class of single-input and single-output nonlinear stochastic systems with unmeasured states. Fuzzy logic systems are used to identify the unknown nonlinear system, and a fuzzy state observer is designed for estimating the unmeasured states. Based on the backstepping recursive design technique and the predefined performance technique, a new fuzzy adaptive output-feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded in probability and the tracking error remains an adjustable neighborhood of the origin with the prescribed performance bounds. A simulation example is provided to show the effectiveness of the proposed approach.

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

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

    2008-01-01

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

  8. Fuzzy Logic Control of Single Phase Matrix Converter Fed Induction Heating System

    P. Umasankar; Dr.S.Senthilkumar

    2014-01-01

    This article represents the modeling and simulation of a Single Phase Matrix Converter (SPMC) fed Induction Heating (IH) system. The working principle and the control system using Fuzzy Logic Controller (FLC) are elucidated in detail. The performance of the system and their harmonic content analysis of Single Phase Matrix Converter are carried out in MATLAB/Simulink environment. Pulse Width Modulation (PWM) switching strategy by varying the duty cycle based on Fuzzy Logic Control is employed ...

  9. Multi-input Fuzzy Logic Controller for Brushless dc Motor Drives

    Y. H. Bharathi; B. R. Rekha; P. Bhaskar Bhaskar; C. S. Parvathi; Kulkarni, A.B.

    2008-01-01

    The brushless dc motors are used in various applications such as defence, industries,robotics, etc. In these applications, the motor should be precisely controlled to give the desiredperformance. The proposed controller systems consist of multi-input fuzzy (two-and three-input)logic controller (FLC) and multi-input integrated fuzzy logic controller (IFLC) for the speed controlof brushless dc servomotor drive. The input for the controllers are error e(k), change in error[first derivative of er...

  10. PERFORMANCE ENHANCEMENT OF DIRECT TORQUE CONTROL OF INDUCTION MOTOR USING FUZZY LOGIC

    P. Sweety Jose; Jovitha Jerome; S. Sathya Bama

    2011-01-01

    DTC is strategy of selecting proper stator voltage vectors to reduce torque error and flux error. DTC uses hysteresis band controller whose control action has no difference between large torque error and small one. This results in high torque ripple. In order to reduce the torque ripple and to improve the performance of DTC, DTC based on Fuzzy Logic is proposed in this paper. The simulations are carried out using MATLAB and comparison is made between conventional DTC and DTC using Fuzzy Logic...

  11. FUZZY LOGIC CONTROLLER FOR CASCADED H-BRIDGE MULTI LEVEL INVERTER

    A. CHITRA,; T. MEENAKSHI,; Asha, J.

    2011-01-01

    This paper describes the design of a rule based Fuzzy Logic Controller (FLC) for multilevel inverter. A multilevel inverter is controlled by varying the modulation index of the inverter by keepingthe DC link voltage constant. The nine level Cascaded H Bridge multilevel inverter topology is designed as the test system for the design of fuzzy logic controller after a thorough evaluation of its advantages. The conventional control methods are mainly restricted to the direct and indirect control ...

  12. Fuzzy logic for burner, solar boiler and catalytic converter; Brander, zonneboiler en katalysator vaag geregeld

    Voorter, P.H.C.

    1995-05-01

    The application of fuzzy logic in the process control of a cement furnace at a Dutch cement industry (Enci in Maastricht) proved to be successful: the production increased by 4% and the energy consumption was reduced by 3% per ton product. Fuzzy logic can also be used in smaller energy equipment. Applications in a burner of a central heating boiler, a solar water heater and a catalytic converter in a motorcycle are discussed. 5 figs., 1 tab., 2 refs.

  13. Simulation of Interleaved Boost Converter Using Closed Loop Fuzzy Logic Controller

    Karthikeyan, R; Argha Paul2; Balamurugan, P.

    2014-01-01

    Interleaved power converters can be very beneficial for high performance electrical equipment applications. Reductions in size and electromagnetic emission along with an increase in efficiency, transient response, and reliability are among the many advantages to using such converters. Fuzzy Logic is a linguistic approach which emerges to design simple, complex and embedded systems with control inputs. A fuzzy logic uses linguistic variables which states IF A AND B THEN C” this...

  14. Operational Investigation of Overhead Crane with Fuzzy Logic Anti-Swing Controller Using 3-D Simulation

    Y. N. Petrenko

    2011-01-01

    Full Text Available The purpose of a crane control system is to provide load transfer with minimum swinging. The paper presents a developed three-dimensional simulation model of a bridge crane with fuzzy logic controller designed with application of genetic algorithms. Comparative indices of oscillation while load transferring are given in the paper. The indices have been obtained at various parameters of the fuzzy logic controller. 

  15. Fuzzy Logic Closed Loop Control of 5 level MLI Driven Three phase Induction motor

    Mulukutla Venkata Subramanyam; P.V.N. Prasad; G.Poornachandra Rao

    2013-01-01

    This paper deals about fuzzy logic control of closed loop controlled five level Multi Level Inverter (MLI) driven three phase induction motor. Three phase Induction motor is most widely used drive in Industries, so needs proper control of speed. Induction motor is fed from five level multilevel inverter which is controlled by fuzzy logic. The closed loop consists of two loops. First inner loop is current loop and second outer loop is speed loop. The torque is varied at different times and cor...

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

    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.

  17. Fuzzy logic based control system for fresh water aquaculture: A MATLAB based simulation approach

    Rana Dinesh Singh

    2015-01-01

    Full Text Available Fuzzy control is regarded as the most widely used application of fuzzy logic. Fuzzy logic is an innovative technology to design solutions for multiparameter and non-linear control problems. One of the greatest advantages of fuzzy control is that it uses human experience and process information obtained from operator rather than a mathematical model for the definition of a control strategy. As a result, it often delivers solutions faster than conventional control design techniques. The proposed system is an attempt to apply fuzzy logic techniques to predict the stress factor on the fish, based on line data and rule base generated using domain expert. The proposed work includes a use of Data acquisition system, an interfacing device for on line parameter acquisition and analysis, fuzzy logic controller (FLC for inferring the stress factor. The system takes stress parameters on the fish as inputs, fuzzified by using FLC with knowledge base rules and finally provides single output. All the parameters are controlled and calibrated by the fuzzy logic toolbox and MATLAB programming.

