Adaptive Background subtraction in Dynamic Environments Using Fuzzy Logic
Directory of Open Access Journals (Sweden)
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.
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
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...
Particle Swarm Optimization Based Adaptive Strategy for Tuning of Fuzzy Logic Controller
Directory of Open Access Journals (Sweden)
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.
Uncovering transcriptional interactions via an adaptive fuzzy logic approach
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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
Particle Swarm Optimization Based Adaptive Strategy for Tuning of Fuzzy Logic Controller
Directory of Open Access Journals (Sweden)
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.
Nguyen, Hung T
2005-01-01
THE CONCEPT OF FUZZINESS Examples Mathematical modeling Some operations on fuzzy sets Fuzziness as uncertainty Exercises SOME ALGEBRA OF FUZZY SETS Boolean algebras and lattices Equivalence relations and partitions Composing mappings Isomorphisms and homomorphisms Alpha-cuts Images of alpha-level sets Exercises FUZZY QUANTITIES Fuzzy quantities Fuzzy numbers Fuzzy intervals Exercises LOGICAL ASPECTS OF FUZZY SETS Classical two-valued logic A three-valued logic Fuzzy logic Fuzzy and Lukasiewi
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.
Fuzzy branching temporal logic.
Moon, Seong-ick; Lee, Kwang H; Lee, Doheon
2004-04-01
Intelligent systems require a systematic way to represent and handle temporal information containing uncertainty. In particular, a logical framework is needed that can represent uncertain temporal information and its relationships with logical formulae. Fuzzy linear temporal logic (FLTL), a generalization of propositional linear temporal logic (PLTL) with fuzzy temporal events and fuzzy temporal states defined on a linear time model, was previously proposed for this purpose. However, many systems are best represented by branching time models in which each state can have more than one possible future path. In this paper, fuzzy branching temporal logic (FBTL) is proposed to address this problem. FBTL adopts and generalizes concurrent tree logic (CTL*), which is a classical branching temporal logic. The temporal model of FBTL is capable of representing fuzzy temporal events and fuzzy temporal states, and the order relation among them is represented as a directed graph. The utility of FBTL is demonstrated using a fuzzy job shop scheduling problem as an example. PMID:15376850
Howard, Ayanna
2005-01-01
The Fuzzy Logic Engine is a software package that enables users to embed fuzzy-logic modules into their application programs. Fuzzy logic is useful as a means of formulating human expert knowledge and translating it into software to solve problems. Fuzzy logic provides flexibility for modeling relationships between input and output information and is distinguished by its robustness with respect to noise and variations in system parameters. In addition, linguistic fuzzy sets and conditional statements allow systems to make decisions based on imprecise and incomplete information. The user of the Fuzzy Logic Engine need not be an expert in fuzzy logic: it suffices to have a basic understanding of how linguistic rules can be applied to the user's problem. The Fuzzy Logic Engine is divided into two modules: (1) a graphical-interface software tool for creating linguistic fuzzy sets and conditional statements and (2) a fuzzy-logic software library for embedding fuzzy processing capability into current application programs. The graphical- interface tool was developed using the Tcl/Tk programming language. The fuzzy-logic software library was written in the C programming language.
Adaptive Fuzzy Logic Control of 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...
Adaptive Fuzzy Logic Control of Wind Turbine Emulator
Directory of Open Access Journals (Sweden)
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.
DEFF Research Database (Denmark)
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...
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...
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...
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.
Malhas, Othman Qasim
1993-10-01
The concept of “abacus logic” has recently been developed by the author (Malhas, n.d.). In this paper the relation of abacus logic to the concept of fuzziness is explored. It is shown that if a certain “regularity” condition is met, concepts from fuzzy set theory arise naturally within abacus logics. In particular it is shown that every abacus logic then has a “pre-Zadeh orthocomplementation”. It is also shown that it is then possible to associate a fuzzy set with every proposition of abacus logic and that the collection of all such sets satisfies natural conditions expected in systems of fuzzy logic. Finally, the relevance to quantum mechanics is discussed.
Fuzzy logic of Aristotelian forms
Energy Technology Data Exchange (ETDEWEB)
Perlovsky, L.I. [Nichols Research Corp., Lexington, MA (United States)
1996-12-31
Model-based approaches to pattern recognition and machine vision have been proposed to overcome the exorbitant training requirements of earlier computational paradigms. However, uncertainties in data were found to lead to a combinatorial explosion of the computational complexity. This issue is related here to the roles of a priori knowledge vs. adaptive learning. What is the a-priori knowledge representation that supports learning? I introduce Modeling Field Theory (MFT), a model-based neural network whose adaptive learning is based on a priori models. These models combine deterministic, fuzzy, and statistical aspects to account for a priori knowledge, its fuzzy nature, and data uncertainties. In the process of learning, a priori fuzzy concepts converge to crisp or probabilistic concepts. The MFT is a convergent dynamical system of only linear computational complexity. Fuzzy logic turns out to be essential for reducing the combinatorial complexity to linear one. I will discuss the relationship of the new computational paradigm to two theories due to Aristotle: theory of Forms and logic. While theory of Forms argued that the mind cannot be based on ready-made a priori concepts, Aristotelian logic operated with just such concepts. I discuss an interpretation of MFT suggesting that its fuzzy logic, combining a-priority and adaptivity, implements Aristotelian theory of Forms (theory of mind). Thus, 2300 years after Aristotle, a logic is developed suitable for his theory of mind.
Directory of Open Access Journals (Sweden)
Zoltan Erdei
2011-12-01
Full Text Available In this paper the authors present the usefulness of fuzzy logic in controlling engineering processes or applications. Although fuzzy logic does not represent a novelty for the scientific and engineering field, it enjoys a great appreciation from those involved in the two domains. The fact that fuzzy logic uses sentences kindred with the natural language make it easier to comprehend that a complex mathematical model required by the classic control theory. In MatLab software there are dedicated toolboxes to this subject that make the design of a fuzzy controller a facile one. In the paper design methods of a fuzzy controller are being presented both in Simulink and MatLab.
Dialectic operator fuzzy logic
Institute of Scientific and Technical Information of China (English)
程晓春; 姜云飞; 刘叙华
1996-01-01
Dialectic operator fuzzy logic (DOFL) is presented which is relevant,paraconsistent and nonmonotonic.DOFL can vividly describe the belief revision in the cognitive process and can infer reasonably well while the knowledge is inconsistent,imprecise or incomplete.
Fuzzy Logic Control of Adaptive ARQ for Video Distribution over a Bluetooth Wireless Link
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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.
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
Abdul Kareem
2012-08-01
Full Text Available This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for thecontrol of dynamic uncertain systems. The proposed controller combines the advantages of Second orderSliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability androbustness of the system with the proposed controller are guaranteed. In addition, the proposed controlleris well suited for simple design and implementation. The effectiveness of the proposed controller over thefirst order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based simulations performed on aDC-DC Buck converter. Based on this comparison, the proposed controller is shown to obtain the desiredtransient response without causing chattering and error under steady-state conditions. The proposedcontroller is able to give robust performance in terms of rejection to input voltage variations and loadvariations
Fuzzy-Logic Adaptive Queuing for a Heuristic TCP Performance in Mobile Wireless Networks
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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.
