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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. 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.

  12. 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

  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. 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.

  4. 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.

  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. 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.

  10. 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.

  11. 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

  12. 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

  13. 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

  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. 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.

  17. 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

  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. 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.

  10. 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.

  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. 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.)

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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.

  1. 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

  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 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

  10. 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.

  11. 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...

  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. 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

  19. 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)

  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. 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....

  6. 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

  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 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...

  15. 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. 

  16. 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.

  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. 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...

  12. 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...

  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. 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.``

  3. 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.

  4. 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.

  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. 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...

  14. 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

  15. 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.)

  16. 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...

  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. 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

  20. 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)

  1. 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.

  2. 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.

  3. 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...

  4. 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.

  5. 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.

  6. 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

  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. 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.

  9. 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 ...

  10. 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 $[0,1]$.

  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. 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.

  13. 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.

  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. 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 ...

  1. 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

  2. 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...

  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. 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)

  6. 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.

  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. 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

  9. 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...

  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 ...