  18. Sliding mode control of wind-induced vibrations using fuzzy sliding surface and gain adaptation

    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.

  19. Fuzzy Control Strategies in Human Operator and Sport Modeling

    Ivancevic, Tijana T; Markovic, Sasa

    2009-01-01

    The motivation behind mathematically modeling the human operator is to help explain the response characteristics of the complex dynamical system including the human manual controller. In this paper, we present two different fuzzy logic strategies for human operator and sport modeling: fixed fuzzy-logic inference control and adaptive fuzzy-logic control, including neuro-fuzzy-fractal control. As an application of the presented fuzzy strategies, we present a fuzzy-control based tennis simulator.

  20. Optimal fuzzy logic-based PID controller for load-frequency control including superconducting magnetic energy storage units

    This paper proposes a new optimal fuzzy logic-based-proportional-integral-derivative (FLPID) controller for load frequency control (LFC) including superconducting magnetic energy storage (SMES) units. Conventionally, the membership functions and control rules of fuzzy logic control are obtained by trial and error method or experiences of designers. To overcome this problem, the multiple tabu search (MTS) algorithm is applied to simultaneously tune PID gains, membership functions and control rules of FLPID controller to minimize frequency deviations of the system against load disturbances. The MTS algorithm introduces additional techniques for improvement of search process such as initialization, adaptive search, multiple searches, crossover and restarting process. Simulation results explicitly show that the performance of the optimum FLPID controller is superior to the conventional PID controller and the non-optimum FLPID controller in terms of the overshoot, settling time and robustness against variations of system parameters

  1. Fuzzy logic controller versus classical logic controller for residential hybrid solar-wind-storage energy system

    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.

  2. Maximum Power Search in Wind Turbine Based on Fuzzy Logic Control

    Evgenije Adzic

    2009-03-01

    Full Text Available This paper describes fuzzy logic control of induction generator speed in windturbine application. The aim of fuzzy controller is to establishe maximum power delivery tothe grid from available wind power. Fully-controlled wind turbine which consists ofinduction generator and back-to-back converter is under estimate. This configuration hasfull control over the electrical torque, full control of the speed, and also supports reactivepower compensation and operation under grid disturbances. Fuzzy logic control alorithmhas been aplied and validated by detailed simulation in MATLAB/Simulink. All systemcomponents have been described in detail.

  3. Design and Simulation of Fuzzy Logic controller for DSTATCOM In Power System

    Anju Gupta

    2011-10-01

    Full Text Available In this paper design of self tuned fuzzy set theory based PI controller is incorporated in typical FACTS device DSTATCOM. Its effects are tested in power systems. The modeling and the controller block diagram for DSTATCOM with detailed design of self tuned fuzzy logic controller is presented. The performance of proposed fuzzy logic DSTATCOM has been simulated for current balancing and harmonic compensation for both linear and non-linear loads. The results show the capability of proposed model in enhancing the dynamic behavior ofinterconnected systems. The simulation is carried out in MATLAB SIMULINK and the results shows the results confirm the feasibility of proposed system.

  4. Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems

    Chien-Hao Tseng

    2016-07-01

    Full Text Available 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.

  5. 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

  6. Transient Stability of A.C Generator Controlled By Using Fuzzy Logic Controller

    Srinivas Singirikonda

    2014-03-01

    Full Text Available This article is focused on the implementation of fuzzy logic controller for a.c generator; a power system is highly nonlinear system. At present, power system can be simulated and analyzed based on a mathematical model however, uncertainty still exists due to change of loads and an occurrence of fault. Recently, fuzzy theory highly flexible easily operated and revised, theory is a better choice, especially for a complicated system with many variables. Hence, this work aims to develop a controller based on fuzzy logic to simulate an automatic voltage regulator in transient stability power system analysis. By adding power system stabilizer for tuning of fuzzy logic stabilizing controller there is no need for exact knowledge of power system mathematical model. The fuzzy controller parameters settings are independent due to nonlinear changes in generator and transmission lines operating conditions. Because of that proposed fuzzy controlled power system stabilizer should perform better than the conventional controller. To overcome the drawbacks of conventional power system stabilizer (CPSS, numerous techniques have been proposed in the article. The conventional PSS's effect on the system damping is then compared with a fuzzy logic based PSS while applied to a single machine infinite bus power system.

  7. Mathematical Fuzzy Logic - What It Can Learn from Mostowski and Rasiowa

    Hájek, Petr

    2006-01-01

    Roč. 84, č. 1 (2006), s. 51-62. ISSN 0039-3215 R&D Projects: GA AV ČR IAA100300503 Institutional research plan: CEZ:AV0Z10300504 Keywords : Mostowski * Rasiowa * many-valued logic * fuzzy logic Subject RIV: BA - General Mathematics

  8. Novel Robot Manipulator Adaptive Artificial Control: Design a Novel SISO Adaptive Fuzzy Sliding Algorithm Inverse Dynamic Like Method

    Farzin Piltan

    2011-12-01

    Full Text Available Refer to the research, design a novel SISO adaptive fuzzy sliding algorithm inverse dynamic like method (NAIDLC and application to robot manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in inverse dynamic controller, fuzzy logic controller and self tuning fuzzy sliding method, the output has improved. The main objective in this research is analyses and design of the adaptive robust controller based on artificial intelligence and nonlinear control. Robot manipulator is nonlinear, time variant and a number of parameters are uncertain, so design the best controller for this plant is the main target. Although inverse dynamic controller have acceptable performance with known dynamic parameters but regarding to uncertainty, this controller\\'s output has fairly fluctuations. In order to solve this problem this research is focoused on two methodology the first one is design a fuzzy inference system as a estimate nonlinear part of main controller but this method caused to high computation load in fuzzy rule base and the second method is focused on design novel adaptive method to reduce the computation in fuzzy algorithm.