Fuzzy logic based Adaptive Modulation Using Non Data Aided SNR Estimation for OFDM system
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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 .
Maximum Power Point Tracking Using Adaptive Fuzzy Logic control for Photovoltaic System
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Anass Ait Laachir
2015-01-01
Full Text Available This work presents an intelligent approach to the improvement and optimization of control performance of a photovoltaic system with maximum power point tracking based on fuzzy logic control. This control was compared with the conventional control based on Perturb &Observe algorithm. The results obtained in Matlab/Simulink under different conditions show a marked improvement in the performance of fuzzy control MPPT of the PV system.
Universal Approximation of Mamdani Fuzzy Controllers and Fuzzy Logical Controllers
Yuan, Bo; Klir, George J.
1997-01-01
In this paper, we first distinguish two types of fuzzy controllers, Mamdani fuzzy controllers and fuzzy logical controllers. Mamdani fuzzy controllers are based on the idea of interpolation while fuzzy logical controllers are based on fuzzy logic in its narrow sense, i.e., fuzzy propositional logic. The two types of fuzzy controllers treat IF-THEN rules differently. In Mamdani fuzzy controllers, rules are treated disjunctively. In fuzzy logic controllers, rules are treated conjunctively. Finally, we provide a unified proof of the property of universal approximation for both types of fuzzy controllers.
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.
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
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.
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.
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...
Institute of Scientific and Technical Information of China (English)
沈理
1997-01-01
A fuzzy logic control VLSI chip,F100,for industry process real-time control has been designed and fabricated with 0.8μm CMOS technology.The chip has the features of simplicity,felexibility and generality.This paper presents the Fuzzy control inrerence method of the chip,its VLSI implementation,and testing esign consideration.
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.
Fuzzy Logic Reliability Centered Maintenance
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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.
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.
DEFF Research Database (Denmark)
Shang, Yunlong; Zhang, Chenghui; Cui, Naxin;
2015-01-01
and electromagnetic interference (EMI). Furthermore, an adaptive fuzzy logic control (AFLC) algorithm is employed to online regulate the equalization period according to the voltage difference between cells and the cell voltage, not only greatly abbreviating the balancing time but also effectively preventing over...... cycle about 62% compared with the traditional fuzzy logic control (FLC) algorithm....
Ruspini, Enrique H.
1991-01-01
Summarized here are the results of recent research on the conceptual foundations of fuzzy logic. The focus is primarily on the principle characteristics of a model that quantifies resemblance between possible worlds by means of a similarity function that assigns a number between 0 and 1 to every pair of possible worlds. Introduction of such a function permits one to interpret the major constructs and methods of fuzzy logic: conditional and unconditional possibility and necessity distributions and the generalized modus ponens of Zadeh on the basis of related metric relationships between subsets of possible worlds.
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
Urban Intersection Traffic Signal Control Based on Fuzzy Logic
Institute of Scientific and Technical Information of China (English)
魏武; 张毅; 张佐; 宋靖雁
2002-01-01
This paper presents a fuzzy logic adaptive traffic signal control method for an isolated four-approach intersection with through and left-turning movements. In the proposed method, the fuzzy logic controller can make adjustments to signal timing in response to observed changes. The "urgency degree" term that can describe different user's demands for a green light is used in the fuzzy logic decision-making. In addition, a three-level fuzzy controller model decides whether to extend or terminate the current signal phase and the sequence of phases. Simulation results show that the fuzzy controller can adjust its signal timing in response to changing traffic conditions on a real-time basis and that the proposed fuzzy logic controller leads to less vehicle delays and a lower percentage of stopped vehicles.
Logical Characterisation of Ontology Construction using Fuzzy Description Logics
DEFF Research Database (Denmark)
Badie, Farshad; Götzsche, Hans
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.......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...
Fuzzy Logic for Incidence Geometry
2016-01-01
The paper presents a mathematical framework for approximate geometric reasoning with extended objects in the context of Geography, in which all entities and their relationships are described by human language. These entities could be labelled by commonly used names of landmarks, water areas, and so forth. Unlike single points that are given in Cartesian coordinates, these geographic entities are extended in space and often loosely defined, but people easily perform spatial reasoning with extended geographic objects “as if they were points.” Unfortunately, up to date, geographic information systems (GIS) miss the capability of geometric reasoning with extended objects. The aim of the paper is to present a mathematical apparatus for approximate geometric reasoning with extended objects that is usable in GIS. In the paper we discuss the fuzzy logic (Aliev and Tserkovny, 2011) as a reasoning system for geometry of extended objects, as well as a basis for fuzzification of the axioms of incidence geometry. The same fuzzy logic was used for fuzzification of Euclid's first postulate. Fuzzy equivalence relation “extended lines sameness” is introduced. For its approximation we also utilize a fuzzy conditional inference, which is based on proposed fuzzy “degree of indiscernibility” and “discernibility measure” of extended points. PMID:27689133
Fuzzy forecasting based on fuzzy-trend logical relationship groups.
Chen, Shyi-Ming; Wang, Nai-Yi
2010-10-01
In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.
Fuzzy logic 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.
Optimization of Fuzzy Logic Controller for Supervisory Power System Stabilizers
Directory of Open Access Journals (Sweden)
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.
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...
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.
Achieving of Fuzzy Automata for Processing Fuzzy Logic
Institute of Scientific and Technical Information of China (English)
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.
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...
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.
Probabilistic and fuzzy logic in clinical diagnosis.
Licata, G
2007-06-01
In this study I have compared classic and fuzzy logic and their usefulness in clinical diagnosis. The theory of probability is often considered a device to protect the classical two-valued logic from the evidence of its inadequacy to understand and show the complexity of world [1]. This can be true, but it is not possible to discard the theory of probability. I will argue that the problems and the application fields of the theory of probability are very different from those of fuzzy logic. After the introduction on the theoretical bases of fuzzy approach to logic, I have reported some diagnostic argumentations employing fuzzy logic. The state of normality and the state of disease often fight their battle on scalar quantities of biological values and it is not hard to establish a correspondence between the biological values and the percent values of fuzzy logic. Accordingly, I have suggested some applications of fuzzy logic in clinical diagnosis and in particular I have utilised a fuzzy curve to recognise subjects with diabetes mellitus, renal failure and liver disease. The comparison between classic and fuzzy logic findings seems to indicate that fuzzy logic is more adequate to study the development of biological events. In fact, fuzzy logic is useful when we have a lot of pieces of information and when we dispose to scalar quantities. In conclusion, increasingly the development of technology offers new instruments to measure pathological parameters through scalar quantities, thus it is reasonable to think that in the future fuzzy logic will be employed more in clinical diagnosis.
Properties of Measure-based Fuzzy Logic
Institute of Scientific and Technical Information of China (English)
无
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) Zadehs fuzzy logic is included in it as a particular case. (5) It gives more hints to the relationship between fuzzy logic and probability logic.
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.
Possible use of fuzzy logic in database
Directory of Open Access Journals (Sweden)
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.