  9. Design and Development of Fuzzy Logic Controller for Liquid Flow Control

    Thae Thae Ei Aung,

    2014-09-01

    Full Text Available The purpose of this paper is to design a simulation system of fuzzy logic controller for Hydro-Electric Power Dam Control by using simulation package which is Fuzzy Logic Toolbox and Simulink in MATLAB software. By doing some modification of this paper, the design will be very useful for the system relates to liquid level control that widely use in industry nowadays. In this paper, we used the liquid level in tank , and use MATLAB to design a Fuzzy Control. The control of liquid level and flow between tanks is a basic problem in the process industries. Measuring the flow of liquids is a critical need in many industrial plants. Designers can realize lower development costs, superior features, and better end product performance by using fuzzy logic. Fuzzy Logic controller has better stability, small overshoot, and fast response. The paper presents Fuzzy Logic Controller (FLC method for safe reservoir control of dams through spillway gates and it presents FLC method for turbine valve to control the water flow through turbine for hydro power generation. Thus it shows overall effective control and operation of the mechanical equipments in a hydro electric power generation project with FLC and its usefulness. Dam control system takes information about water level, gate opening ratios, gate operation as parameters and controls spillway in case of flooding. In this design two input parameters: water level and flow rate and two output parameters: release valve control and drain valve control are used.

  10. Fuzzy Logic Based Group Maturity Rating for Software Performance Prediction

    2007-01-01

    Driven by market requirements, software services organizations have adopted various software engineering process models (such as capability maturity model (CMM), capability maturity model integration (CMMI), ISO 9001:2000, etc.) and practice of the project management concepts defined in the project management body of knowledge. While this has definitely helped organizations to bring some methods into the software development madness, there always exists a demand for comparing various groups within the organization in terms of the practice of these defined process models. Even though there exist many metrics for comparison, considering the variety of projects in terms of technology, life cycle, etc., finding a single metric that caters to this is a difficult task. This paper proposes a model for arriving at a rating on group maturity within the organization. Considering the linguistic or imprecise and uncertain nature of software measurements, fuzzy logic approach is used for the proposed model. Without the barriers like technology or life cycle difference, the proposed model helps the organization to compare different groups within it with reasonable precision.

  11. Monitoring nuclear reactor systems using neural networks and fuzzy logic

    A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such ''virtual measurements'' the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up or performance can be determined. In the methodology presented the output of a virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control valve of an experimental reactor using data obtained during a start-up. The enhanced noise tolerance of the methodology is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems. 8 refs., 11 figs., 1 tab

  12. Novel Spectrum Handoff in Cognitive Radio Networks Using Fuzzy Logic

    Nisar A. Lala

    2013-10-01

    Full Text Available Cognitive radio is a technology initiated by many research organizations and academic institutions to raise the spectrum utilization of underutilized channels in order to alleviate spectrum scarcity problem to a larger extent. Spectrum handoff is initiated due to appearance of primary user (PU on the channels occupied by the secondary user (SU at that time and location or interference to the PU exceeds the certain threshold. In this paper, we propose a novel spectrum handoff algorithm using fuzzy logic based approach that does two important functions: 1 adjusts transmission power of SU intelligently in order to avoid handoff by reducing harmful interference to PUs and 2 takes handoff decisions intelligently in the light of new parameter such as expected holding time (HT of the channel as one of its antecedent. Simulated results show impact analysis of selection of the channel in the light of HT information and the comparison with random selection algorithm demonstrates that there is considerable reduction in handoff rate of SU.

  13. Monitoring of Air Polution by Using Fuzzy Logic

    Dr. Gopal Upadhyaya,

    2010-10-01

    Full Text Available The Air Quality Index is a simple and generalized way to describe the air quality in China, Hong Kong, Malaysia and now in India. Indian Air Quality Index (IND-AQI is mainly a health related index with the descriptor words: “Good (0- 100”, “Moderate (101-200 ”, “Poor (201-300”, “Very Poor (301-400”, “Severe (401-500”. State Environment Protection Agency (SEPA is responsible for measuring the level of air pollution in China . In China the AQI is based on the level of 5 atmospheric pollutants, namely sulferdioxide(SO2, nitrogen dioxide (NO2, suspended particulates (PM10, carbon monoxide (CO, and ozone (O3 measured at the monitoring stations throughout each city (USEPA et al. 1998. An individual score is assigned to the level of each pollutant and the final AQI is the Highest of those scores. Air quality measurement are commonly reported in terms of micrograms per cubic meter (μgm/m3 or parts per million (ppm (http://en.wikipedia.org. The Conventional method used Linear Interpolation for calculating AQI . We applied a real time Fuzzy Logic System with Simulink to calculate AQI. This method gives satisfactory result and it is efficient to work under continuous working mode .

  14. Bioimpedance-based identification of malnutrition using fuzzy logic

    Protein-energy malnutrition reduces the quality of life, lengthens the time in hospital and dramatically increases mortality. Currently there is no simple and objective method available for assessing nutritional status and identifying malnutrition. The aim of this work is to develop a novel assistance system that supports the physician in the assessment of the nutritional status. Therefore, three subject groups were investigated: the first group consisted of 688 healthy subjects. Two additional groups consisted of 707 patients: 94 patients with primary diseases that are known to cause malnutrition, and 613 patients from a hospital admission screening. In all subjects bioimpedance spectroscopy measurements were performed, and the body composition was calculated. Additionally, in all patients the nutritional status was assessed by the subjective global assessment score. These data are used for the development and validation of the assistance system. The basic idea of the system is that nutritional status is reflected by body composition. Hence, features of the nutritional status, based on the body composition, are determined and compared with reference ranges, derived from healthy subjects' data. The differences are evaluated by a fuzzy logic system or a decision tree in order to identify malnourished patients. The novel assistance system allows the identification of malnourished patients, and it can be applied for screening and monitoring of the nutritional status of hospital patients

  15. Monitoring nuclear reactor systems using neural networks and fuzzy logic

    A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such virtual measurements the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up-or performance can be determined. In the methodology presented the output of virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems

  16. Fuzzy logic path planning system for collision avoidance by an autonomous rover vehicle

    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.

  17. A FUZZY-LOGIC CONTROL ALGORITHM FOR ACTIVE QUEUE MANAGEMENT IN IP NETWORKS

    2008-01-01

    Active Queue Management (AQM) is an active research area in the Internet community. Random Early Detection (RED) is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its average queue length is closely related to the load level. This paper proposes an effective fuzzy congestion control algorithm based on fuzzy logic which uses the predominance of fuzzy logic to deal with uncertain events. The main advantage of this new congestion control algorithm is that it discards the packet dropping mechanism of RED, and calculates packet loss according to a preconfigured fuzzy logic by using the queue length and the buffer usage ratio. Theoretical analysis and Network Simulator (NS) simulation results show that the proposed algorithm achieves more throughput and more stable queue length than traditional schemes. It really improves a router's ability in network congestion control in IP network.

  18. FUZZY LOGIC CONTROLLER FOR CASCADED H-BRIDGE MULTI LEVEL INVERTER

    A. CHITRA,

    2011-02-01

    Full Text Available This paper describes the design of a rule based Fuzzy Logic Controller (FLC for multilevel inverter. A multilevel inverter is controlled by varying the modulation index of the inverter by keepingthe DC link voltage constant. The nine level Cascaded H Bridge multilevel inverter topology is designed as the test system for the design of fuzzy logic controller after a thorough evaluation of its advantages. The conventional control methods are mainly restricted to the direct and indirect control of the inverter. The proposed fuzzy logic controller shows improved functionalities in the simulative experimental studies. The Fuzzy Associative Memory (FAM table is derived after a thorough research of the characteristics and compared with the conventional controller for harmonic disturbance, voltage profile and other system parameters.

  19. Applicability of the Fuzzy Logic Controllers for the Steam Generator Level Control

    The fuzzy logic controllers are found to provide real advantages for some control problems. They are actually being used in consumer products made in Japan or in Korea. For the application of the fuzzy logic controllers to the nuclear power plants, there have been several preliminary studies most of which are related to the steam generator level control where the enhancement of the controller capability is mostly needed. In this paper, we examine the possibility of their applications to the nuclear power plants, We start with a brief discussion on how to build a fuzzy algorithm and how to generate an equivalent analytic form of the algorithm, and then propose a method using the artificial neural network technique for the tuning of the controller. Finally, we examine the major licensing problems related to the application of the fuzzy logic controllers to the nuclear power plants

  20. Fuzzy logic controller for the electric motor driving the astronomical telescope

    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.

  1. Adaptive Fuzzy Sliding Mode Tracking Control of Uncertain Underactuated Nonlinear Systems: A Comparative Study

    Faten Baklouti

    2016-01-01

    Full Text Available The trajectory tracking of underactuated nonlinear system with two degrees of freedom is tackled by an adaptive fuzzy hierarchical sliding mode controller. The proposed control law solves the problem of coupling using a hierarchical structure of the sliding surfaces and chattering by adopting different reaching laws. The unknown system functions are approximated by fuzzy logic systems and free parameters can be updated online by adaptive laws based on Lyapunov theory. Two comparative studies are made in this paper. The first comparison is between three different expressions of reaching laws to compare their abilities to reduce the chattering phenomenon. The second comparison is made between the proposed adaptive fuzzy hierarchical sliding mode controller and two other control laws which keep the coupling in the underactuated system. The tracking performances of each control law are evaluated. Simulation examples including different amplitudes of external disturbances are made.

  2. Improvement of Torque Response and Reduction of Error Speed in Direct Torque Control of Induction Motor by Fuzzy Logic

    HamidReza Fakharizadeh

    2009-01-01

    In this paper, the direct torque control (DTC) technique is used for the speed control of induction motors, and then fuzzy logic is used for designing the speed controller, the improvement of torque response and the reduction of the speed error. The DTC method is utilized due to its quick torque response and robustness against sudden load variations. Also, by applying fuzzy logic unpredicted problems can be solved. The fuzzy logic also can improve the work of the speed control in induction mo...

  3. Fuzzy logic and neural networks in artificial intelligence and pattern recognition

    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.

  4. Fuzzy logic type 1 and type 2 based on LabVIEW FPGA

    Ponce-Cruz, Pedro; MacCleery, Brian

    2016-01-01

    This book is a comprehensive introduction to LabVIEW FPGA™, a package allowing the programming of intelligent digital controllers in field programmable gate arrays (FPGAs) using graphical code. It shows how both potential difficulties with understanding and programming in VHDL and the consequent difficulty and slowness of implementation can be sidestepped. The text includes a clear theoretical explanation of fuzzy logic (type 1 and type 2) with case studies that implement the theory and systematically demonstrate the implementation process. It goes on to describe basic and advanced levels of programming LabVIEW FPGA and show how implementation of fuzzy-logic control in FPGAs improves system responses. A complete toolkit for implementing fuzzy controllers in LabVIEW FPGA has been developed with the book so that readers can generate new fuzzy controllers and deploy them immediately. Problems and their solutions allow readers to practice the techniques and to absorb the theoretical ideas as they arise. Fuzzy L...