Fuzzy logic marketing models for sustainable development
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Ioan Constantin ENACHE
2015-06-01
Full Text Available Fuzzy logic offers a different approach to describe economic and marketing phenomena. By providing a replacement for crisp values the fuzzy sets proved to be efficient alternatives for customer behaviour analysis. These advantages can provide a new way to address sustainable development issues. The present paper aims at presenting the main characteristics of fuzzy models and their main advantages. Evidence on how to implement a fuzzy model and what are its strong points are provided based on previous research and published scientific papers. It is concluded that fuzzy logic gives a different view on a wide range of topics
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.
Fuzzy Logic Unmanned Air Vehicle Motion Planning
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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.
Fuzzy Logic and Neuro-fuzzy Systems: A Systematic Introduction
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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.
Optimization of fuzzy logic controller and simulation in matlab
International Nuclear Information System (INIS)
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)
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.
Temperature Control System Using Fuzzy Logic Technique
Directory of Open Access Journals (Sweden)
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.
Synthesis water level control by fuzzy logic
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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
Fuzzy logic foundations of optimal inference
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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.
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...
Nursing and fuzzy logic: an integrative review.
Jensen, Rodrigo; Lopes, Maria Helena Baena de Moraes
2011-01-01
This study conducted an integrative review investigating how fuzzy logic has been used in research with the participation of nurses. The article search was carried out in the CINAHL, EMBASE, SCOPUS, PubMed and Medline databases, with no limitation on time of publication. Articles written in Portuguese, English and Spanish with themes related to nursing and fuzzy logic with the authorship or participation of nurses were included. The final sample included 21 articles from eight countries. For the purpose of analysis, the articles were distributed into categories: theory, method and model. In nursing, fuzzy logic has significantly contributed to the understanding of subjects related to: imprecision or the need of an expert; as a research method; and in the development of models or decision support systems and hard technologies. The use of fuzzy logic in nursing has shown great potential and represents a vast field for research.
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.
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 ...
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...
A Fuzzy Logic Based Sentiment Classification
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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.
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.
A FUZZY LOGIC APPLICATION IN VIRTUAL EDUCATION
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Thiago Drummond Moreira
2015-06-01
Full Text Available Traditionally, the teaching and learning process uses the problems resolving for fixing, transmitting and evaluating concepts and knowledge about a subject. Learning is the process of acquiring relative permanent changes in understanding, attitude, knowledge, information, capacity and ability through experience. A change can be decided or involuntary, to better or worsen learning. The learning process is an internal cognitive event. To help this teaching and learning process, it is important the use of a computer tool able to stimulate these changes. Also, it is important that it can function as validation and helping tool to the student. These functions are performed by computer systems called Intelligent Tutoring Systems. This paper describes the use of artificial intelligence techniques as a teaching support tool. Using Intelligent Tutoring Systems e fuzzy logic, this work shows, throgh eletronic ways, teaching will be more efficient and more adapted to students necessities, in group or individually.
A Brief History of Fuzzy Logic
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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.
Robust fuzzy logic stabilization with disturbance elimination.
Danapalasingam, Kumeresan A
2014-01-01
A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic controller that addresses unknown model uncertainties and an unmeasured disturbance. Since it is challenging to derive a highly accurate mathematical model, the proposed controller requires only nominal functions of a control system. In this paper, a mathematical derivation is carried out to prove that the controller is able to achieve asymptotic stability by processing state measurements. Robustness here refers to the ability of the controller to asymptotically steer the state vector towards the origin in the presence of model uncertainties and a disturbance input. Simulation results of the robust fuzzy logic controller application in a magnetic levitation system demonstrate the feasibility of the control design.
Fault Diagnosis in Deaerator Using Fuzzy Logic
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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.
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.
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.
Automating Software Development Process using Fuzzy Logic
Marcelloni, Francesco; Aksit, Mehmet; Damiani, Ernesto; Jain, Lakhmi C.; Madravio, Mauro
2004-01-01
In this chapter, we aim to highlight how fuzzy logic can be a valid expressive tool to manage the software development process. We characterize a software development method in terms of two major components: artifact types and methodological rules. Classes, attributes, operations, and inheritance an
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
Indeterminacy, linguistic semantics and fuzzy logic
Energy Technology Data Exchange (ETDEWEB)
Novak, V. [Univ. of Ostrava (Czech Republic)
1996-12-31
In this paper, we discuss the indeterminacy phenomenon which has two distinguished faces, namely uncertainty modeled especially by the probability theory and vagueness, modeled by fuzzy logic. Other important mathematical model of vagueness is provided by the Alternative Set Theory. We focus on some of the basic concepts of these theories in connection with mathematical modeling of the linguistic semantics.
Fuzzy logic systems are equivalent to feedforward neural networks
Institute of Scientific and Technical Information of China (English)
李洪兴
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.
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
基于Fuzzy Logic的PID自适应控制仿真%Simulation Research on PID Self Adaptive Control Based on Fuzzy Logic
Institute of Scientific and Technical Information of China (English)
刘文江; 马思根; 刘文海
2009-01-01
介绍了基于Fuzzy Logic的模糊控制原理,结合基于Fuzzy Logic的模糊控制和传统PID控制的优点,提出基于Fuzzy Logic的在线整定PID参数的自适应控制.Matlab仿真结果表明,控制系统具有很好的鲁棒性.
A STUDY OF FUZZY LOGICAL PETRI NETS AND ITS APPLICATION
Institute of Scientific and Technical Information of China (English)
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.
Modeling of Kefir Production with Fuzzy Logic
Directory of Open Access Journals (Sweden)
Hüseyin Nail Akgül
2014-06-01
Full Text Available The fermentation is ended with pH 4.6 values in industrial production of kefir. In this study, the incubation temperature, the incubation time and inoculums of culture were chose as variable parameters of kefir. In conventional control systems, the value of pH can be found by trial method. In these systems, if the number of input parameters is greater, the method of trial and error creates a system dependent on the person as well as troublesome. Fuzzy logic can be used in such cases. Modeling studies with this fuzzy logic control are examined in two portions. The first part consists of fuzzy rules and membership functions, while the second part consists of clarify. Kefir incubation temperature between 20 and 25°C, the incubation period between 18 to 22 hours and the inoculum ratio of culture between 1-5% are selected for optimum production conditions. Three separate fuzzy sets (triangular membership function are used to blur the incubation temperature, the incubation time and the inoculum ratio of culture. Because the membership function numbers belonging to the the input parameters are 3 units, 3x3x3=27 line rule is obtained by multiplying these numbers. The table of fuzzy rules was obtained using the method of Mamdani. The membership function values were determined by the method of average weight using three trapezoidal area of membership functions created for clarification. The success of the system will be found, comparing the numerical values obtained with pH values that should be. Eventually, to achieve the desired pH value of 4.6 in the production of kefir, with the using of fuzzy logic, the workload of people will be decreased and the productivity of business can be increased. In this case, it can be provided savings in both cost and time.
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...
Searching the Arcane Origins of Fuzzy Logic
Directory of Open Access Journals (Sweden)
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.