  5. Improved fuzzy logic controller using genetic algorithms and its application to vibration suppression of flexible structures

    This paper presents the application of an improved fuzzy controller to vibration suppression of a cantilever beam structure. A Genetic Algorithm (G A) optimizer, which emulates natural biological evolutionary theories, offers a technology that supports optimization of the parameters of fuzzy logic and other parameterized non-linear controllers. This paper shows how G As can effectively and efficiently optimize the performance of fuzzy net controllers. Some results are presented which show the ability of the improved fuzzy controller to highly improve the vibration cancellation performance of the flexible beam. (author). 25 refs. 3 tab., 10 figs

  6. Design and Construction of Intelligent Traffic Light Control System Using Fuzzy Logic

    Lin, Htin; Aye, Khin Muyar; Tun, Hla Myo; Theingi, Naing, Zaw Min

    2008-10-01

    Vehicular travel is increasing throughout the world, particularly in large urban areas. Therefore the need arises for simulation and optimizing traffic control algorithms to better accommodate this increasing demand. This paper presents a microcontroller simulation of intelligent traffic light controller using fuzzy logic that is used to change the traffic signal cycles adaptively at a two-way intersection. This paper is an attempt to design an intelligent traffic light control systems using microcontrollers such as PIC 16F84A and PIC 16F877A. And then traffic signal can be controlled depending upon the densities of cars behind green and red lights of the two-way intersection by using sensors and detectors circuits.

  7. Students Classification With Adaptive Neuro Fuzzy

    Mohammad Saber Iraji

    2012-07-01

    Full Text Available Identifying exceptional students for scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. In this article, we have tried to design an intelligent system which can separate and classify student according to learning factor and performance. a system is proposed through Lvq networks methods, anfis method to separate these student on learning factor . In our proposed system, adaptive fuzzy neural network(anfis has less error and can be used as an effective alternative system for classifying students

  8. Adaptive Fuzzy Backstepping Control against Actuator Faults

    Fujiang Jin

    2011-01-01

    Full Text Available In this study, the problem of Fault-Tolerant Control (FTC for a class of uncertain nonlinear systems is studied. A novel FTC scheme is proposed to deal with both lock-in-place and loss of effectiveness faults of actuators. By employing fuzzy approximation and on-line adaptive updating, the proposed control scheme can tolerate the faults without detection and diagnosis mechanism. It is proved in theory that the FTC scheme can guarantee the closed-loop stability and desired output tracking performance in spite of all kinds of the faults and external disturbances. A simulation example is also included to show the effectiveness of the scheme.

  9. Three-phase three-level grid interactive inverter with fuzzy logic based maximum power point tracking controller

    Highlights: ► We propose a three phase three-level NPC inverter for grid interactive PV systems. ► We design fuzzy logic based maximum power point tracking algorithm. ► The proposed algorithm is robust with respect to parameter variations of PV system. ► THD level of the inverter current is in the limits of international standards. ► Total system efficiency is measured as 93.12%. - Abstract: In this study, three-phase single stage grid interactive inverter with maximum power point tracking capability is proposed. The proposed system consists of three-level neutral point clamped inverter, LCL output filter, line frequency transformer, PI current regulator and fuzzy logic based maximum power point tracking algorithm. Rate of change of photovoltaic power and voltage are defined as input variables, and the change in reference current is defined as output variable for the fuzzy logic controller. The proposed maximum power point tracking algorithm is robust with respect to parameter variations of photovoltaic system with adaptive feature of fuzzy logic controller. Maximum power point tracking algorithm determines the inverter current reference depending on the system conditions such as irradiation level and temperature, and PI regulator shapes the inverter output current. Two capacitors’ voltages of neutral point clamped inverter are also balanced. Simulation and experimental results show that the proposed inverter system has fast transient response and can track the maximum power point of PV system even if atmospheric condition changes rapidly. Also, the inverter output current is in sinusoidal waveform and in phase with line frequency and phase. In addition, total harmonic distortion level of the inverter output current is in the limits of international standards (<5%) and efficiencies of maximum power point tracking algorithm and total system are measured as 98.78% and 93.12%, respectively

  10. Fuzzy logic control of water level in advanced boiling water reactor

    The feedwater control system in the Advanced Boiling Water Reactor (ABWR) is more challenging to design compared to other control systems in the plant, due to the possible change in level from void collapses and swells during transient events. A basic fuzzy logic controller is developed using a simplified ABWR mathematical model to demonstrate and compare the performance of this controller with a simplified conventional controller. To reduce the design effort, methods are developed to automatically tune the scaling factors and control rules. As a first step in developing the fuzzy controller, a fuzzy controller with a limited number of rules is developed to respond to normal plant transients such as setpoint changes of plant parameters and load demand changes. Various simulations for setpoint and load demand changes of plant performances were conducted to evaluate the modeled fuzzy logic design against the simplified ABWR model control system. The simulation results show that the performance of the fuzzy logic controller is comparable to that of the Proportional-Integral (PI) controller, However, the fuzzy logic controller produced shorter settling time for step setpoint changes compared to the simplified conventional controller

  11. 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. PMID:18255897

  12. Poisson's ratio prediction through dual stimulated fuzzy logic by ACE and GA-PS

    Bagheripour, Parisa; Asoodeh, Mojtaba

    2014-08-01

    Poisson's ratio is one of the most important rock mechanical parameters having significance in both planning and post analysis of wellbore operations. Laboratory measurement of this parameter covers a broad range of costs, including sidewall sampling, preservation, and laboratory tests. This study proposes an improved strategy, called dual stimulated fuzzy logic by ACE and GA-PS for determining Poisson's ratio from conventional well log data in a rapid, precise, and cost-effective way. Firstly, conventional well log data are transformed to a higher correlated data space with Poisson's ratio through the use of alternative condition expectation (ACE) algorithm. This step simplifies the convoluted space of the problem and makes it easier to solve for fuzzy logic. Subsequently, transformed conventional well log data are fed to fuzzy logic model. To ensure that optimal fuzzy model is constructed, a hybrid genetic algorithm-pattern search (GA-PS) technique is employed for extracting fuzzy clusters (or rules). This step sets fuzzy logic to its optimal performance. The propounded strategy was successfully applied to data from carbonate reservoir rocks of an Iranian Oil Field. A comparison between present model and previous models showed superiority of current study.

  13. Fuzzy-logic-based resource allocation for isolated and multiple platforms

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

    2000-08-01

    Modern naval battle forces generally include many different platforms each with its own sensors, radar, ESM, and communications. The sharing of information measured by local sensors via communication links across the battle group should allow for optimal or near optimal decision. The survival of the battle group or members of the group depends on the automatic real-time allocation of various resources. A fuzzy logic algorithm has been developed that automatically allocates electronic attack resources in real- time. The particular approach to fuzzy logic that is used is the fuzzy decision tree, a generalization of the standard artificial intelligence technique of decision trees. The controller must be able to make decisions based on rules provided by experts. The fuzzy logic approach allows the direct incorporation of expertise forming a fuzzy linguistic description, i.e. a formal representation of the system in terms of fuzzy if-then rules. Genetic algorithm based optimization is conducted to determine the form of the membership functions for the fuzzy root concepts. The isolated platform and multi platform resource manager models are discussed as well as the underlying multi-platform communication model. The resource manager is shown to exhibit excellent performance under many demanding scenarios.

  14. Fuzzy Adaptive Model Following Speed Control for Vector Controlled Permanent Magnet Synchronous Motor

    Baghdad BELABES

    2008-12-01

    Full Text Available In this paper a hybrid controller combining a linear model following controller (LMFC and fuzzy logic control (FLC for speed vector controlled permanent magnet synchronous motor (PMSM is described on this study. The FLC is introduced at the adaptive mechanism level. First, an LMFC system is designed to allow the plant states to be controlled to follow the states produced by a reference model. In the nominal conditions, the model following is perfect and the adaptive mechanism based on the fuzzy logic is idle. Secondly, when parameter variations or external disturbances occur, an augmented signal will be generated by FLC mechanism to preserve the desired model following control performance. The effectiveness and robustness of the proposed controller is demonstrated by some simulation results.

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

    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.

  16. Design and Implementation of Takagi-Sugeno Fuzzy Logic Controller for Shunt Compensator

    Singh, Alka; Badoni, Manoj

    2015-08-01

    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.

  17. A fuzzy logic approach to modeling the underground economy in Taiwan

    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.

  18. A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

    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.

  19. Traffic Signals Control with Adaptive Fuzzy Controller in Urban Road Network

    LI Yan; FAN Xiao-ping

    2008-01-01

    An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network.The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level.The control level decides the signal tunings in an intersection with a fuzzy logic controller.The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one.Consequently the system performances are improved.A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections.So the AFC combined with the WCC can be applied in a road network for signal timings.Simulations of the AFC on a real traffic scenario have been conducted.Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.

  20. The development of fuzzy logic preventive maintenance planning applied in a wire and cable factory

    To days the fuzzy logic approach has been widely applied in the various production plants. The basic objective of this article is to demonstrate the applicability of fuzzy logic concept in the process of Preventive Maintenance and repair planning situation in a Wire and Cable factory in Iran. The process of a continuous production line has a very high sensitivity to a sudden breakdown or any emergency stoppage and caused a very considerable lost production. For this reason, with the help of fuzzy logic concept, the Preventive Maintenance planning has been developed to be implemented in a Wire and Cable factory. The results of the implemented Preventive Maintenance program shows a tremendous cost savings in the mentioned factory

  1. Wavelet zero crossings and paraconsistent fuzzy logic in the diagnostic of rolling bearings

    A new defect characteristic extraction method for rolling bearings vibration signals based on wavelet transform is presented. A more robust automated diagnostic system for defects in bearings based on paraconsistent fuzzy logic is also presented which deals with inconsistent and ambiguous information. There is a need for the optimization of diagnosis systems in order to increase precision and to reduce human errors. Automatic diagnosis systems should be robust to a point where it must operate with a diversified source of information allowing for analysis of different equipment and existing defects. The paraconsistent fuzzy logic is applied in the present work. This technique is a flexible tool which allows the modeling of uncertain and ambiguous data frequently found in real situations. Experimental data were used to test the methodology. The results obtained by using wavelet zero crossings for characteristic extraction and Paraconsistent fuzzy logic for defect classification were conclusive showing that the system is capable to identify and to classify defects in bearings. (author)

  2. Development and application of fuzzy logic control system for nuclear power plant

    This paper describes the development and successful actual application of fuzzy logic control system (FLCS) to feed-water control of the Fugen Nuclear Power Station. FLCS uses fuzzy logic to adjust the steam drum water level to a set point by means of controlling feed-water flow rate to the steam drum. Although control performance of some fuzzy logic control systems in a nuclear power plant are analytically evaluated, there has been only a few examples of the application to an actual plant. This is mainly because that it requires long time for careful tuning of parameters (membership functions, etc.) as compared with the case in conventional control system. In order to reduce such tuning time in an actual plant, we used weight-gains instead of membership functions as the tuning parameters. From the operational data of the actual plant, we confirmed the further improvement of controllability by FLCS. (orig.)

  3. A real time fuzzy logic power management strategy for a fuel cell vehicle

    Highlights: • We present a real time fuzzy logic power management strategy. • This strategy is applied to hybrid electric vehicle dynamic model. • Three configurations evaluated during a drive cycle. • The hydrogen consumption is analysed for the three configurations. - Abstract: This paper presents real time fuzzy logic controller (FLC) approach used to design a power management strategy for a hybrid electric vehicle and to protect the battery from overcharging during the repetitive braking energy accumulation. The fuel cell (FC) and battery (B)/supercapacitor (SC) are the primary and secondary power sources, respectively. This paper analyzes and evaluates the performance of the three configurations, FC/B, FC/SC and FC/B/SC during real time driving conditions and unknown driving cycle. The MATLAB/Simulink and SimPowerSystems software packages are used to model the electrical and mechanical elements of hybrid vehicles and implement a fuzzy logic strategy