Reasoning formalism in Boolean operator fuzzy logic
Institute of Scientific and Technical Information of China (English)
邓安生; 刘叙华
1995-01-01
Based on the newly introduced concepts of true-level and false-level, the formal structure of reasoning in Boolean operator fuzzy logic is presented. As a generalization of the theory of epistemic process in open logic, a formalism is also proposed to describe human reasoning with uncertain, inconsistent and insufficient knowledge, which can characterize the knowledge increment and revision, as well as the epistemic evolution. The formalism provides an explanation to the dynamic properties of human reasoning, i. e. continuous revision and combination of beliefs.
Fuzzy logic model to quantify risk perception
International Nuclear Information System (INIS)
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)
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...
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.
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
Automated interpretation of LIBS spectra using a fuzzy logic inference engine.
Hatch, Jeremy J; McJunkin, Timothy R; Hanson, Cynthia; Scott, Jill R
2012-03-01
Automated interpretation of laser-induced breakdown spectroscopy (LIBS) data is necessary due to the plethora of spectra that can be acquired in a relatively short time. However, traditional chemometric and artificial neural network methods that have been employed are not always transparent to a skilled user. A fuzzy logic approach to data interpretation has now been adapted to LIBS spectral interpretation. Fuzzy logic inference rules were developed using methodology that includes data mining methods and operator expertise to differentiate between various copper-containing and stainless steel alloys as well as unknowns. Results using the fuzzy logic inference engine indicate a high degree of confidence in spectral assignment.
Structural modeling and fuzzy-logic based diagnosis of a ship propulsion benchmark
DEFF Research Database (Denmark)
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....
Structural modeling and fuzzy-logic based diagnosis of a ship propulsion benchmark
DEFF Research Database (Denmark)
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...
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
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...
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...
NETWORK INTRUSION DETECTION SYSTEM USING FUZZY LOGIC
Directory of Open Access Journals (Sweden)
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.
Delay Computation Using Fuzzy Logic Approach
Directory of Open Access Journals (Sweden)
Ramasesh G. R.
2012-10-01
Full Text Available The paper presents practical application of fuzzy sets and system theory in predicting delay, with reasonable accuracy, a wide range of factors pertaining to construction projects. In this paper we shall use fuzzy logic to predict delays on account of Delayed supplies and Labor shortage. It is observed that the project scheduling software use either deterministic method or probabilistic method for computation of schedule durations, delays, lags and other parameters. In other words, these methods use only quantitative inputs leaving-out the qualitative aspects associated with individual activity of work. The qualitative aspect viz., the expertise of the mason or the lack of experience can have a significant impact on the assessed duration. Such qualitative aspects do not find adequate representation in the Project Scheduling software. A realistic project is considered for which a PERT chart has been prepared using showing all the major activities in reasonable detail. This project has been periodically updated until its completion. It is observed that some of the activities are delayed due to extraneous factors resulting in the overall delay of the project. The software has the capability to calculate the overall delay through CPM (Critical Path Method when each of the activity-delays is reported. We shall now demonstrate that by using fuzzy logic, these delays could have been predicted well in advance.
Mapping Shape Geometry And Emotions Using Fuzzy Logic
DEFF Research Database (Denmark)
Achiche, Sofiane; Ahmed, Saeema
2008-01-01
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...... 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....
Fuzzy Logic Connectivity in Semiconductor Defect Clustering
Energy Technology Data Exchange (ETDEWEB)
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.
Output-back fuzzy logic systems and equivalence with feedback neural networks
Institute of Scientific and Technical Information of China (English)
无
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.
A fuzzy logical model of letter identification.
Oden, G C
1979-05-01
Stimuli were generated by factorially varying two sets of features that distinguish between two letter patterns. Subjects rated the degree to which each stimulus was an instance of one letter rather than the alternative. The obtained ratings were relatively continuous and systematic functions of the feature manipulations. The results were well accounted for by a model in which (a) each feature has an associated fuzzy predicate that is used to independently evaluate the degree to which it is true that the feature is present in the stimulus; (b) the featural truth values are integrated according to fuzzy logical expressions that correspond directly to propositional descriptions of each letter pattern; and (c) the resulting goodness of match to the stimulus for each letter is compared to that of the alternatives to determine the final identification. PMID:528944
Power transformer protection by using Fuzzy Logic
Directory of Open Access Journals (Sweden)
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
FUZZY LOGIC CONTROLLER IMPLEMENTATION FOR PHOTOVOLTAIC STATION
Directory of Open Access Journals (Sweden)
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.
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.
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...
Fault Diagnosis and Reliability Analysis Using Fuzzy Logic Method
Institute of Scientific and Technical Information of China (English)
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 humans 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.
The Application of Fuzzy Logic to Collocation Extraction
Bisht, Raj Kishor
2008-01-01
Collocations are important for many tasks of Natural language processing such as information retrieval, machine translation, computational lexicography etc. So far many statistical methods have been used for collocation extraction. Almost all the methods form a classical crisp set of collocation. We propose a fuzzy logic approach of collocation extraction to form a fuzzy set of collocations in which each word combination has a certain grade of membership for being collocation. Fuzzy logic provides an easy way to express natural language into fuzzy logic rules. Two existing methods; Mutual information and t-test have been utilized for the input of the fuzzy inference system. The resulting membership function could be easily seen and demonstrated. To show the utility of the fuzzy logic some word pairs have been examined as an example. The working data has been based on a corpus of about one million words contained in different novels constituting project Gutenberg available on www.gutenberg.org. The proposed me...
Applications of Fuzzy Logic in Image Processing – A Brief
Directory of Open Access Journals (Sweden)
Mahesh Prasanna K
2015-10-01
Full Text Available The subject of this study is to show the application of fuzzy logic in image processing with a brief introduction to fuzzy logic and digital image processing. Digital image processing is an ever expanding and dynamic area with applications reaching out into our everyday life such as medicine, space exploration, surveillance, authentication, automated industry inspection and many more areas. Fuzzy logic, one of the decision-making techniques of artificial intelligence, has many application areas. Although it has been subjected to criticisms since its birth, especially in recent years, fuzzy logic has been proven to be applicable in almost all scientific fields. This shows that the concept of fuzzy logic will maintain its validity and the number of fields where it draws attention will increase further.
Fingerprint Recognition using Fuzzy Logic with Triangular Pattern Template
DEFF Research Database (Denmark)
Hussain, Dil Muhammad Akbar
2006-01-01
in a fingerprint for identification. However, we propose a fingerprint recognition technique using fuzzy logic. This approach utilizes two types of features; ridge ending and bifurcation to construct triangles and the fuzzy logic determine the degree of membership for matching. The unique difference in our...... implementation is that fuzzy concept is used for matching rather than the feature extraction. The laboratory results obtained indicate that matching process efficiency can be improved using this technique....
Chen, Shyi-Ming; Chen, Shen-Wen
2015-03-01
In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy-trend logical relationships. Firstly, the proposed method fuzzifies the historical training data of the main factor and the secondary factor into fuzzy sets, respectively, to form two-factors second-order fuzzy logical relationships. Then, it groups the obtained two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, it calculates the probability of the "down-trend," the probability of the "equal-trend" and the probability of the "up-trend" of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group, respectively. Finally, it performs the forecasting based on the probabilities of the down-trend, the equal-trend, and the up-trend of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the NTD/USD exchange rates. The experimental results show that the proposed method outperforms the existing methods.