  4. Design of a fuzzy logic based controller for neutron power regulation

    This work presents a fuzzy logic controller design for neutron power control, from its source to its full power level, applied to a nuclear reactor model. First, we present the basic definitions on fuzzy sets as generalized definitions of the crisp (non fuzzy) set theory. Likewise, we define the basic operations on fuzzy sets (complement, union, and intersection), and the operations on fuzzy relations such as projection and cylindrical extension operations. Furthermore, some concepts of the fuzzy control theory, such as the main modules of the typical fuzzy controller structure and its internal variables, are defined. After the knowledge base is obtained by simulation of the reactor behavior, where the controlled system is modeled by a simple nonlinear reactor model, this model is used to infer a set of fuzzy rules for the reactor response to different insertions of reactivity. The reduction of the response time, using fuzzy rule based controllers on this reactor, is possible by adjusting the output membership functions, by selecting fuzzy rule sets, or by increasing the number of crisp inputs to the fuzzy controller. System characteristics, such as number of rules, response times, and safety parameter values, were considered in the evaluation of each controller merits. Different fuzzy controllers are designed to attain the desired power level, to maintain a constant level for long periods of time, and to keep the reactor away from a shutdown condition. The basic differences among the controllers are the number of crisp inputs and the novel implementation of a crisp power level-based selection of different sets of output membership functions. Simulation results highlight, mainly: (1) A decrease of the response variations at low power level, and (2) a decrease in the time required to attain the desired neutron power. Finally, we present a comparative study of different fuzzy control algorithms applied to a nuclear model. (Author)

  5. Employees’ Satisfaction Assessment Based on Fuzzy Logic: A case study of Bandar Abbas Oil Refining Company

    Khazaki, Hamid reza

    2013-01-01

    Employees satisfaction is a pre-condition for increasing productivity and attention to it is a success factor for excellent organizations. This paper presents a new approach for employees satisfaction assessment (ESA) based on fuzzy logic. In the process of ESA there is an element of vagueness or fuzziness associated with inputs that they are language terms. Also, it can be helpful for management having a model to prioritize dissatisfaction key factors according to satisfaction level and weig...

  6. Towards a Semantic Portal for Oncology using a Description Logic with Fuzzy Concrete Domains

    D'Aquin, Mathieu; Lieber, Jean; Napoli, Amedeo

    2006-01-01

    This paper presents three systems that are fully implemented and a proposal for a fourth one. KASIMIR is a knowledge based-system using an ad hoc formalism similar to a simple description logic with concrete domains which is used for representing decision protocols in oncology. FUZZY-KASIMIR is an extension of KASIMIR with fuzzy concrete domains taking into account discontinuities in the decision that are due to numerical thresholds. Another extension of KASIMIR has led to embed it into a sem...

  7. Semiactive Self-Tuning Fuzzy Logic Control of Full Vehicle Model with MR Damper

    Mahmut Paksoy; Rahmi Guclu; Saban Cetin

    2014-01-01

    Intelligent controllers are studied for vibration reduction of a vehicle consisting in a semiactive suspension system with a magnetorheological (MR) damper. The vehicle is modeled with seven degrees of freedom as a full vehicle model. The semiactive suspension system consists of a linear spring and an MR damper. MR damper is modeled using Bouc-Wen hysteresis phenomenon and applied to a full vehicle model. Fuzzy Logic based controllers are designed to determine the MR damper voltage. Fuzzy Log...

  8. Simulation Of Speed Control Of Brushless Dc Motor, With Fuzzy Logic Controller

    C.Sheeba Joice; P.Nivedhitha

    2014-01-01

    Abstract— The electronically commuted Brushless DC motors are widely used in many industrial applications which increase the need for design of efficient control strategy for these noiseless motors. This paper deals with the efficient speed control mechanisms for these drives using meaningful fuzzy sets and rules. The fuzzy logic controller is developed using a MATLAB/ Simulink tool. The paper deals with the possibility of designing a control strategy, to achieve accurate speed control with t...

  9. Traffic Forecasting Model Based on Takagi-Sugeno Fuzzy Logical System

    WANG Wei-gong; LI Zheng; CHENG Mei-ling

    2005-01-01

    The local multiple regression fuzzy(LMRF)model based on Takagi-Sugeno fuzzy logical system and its application in traffic forecasting is proposed. Besides its prediction accuracy is testified and the model is proved much better than conventional forecasting methods. According to the regional traffic system, the model perfectly states the complex non-linear relation of the traffic and the local social economy. The model also efficiently deals with the system lack of enough data.

  10. Towards a Logical Calculus for Fuzzy Mathematics I, II

    Běhounek, Libor; Cintula, Petr

    Linz : Johannes Kepler Universität, 2005. s. 1-4. [FLLL/SCCH Master and PhD Seminar. 00.02.2005-00.02.2005, Hagenberg] Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy mathematics * fuzzy class theory * notation * proof Subject RIV: BA - General Mathematics

  11. A Reverse Style of Logic-Based Fuzzy Topology

    Běhounek, Libor

    Ostrava : Universitas Ostraviensis, 2008. s. 16-17. [The Czech-Latvian Seminar on Advanced Methods in Soft Computing /1./. 19.11.2008-20.11.2008, Trojanovice] Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy topology * reverse mathematics * fuzzy mathematics Subject RIV: BA - General Mathematics

  12. Fuzzy Logic: Toward Measuring Gottfredson's Concept of Occupational Social Space.

    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…

  13. A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables

    Jaw-Kuen Shiau

    2015-04-01

    Full Text Available Maximum power point tracking (MPPT is one of the key functions of the solar power management system in solar energy deployment. This paper investigates the design of fuzzy-logic-based solar power MPPT algorithms using different fuzzy input variables. Six fuzzy MPPT algorithms, based on different input variables, were considered in this study, namely (i slope (of solar power-versus-solar voltage and changes of the slope; (ii slope and variation of the power; (iii variation of power and variation of voltage; (iv variation of power and variation of current; (v sum of conductance and increment of the conductance; and (vi sum of angles of arctangent of the conductance and arctangent of increment of the conductance. Algorithms (i–(iv have two input variables each while algorithms (v and (vi use a single input variable. The fuzzy logic MPPT function is deployed using a buck-boost power converter. This paper presents the details of the determinations, considerations of the fuzzy rules, as well as advantages and disadvantages of each MPPT algorithm based upon photovoltaic (PV cell properties. The range of the input variable of Algorithm (vi is finite and the maximum power point condition is well defined in steady condition and, therefore, it can be used for multipurpose controller design. Computer simulations are conducted to verify the design.