Directory of Open Access Journals (Sweden)
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
Active structural control by fuzzy logic rules: An introduction
Energy Technology Data Exchange (ETDEWEB)
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.
Active structural control by fuzzy logic rules: An introduction
Energy Technology Data Exchange (ETDEWEB)
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.
Variable universe adaptive fuzzy control on the quadruple inverted pendulum
Institute of Scientific and Technical Information of China (English)
LI; Hongxing(
2002-01-01
［1］Magana,M.E.,Fuzzy-logic control of an inverted pendulum with vision feedback,IEEE Transactions on Education,1998,41(2):165.［2］Chen,C.S.,Chen,W.L.,Robust adaptive sliding-mode control using fuzzy modeling for an inverted-pendulum system,IEEE Transactions on Industrial Electronics,1998,45(2):297.［3］Cheng,F.Y.,Zhong,G.M.,Li,Y.S.et al.,Fuzzy control of a double-inverted pendulum,Fuzzy Sets and System,1996,79(3):315-321.［4］Zhang,H.M.,Ma,X.W.,Xu,W.et al.,Design fuzzy controllers complex systems with an application to 3-stage inverted pendulums,Information Sciences,1993,72:271.［5］Zhang,M.L.,Hao,J.K.,Hei,W.D.,Personification intelligence control and triple inverted pendulum,Journal of Aeronautics (in Chinese),1995,16(4):654.［6］Li,H.X.,To see the success of fuzzy logic from mathematical essence of fuzzy control,Fuzzy Systems and Mathematics (in Chinese),1995,9(4):1-14.［7］Li,H.X.,Mathematical essence of fuzzy controls and design of a kind of high precision fuzzy controllers,Control Theory and Application (in Chinese),1997,14(6):868.［8］Li,H.X.,Adaptive fuzzy controllers based on variable universe,Science in China,Ser.E,1999,42(1):10.［9］Li,H.X.,Interpolation mechanism of fuzzy control,Science in China,Ser.E,1998,41(3):312.［10］Li,H.X.,The equivalence between fuzzy logic systems and feedforward neural networks,Science in China,Ser.E,2000,43(1):42.
SOFC temperature evaluation based on an adaptive fuzzy controller
Institute of Scientific and Technical Information of China (English)
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.
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...
Compensatory fuzzy logic for intelligent social network analysis
Directory of Open Access Journals (Sweden)
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.
Fuzzy Optimized Metric for Adaptive Network Routing
Directory of Open Access Journals (Sweden)
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 .
Fuzzy logic and its application in football team ranking.
Zeng, Wenyi; Li, Junhong
2014-01-01
Fuzzy set theory and fuzzy logic are a highly suitable and applicable basis for developing knowledge-based systems in physical education for tasks such as the selection for athletes, the evaluation for different training approaches, the team ranking, and the real-time monitoring of sports data. In this paper, we use fuzzy set theory and apply fuzzy clustering analysis in football team ranking. Based on some certain rules, we propose four parameters to calculate fuzzy similar matrix, obtain fuzzy equivalence matrix and the ranking result for our numerical example, T 7, T 3, T 1, T 9, T 10, T 8, T 11, T 12, T 2, T 6, T 5, T 4, and investigate four parameters sensitivity analysis. The study shows that our fuzzy logic method is reliable and stable when the parameters change in certain range.
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
Hybrid Genetic Algorithms with Fuzzy Logic Controller
Institute of Scientific and Technical Information of China (English)
无
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.``
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.
CAC Algorithm Based on Fuzzy Logic
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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.
Adaptive Fuzzy Control for CVT Vehicle
Institute of Scientific and Technical Information of China (English)
无
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.
FUZZY LOGIC APPLICATION IN POWER SYSTEM FAULT DIAGNOSIS
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KRISNA KANT GAUTAM
2011-09-01
Full Text Available Fuzzy logic allows a convenient way to incorporate the knowledge of human experts into the expert systems using qualitative and natural language-like expressions. Recent advances in the field of fuzzy systems and a number of successful real-world applications in power systems show that logic can be efficiently applied to dealwith imprecision, ambiguity and probabilistic information in input data. Fuzzy logic based systems with their capability to deal with incomplete information, imprecision, and incorporation of qualitative knowledge have shown great potential for application in electric system fault detection.
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.
Yan, Gang; Zhou, Lily L.
2006-09-01
This study presents a design strategy based on genetic algorithms (GA) for semi-active fuzzy control of structures that have magnetorheological (MR) dampers installed to prevent damage from severe dynamic loads such as earthquakes. The control objective is to minimize both the maximum displacement and acceleration responses of the structure. Interactive relationships between structural responses and input voltages of MR dampers are established by using a fuzzy controller. GA is employed as an adaptive method for design of the fuzzy controller, which is here known as a genetic adaptive fuzzy (GAF) controller. The multi-objectives are first converted to a fitness function that is used in standard genetic operations, i.e. selection, crossover, and mutation. The proposed approach generates an effective and reliable fuzzy logic control system by powerful searching and self-learning adaptive capabilities of GA. Numerical simulations for single and multiple damper cases are given to show the effectiveness and efficiency of the proposed intelligent control strategy.
An Evaluation of Total Project Risk Based on Fuzzy Logic
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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.
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…
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.
Improvement of Transient Stability using Fuzzy Logic Controlled SMES
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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.
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...
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...
Fuzzy logic color detection: Blue areas in melanoma dermoscopy images.
Lingala, Mounika; Stanley, R Joe; Rader, Ryan K; Hagerty, Jason; Rabinovitz, Harold S; Oliviero, Margaret; Choudhry, Iqra; Stoecker, William V
2014-07-01
Fuzzy logic image analysis techniques were used to analyze three shades of blue (lavender blue, light blue, and dark blue) in dermoscopic images for melanoma detection. A logistic regression model provided up to 82.7% accuracy for melanoma discrimination for 866 images. With a support vector machines (SVM) classifier, lower accuracy was obtained for individual shades (79.9-80.1%) compared with up to 81.4% accuracy with multiple shades. All fuzzy blue logic alpha cuts scored higher than the crisp case. Fuzzy logic techniques applied to multiple shades of blue can assist in melanoma detection. These vector-based fuzzy logic techniques can be extended to other image analysis problems involving multiple colors or color shades.
Application of fuzzy logic in performance management: a literature review
Directory of Open Access Journals (Sweden)
Verónica Gurrea
2014-07-01
Full Text Available Performance management has become in a key success factor for any organization. Traditionally, performance management has focused uniquely in financial measures, mainly using quantitative measures, but two decades ago they were extended towards an integral view of the organization, appearing qualitative measures. This type of extended view and associated measures have a degree of uncertainty that needs to be bounded. One of the essential tools for uncertainty bounding is the fuzzy logic and, therefore,the main objective of this paper is the analysis of the literature about the application of fuzzy logic in performance measurement systems operating within uncertainty environments with the aim of categorizing, conceptualizing and classifying the works written so far. Finally, three categories are defined according to the different uses of fuzzy logic within performance management concluding that the most important application of fuzzy logic that counts with a higher number of studies is uncertainty bounding.
Application of Fuzzy Logic in Control of Switched Reluctance Motor
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Pavel Brandstetter
2006-01-01
Full Text Available The flux linkage of switched reluctance motor (SRM depends on the stator current and position between the rotor and stator poles. The fact determines that during control of SRM current with the help of classical PI controllers in a wide regulation range unsatisfied results occur. The main reasons of the mentioned situation are big changes of the stator inductance depending on the stator current and rotor position. In a switched reluctance motor the stator phase inductance is a non-linear function of the stator phase current and rotor position. Fuzzy controller and fuzzy logic are generally non-linear systems; hence they can provide better performance in this case. Fuzzy controller is mostly presented as a direct fuzzy controller or as a system, which realizes continued changing parameters of other controller, so-called fuzzy supervisor. Referring to the usage of fuzzy logic as a supervisor of conventional PI controller in control of SRM possible improvement occurs.
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.
Some Fuzzy Logic Based Methods to Deal with Sensorial Information
Institute of Scientific and Technical Information of China (English)
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.
Control of Overhead Crane while Using Fuzzy Logic Dispatcher
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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.
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...
Application of fuzzy logic in content-based image retrieval
Institute of Scientific and Technical Information of China (English)
WANG Xiao-ling; XIE Kang-lin
2008-01-01
We propose a fuzzy logic-based image retrieval system, in which the image similarity can be inferred in a nonlinear manner as human thinking. In the fuzzy inference process, weight assignments of multi-image features were resolved impliedly. Each fuzzy rule was embedded into the subjectivity of human perception of image contents. A color histogram called the average area histogram is proposed to represent the color features. Experimental results show the efficiency and feasibility of the proposed algorithms.
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 $[0,1]$.
Saravanan, Vijayakumar; Lakshmi, P T V
2014-09-01
The path to personalized medicine demands the use of new and customized biopharmaceutical products containing modified proteins. Hence, assessment of these products for allergenicity becomes mandatory before they are introduced as therapeutics. Despite the availability of different tools to predict the allergenicity of proteins, it remains challenging to predict the allergens and nonallergens, when they share significant sequence similarity with known nonallergens and allergens, respectively. Hence, we propose "FuzzyApp," a novel fuzzy rule based system to evaluate the quality of the query protein to be an allergen. It measures the allergenicity of the protein based on the fuzzy IF-THEN rules derived from five different modules. On various datasets, FuzzyApp outperformed other existing methods and retained balance between sensitivity and specificity, with positive Mathew's correlation coefficient. The high specificity of allergen-like putative nonallergens (APN) revealed the FuzzyApp's capability in distinguishing the APN from allergens. In addition, the error analysis and whole proteome dataset analysis suggest the efficiency and consistency of the proposed method. Further, FuzzyApp predicted the Tropomyosin from various allergenic and nonallergenic sources accurately. The web service created allows batch sequence submission, and outputs the result as readable sentences rather than values alone, which assists the user in understanding why and what features are responsible for the prediction. FuzzyApp is implemented using PERL CGI and is freely accessible at http://fuzzyapp.bicpu.edu.in/predict.php . We suggest the use of Fuzzy logic has much potential in biomarker and personalized medicine research to enhance predictive capabilities of post-genomics diagnostics.
Fuzzy logic and genetic algorithms for intelligent control of structures using MR dampers
Yan, Gang; Zhou, Lily L.
2004-07-01
Fuzzy logic control (FLC) and genetic algorithms (GA) are integrated into a new approach for the semi-active control of structures installed with MR dampers against severe dynamic loadings such as earthquakes. The interactive relationship between the structural response and the input voltage of MR dampers is established by using a fuzzy controller rather than the traditional way by introducing an ideal active control force. GA is employed as an adaptive method for optimization of parameters and for selection of fuzzy rules of the fuzzy control system, respectively. The maximum structural displacement is selected and used as the objective function to be minimized. The objective function is then converted to a fitness function to form the basis of genetic operations, i.e. selection, crossover, and mutation. The proposed integrated architecture is expected to generate an effective and reliable fuzzy control system by GA"s powerful searching and self-learning adaptive capability.
A FUZZY LOGIC CONTROLLERFORA TWO-LINK FUNCTIONAL MANIPULATOR
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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
Directory of Open Access Journals (Sweden)
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.
Optimized and Self-Organized Fuzzy Logic Controller for pH Neutralization Process
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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.
A fuzzy logic cooperative MAC for MANET
Institute of Scientific and Technical Information of China (English)
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.
Astronomical pipeline processing using fuzzy logic
Shamir, Lior
In the past few years, pipelines providing astronomical data have been becoming increasingly important. The wide use of robotic telescopes has provided significant discoveries, and sky survey projects such as SDSS and the future LSST are now considered among the premier projects in the field astronomy. The huge amount of data produced by these pipelines raises the need for automatic processing. Astronomical pipelines introduce several well-defined problems such as astronomical image compression, cosmic-ray hit rejection, transient detection, meteor triangulation and association of point sources with their corresponding known stellar objects. We developed and applied soft computing algorithms that provide new or improved solutions to these growing problems in the field of pipeline processing of astronomical data. One new approach that we use is fuzzy logic-based algorithms, which enables the automatic analysis of the astronomical pipelines and allows mining the data for not-yet-known astronomical discoveries such as optical transients and variable stars. The developed algorithms have been tested with excellent results on the NightSkyLive sky survey, which provides a pipeline of 150 astronomical pictures per hour, and covers almost the entire global night sky.
Using fuzzy logic to integrate neural networks and knowledge-based systems
Yen, John
1991-01-01
Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.
Fuzzy logic model of Langmuir probe discharge data.
Kim, Byungwhan; Park, Jang Hyun; Kim, Beom-Soo
2002-11-01
Plasma models are crucial to gain physical insights into complex discharges as well as to optimizing plasma-driven processes. As an alternative to physical model, a qualitative model was constructed using adaptive fuzzy logic called adaptive network fuzzy inference system (ANFIS). Prediction performance of ANFIS was evaluated on two sets of experimental discharge data. One referred to as hemispherical inductively coupled plasma (HICP) was characterized with a 2(4) full factorial experiment, in which the factors that were varied include source power, pressure, chuck position, and Cl2 flow rate. The other called multipole ICP was characterized by performing a 3(3) full factorial experiment on the factors, including source power, pressure, and Ar flow rate. Trained ANFIS models were tested on eight and 16 experiments not pertaining to previous training data for HICP and MICP, respectively. Plasma attributes modeled include electron density. electron temperature, and plasma potential. The performance of ANFIS was optimized as a function of a type of membership function, number of membership function, and two learning factors. The number of membership functions was different depending on the type of plasma data and employing too large number of membership functions resulted in a drastic degradation in prediction performances. Optimized ANFIS models were compared to statistical regression models and demonstrated improved predictions in all comparisons. PMID:12385474
Automated Interpretation of LIBS Spectra using a Fuzzy Logic Inference Engine
Energy Technology Data Exchange (ETDEWEB)
Jeremy J. Hatch; Timothy R. McJunkin; Cynthia Hanson; Jill R. Scott
2012-02-01
Automated interpretation of laser-induced breakdown spectroscopy (LIBS) data is necessary due to the plethora of spectra that can be acquired in a relatively short time. However, traditional chemometric and artificial neural network methods that have been employed are not always transparent to a skilled user. A fuzzy logic approach to data interpretation has now been adapted to LIBS spectral interpretation. A fuzzy logic inference engine (FLIE) was used to differentiate between various copper containing and stainless steel alloys as well as unknowns. Results using FLIE indicate a high degree of confidence in spectral assignment.