  14. Planning by Case-Based Reasoning Based on Fuzzy Logic

    Atmani Baghdad

    2013-05-01

    Full Text Available The treatment of complex systems often requires the manipulation of vague, imprecise and uncertain information. Indeed, the human being is c ompetent in handling of such systems in a natural way. Instead of thinking in mathematical te rms, humans describes the behavior of the system by language proposals. In order to represent this type of information, Zadeh proposed to model the mechanism of human thought by approximate reasoning based on linguistic variables. He introduced the theory of fuzzy sets i n 1965, which provides an interface between language and digital worlds. In this paper, we prop ose a Boolean modeling of the fuzzy reasoning that we baptized Fuzzy-BML and uses the c haracteristics of induction graph classification. Fuzzy-BML is the process by which t he retrieval phase of a CBR is modelled not in the conventional form of mathematical equations, but in the form of a database with membership functions of fuzzy rules.

  15. Design and Implementation of Fuzzy Logic Controlled Uninterruptible Power Supply Integrating Renewable Solar Energy

    Angelo A. Beltran Jr.

    2014-03-01

    Full Text Available —The control and operation of electronic systems relies and depends on the availability of the power supply. Rechargeable batteries have been more pervasively used as the energy storage and power source for various electrical and electronic systems and devices, such as communication systems, electronic devices, renewable power systems, electric vehicles, etc. However, the rechargeable batteries are subjected to the availability of the external power source when it is drained out. Because of the concern of battery life, environmental pollution and a possible energy crisis, the renewable solar energy has received an increasing attention in recent years. A fuzzy logic control based grid tied uninterruptible power supply integrating renewable solar energy can be used for electrical and electronic systems to produce power generation. This paper presents the design and implementation of fuzzy logic control based grid tied uninterruptible power supply integrating the renewable solar power energy system. The uninterruptible power supply (UPS system is characterized by the rechargeable battery that is connected with the Photovoltaic Panel through the DC/DC converter, the utility AC through the AC/DC converter and the load is connected through the DC/AC converter. The whole operation is controlled by the fuzzy logic algorithm. A complete hardware prototype system model of the fuzzy logic control based on the grid tied uninterruptible power supply integrating with the renewable solar energy is designed and implemented. The operation and effectiveness of the proposed system is then demonstrated by the actual and real time implementation of the fuzzy logic control grid tied operation uninterruptible power supply integrating renewable solar energy connected to the rechargeable battery bank and a PIC microcontroller platform for fuzzy logic control and operation

  16. Robust Fault Detection Using Robust Z1 Estimation and Fuzzy Logic

    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.

  17. Detection of Stator Winding Fault in Induction Motor Using Fuzzy Logic with Optimal Rules

    Hamid Fekri Azgomi

    2013-04-01

    Full Text Available Induction motors are critical components in many industrial processes. Therefore, swift, precise and reliable monitoring and fault detection systems are required to prevent any further damages. The online monitoring of induction motors has been becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose traction motor faults. This paper presents a simple method for the detection of stator winding faults (which make up 38% of induction motor failures based on monitoring the line/terminal current amplitudes. In this method, fuzzy logic is used to make decisions about the stator motor condition. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the fuzzy inference. Simulation results are presented to verify the accuracy of motor’s fault detection and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis.

  18. FUZZY LOGIC CONTROLLER AS MODELING TOOL FOR THE BURNING PROCESS OF A CEMENT PRODUCTION PLANT

    P.B. Osofisan

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: A comprehensive optimisation of the cement production process presents a problem since the input variables as well as the output variables are non-linear, interdependent and contain uncertainties. To arrive at a solution, a Fuzzy Logic controller has been designed to achieve a well-defined relationship between the main and vital variables through the instrumentality of a Fuzzy Model. The Fuzzy Logic controller has been simulated on a digital computer using MATLAB 5.0 Fuzzy Logic Tool Box, using data from a local cement production plant.

    AFRIKAANSE OPSOMMING: Die omvattende optimisering van 'n proses wat sement vervaardig, word beskryf deur nie-linieêre inset- en uitsetveranderlikes wat onderling afhanklik is, en ook van onsekere aard is. Om 'n optimum oplossing te verkry, word 'n Wasigheidsmodel gebruik. Die model word getoets deur gebruik te maak van die MATLAB 5.0 Fuzzy Logic Tool Box en data vanaf 'n lokale sementvervaardigingsaanleg.

  19. 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.

  20. Fuzzy logic and information fusion to commemorate the 70th birthday of Professor Gaspar Mayor

    Sastre, Joan

    2016-01-01

    This book offers a timely report on key theories and applications of soft-computing. Written in honour of Professor Gaspar Mayor on his 70th birthday, it primarily focuses on areas related to his research, including fuzzy binary operators, aggregation functions, multi-distances, and fuzzy consensus/decision models. It also discusses a number of interesting applications such as the implementation of fuzzy mathematical morphology based on Mayor-Torrens t-norms. Importantly, the different chapters, authored by leading experts, present novel results and offer new perspectives on different aspects of Mayor’s research. The book also includes an overview of evolutionary fuzzy systems, a topic that is not one of Mayor’s main areas of interest, and a final chapter written by the Spanish pioneer in fuzzy logic, Professor E. Trillas. Computer and decision scientists, knowledge engineers and mathematicians alike will find here an authoritative overview of key soft-computing concepts and techniques.