Energy Technology Data Exchange (ETDEWEB)
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
Fuzzy Logic Controller based on geothermal recirculating aquaculture system
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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.
Adaptive fuzzy controllers based on variable universe
Institute of Scientific and Technical Information of China (English)
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.
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.
INDIRECT ACCELERATED ADAPTIVE FUZZY CONTROLLER
Institute of Scientific and Technical Information of China (English)
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.
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 ...
Fuzzy logic control and optimization system
Energy Technology Data Exchange (ETDEWEB)
Lou, Xinsheng (West Hartford, CT)
2012-04-17
A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
Energy Technology Data Exchange (ETDEWEB)
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
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.
FUZZY LOGIC BASED ENERGY EFFICIENT PROTOCOL IN WIRELESS SENSOR NETWORKS
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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.
Fuzzy Logic as a Tool for Assessing Students’ Knowledge and Skills
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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.
Fuzzy logic applications to expert systems and control
Lea, Robert N.; Jani, Yashvant
1991-01-01
A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings.
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.
Modeling Academic Performance Evaluation Using Soft Computing Techniques: A Fuzzy Logic Approach
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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.
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.
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.
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.
Improved adaptive fuzzy control for MIMO nonlinear time-delay systems
Institute of Scientific and Technical Information of China (English)
无
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...
Fuzzy Logic for Elimination of Redundant Information of Microarray Data
Institute of Scientific and Technical Information of China (English)
Edmundo Bonilla Huerta; Béatrice Duval; Jin-Kao Hao
2008-01-01
Gene subset selection is essential for classification and analysis of microarray data. However, gene selection is known to be a very difficult task since gene expression data not only have high dimensionalities, but also contain redundant information and noises. To cope with these difficulties, this paper introduces a fuzzy logic based pre-processing approach composed of two main steps. First, we use fuzzy inference rules to transform the gene expression levels of a given dataset into fuzzy values. Then we apply a similarity relation to these fuzzy values to define fuzzy equivalence groups, each group containing strongly similar genes. Dimension reduction is achieved by considering for each group of similar genes a single representative based on mutual information. To assess the usefulness of this approach, extensive experimentations were carried out on three well-known public datasets with a combined classification model using three statistic filters and three classifiers.
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
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 ...
A New Approach of Learning Hierarchy Construction Based on Fuzzy Logic
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Ali AAJLI
2014-10-01
Full Text Available In recent years, adaptive learning systems rely increasingly on learning hierarchy to customize the educational logic developed in their courses. Most approaches do not consider that the relationships of prerequisites between the skills are fuzzy relationships. In this article, we describe a new approach of a practical application of fuzzy logic techniques to the construction of learning hierarchies. For this, we use a learning hierarchy predefined by one or more experts of a specific field. However, the relationships of prerequisites between the skills in the learning hierarchy are not definitive and they are fuzzy relationships. Indeed, we measure relevance degree of all relationships existing in this learning hierarchy and we try to answer to the following question: Is the relationships of prerequisites predefined in initial learning hierarchy are correctly established or not?
Automated cloud classification with a fuzzy logic expert system
Tovinkere, Vasanth; Baum, Bryan A.
1993-01-01
An unresolved problem in current cloud retrieval algorithms concerns the analysis of scenes containing overlapping cloud layers. Cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget. Most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. One promising method uses fuzzy logic to determine whether mixed cloud and/or surface types exist within a group of pixels, such as cirrus, land, and water, or cirrus and stratus. When two or more class types are present, fuzzy logic uses membership values to assign the group of pixels partially to the different class types. The strength of fuzzy logic lies in its ability to work with patterns that may include more than one class, facilitating greater information extraction from satellite radiometric data. The development of the fuzzy logic rule-based expert system involves training the fuzzy classifier with spectral and textural features calculated from accurately labeled 32x32 regions of Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data. The spectral data consists of AVHRR channels 1 (0.55-0.68 mu m), 2 (0.725-1.1 mu m), 3 (3.55-3.93 mu m), 4 (10.5-11.5 mu m), and 5 (11.5-12.5 mu m), which include visible, near-infrared, and infrared window regions. The textural features are based on the gray level difference vector (GLDV) method. A sophisticated new interactive visual image Classification System (IVICS) is used to label samples chosen from scenes collected during the FIRE IFO II. The training samples are chosen from predefined classes, chosen to be ocean, land, unbroken stratiform, broken stratiform, and cirrus. The November 28, 1991 NOAA overpasses contain complex multilevel cloud situations ideal for training and validating the fuzzy logic expert system.
Adaptive Fuzzy Dynamic Surface Control for Uncertain Nonlinear Systems
Institute of Scientific and Technical Information of China (English)
Xiao-Yuan Luo; Zhi-Hao Zhu; Xin-Ping Guan
2009-01-01
In this paper, a robust adaptive fuzzy dynamic surface control for a class of uncertain nonlinear systems is proposed. A novel adaptive fuzzy dynamic surface model is built to approximate the uncertain nonlinear functions by only one fuzzy logic system. The approximation capability of this model is proved and the model is implemented to solve the problem that too many approximators are used in the controller design of uncertain nonlinear systems. The shortage of "explosion of complexity" in backstepping design procedure is overcome by using the proposed dynamic surface control method. It is proved by constructing appropriate Lyapunov candidates that all signals of closed-loop systems are semi-globaily uniformly ultimate bounded. Also, this novel controller stabilizes the states of uncertain nonlinear systems faster than the adaptive sliding mode controller (SMC). Two simulation examples are provided to illustrate the effectiveness of the control approach proposed in this paper.
Noise-based logic: Binary, multi-valued, or fuzzy, with optional superposition of logic states
Kish, Laszlo B
2008-01-01
A new type of deterministic (non-probabilistic) computer logic system inspired by the stochasticity of brain signals is shown. The distinct values are represented by independent stochastic processes: independent voltage (or current) noises. The orthogonality of these processes provides a natural way to construct binary or multi-valued logic circuitry with arbitrary number N of logic values by using analog circuitry. Moreover, the logic values on a single wire can be made a (weighted) superposition of the N distinct logic values. Fuzzy logic is also naturally represented by a two-component superposition within the binary case (N=2). Error propagation and accumulation are suppressed. Other relevant advantages are reduced energy dissipation and leakage current problems, and robustness against circuit noise and background noises such as 1/f, Johnson, shot and crosstalk noise. Variability problems are also nonexistent because the logic value is an AC signal. A similar logic system can be built with orthogonal sinu...
A practical introduction to fuzzy logic using LISP
Argüelles Mendez, Luis
2016-01-01
This book makes use of the LISP programming language to provide readers with the necessary background to understand and use fuzzy logic to solve simple to medium-complexity real-world problems. It introduces the basics of LISP required to use a Fuzzy LISP programming toolbox, which was specifically implemented by the author to “teach” the theory behind fuzzy logic and at the same time equip readers to use their newly-acquired knowledge to build fuzzy models of increasing complexity. The book fills an important gap in the literature, providing readers with a practice-oriented reference guide to fuzzy logic that offers more complexity than popular books yet is more accessible than other mathematical treatises on the topic. As such, students in first-year university courses with a basic tertiary mathematical background and no previous experience with programming should be able to easily follow the content. The book is intended for students and professionals in the fields of computer science and engineering, ...
Modeling and simulation of evacuation behavior using fuzzy logic in a goal finding application
Sharma, Sharad; Ogunlana, Kola; Sree, Swetha
2016-05-01
Modeling and simulation has been widely used as a training and educational tool for depicting different evacuation strategies and damage control decisions during evacuation. However, there are few simulation environments that can include human behavior with low to high levels of fidelity. It is well known that crowd stampede induced by panic leads to fatalities as people are crushed or trampled. Our proposed goal finding application can be used to model situations that are difficult to test in real-life due to safety considerations. It is able to include agent characteristics and behaviors. Findings of this model are very encouraging as agents are able to assume various roles to utilize fuzzy logic on the way to reaching their goals. Fuzzy logic is used to model stress, panic and the uncertainty of emotions. The fuzzy rules link these parts together while feeding into behavioral rules. The contributions of this paper lies in our approach of utilizing fuzzy logic to show learning and adaptive behavior of agents in a goal finding application. The proposed application will aid in running multiple evacuation drills for what-if scenarios by incorporating human behavioral characteristics that can scale from a room to building. Our results show that the inclusion of fuzzy attributes made the evacuation time of the agents closer to the real time drills.
Professional Learning: A Fuzzy Logic-Based Modelling Approach
Gravani, M. N.; Hadjileontiadou, S. J.; Nikolaidou, G. N.; Hadjileontiadis, L. J.
2007-01-01
Studies have suggested that professional learning is influenced by two key parameters, i.e., climate and planning, and their associated variables (mutual respect, collaboration, mutual trust, supportiveness, openness). In this paper, we applied analysis of the relationships between the proposed quantitative, fuzzy logic-based model and a series of…
Capturing hand tremors with a fuzzy logic wheelchair joystick controller
Zwaag, van der Berend-Jan; Corbett, Dan
1999-01-01
We have designed and built a fuzzy logic wheelchair controller which minimizes the effect of Multiple Sclerosis and tremors. The aim of our project has been to give people with Multiple Sclerosis better control of an electric wheelchair by removing tremors from the joystick signal. The system interc
Minimising tremor in a joystick using fuzzy logic
Zwaag, van der Berend-Jan; Corbett, Dan; Jain, Lakhmi
1999-01-01
We have designed and built a fuzzy logic controller which minimises the effect of Multiple Sclerosis (MS) hand tremors. The aim of our project has been to give people with Multiple Sclerosis better control of an electronic wheelchair by removing tremors from the joystick signal. The system intercept
PERFORMANCE ANALYSIS OF IMAGE COMPRESSION USING FUZZY LOGIC ALGORITHM
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Rohit Kumar Gangwar
2014-04-01
Full Text Available With the increase in demand, product of multimedia is increasing fast and thus contributes to insufficient network bandwidth and memory storage. Therefore image compression is more significant for reducing data redundancy for save more memory and transmission bandwidth. An efficient compression technique has been proposed which combines fuzzy logic with that of Huffman coding. While normalizing image pixel, each value of pixel image belonging to that image foreground are characterized and interpreted. The image is sub divided into pixel which is then characterized by a pair of set of approximation. Here encoding represent Huffman code which is statistically independent to produce more efficient code for compression and decoding represents rough fuzzy logic which is used to rebuilt the pixel of image. The method used here are rough fuzzy logic with Huffman coding algorithm (RFHA. Here comparison of different compression techniques with Huffman coding is done and fuzzy logic is applied on the Huffman reconstructed image. Result shows that high compression rates are achieved and visually negligible difference between compressed images and original images.
Bicycle Frame Prediction Techniques with Fuzzy Logic Method
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Rafiuddin Syam
2015-03-01
Full Text Available In general, an appropriate size bike frame would get comfort to the rider while biking. This study aims to predict the simulation system on the bike frame sizes with fuzzy logic. Testing method used is the simulation test. In this study, fuzzy logic will be simulated using Matlab language to test their performance. Mamdani fuzzy logic using 3 variables and 1 output variable intake. Triangle function for the input and output. The controller is designed in the type mamdani with max-min composition and the method deffuzification using center of gravity method. The results showed that height, inseam and Crank Size generating appropriate frame size for the rider associated with comfort. Has a height range between 142 cm and 201 cm. Inseam has a range between 64 cm and 97 cm. Crank has a size range between 175 mm and 180 mm. The simulation results have a range of frame sizes between 13 inches and 22 inches. By using the fuzzy logic can be predicted the size frame of bicycle suitable for the biker.
Genetic Algorithm Tuned Fuzzy Logic Controller for Rotary Inverted Pendulum
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Tzu-Chun Kuo
2013-06-01
Full Text Available In this study, a Genetic Algorithm (GA is proposed to search for the optimal input membership functions of the fuzzy logic controller. With the optimal membership function, the fuzzy logic controller can efficiently control a rotary inverted pendulum. The advantage of the proposed method is tuning the parameters of membership functions automatically rather than tuning them manually. In genetic algorithm, these parameters are converted to a chromosome which is encoded into a binary string. Because the membership functions are symmetric to zero, the length of each chromosome could be reduced by half. The computation time will also be shorter with the shorter chromosomes. Moreover, the roulette wheel selection is chosen as reproduction operator and one-point crossover operator and random mutation operator are also used. After the genetic algorithm completes searching for optimal parameters, the optimal membership function will be introduced to the fuzzy logic controller. Finally, simulation results show that the proposed GA-tuned fuzzy logic controller is effective for the rotary inverted pendulum control system with robust stabilization capability.
Autonomous vehicle motion control, approximate maps, and fuzzy logic
Ruspini, Enrique H.
1993-01-01
Progress on research on the control of actions of autonomous mobile agents using fuzzy logic is presented. The innovations described encompass theoretical and applied developments. At the theoretical level, results of research leading to the combined utilization of conventional artificial planning techniques with fuzzy logic approaches for the control of local motion and perception actions are presented. Also formulations of dynamic programming approaches to optimal control in the context of the analysis of approximate models of the real world are examined. Also a new approach to goal conflict resolution that does not require specification of numerical values representing relative goal importance is reviewed. Applied developments include the introduction of the notion of approximate map. A fuzzy relational database structure for the representation of vague and imprecise information about the robot's environment is proposed. Also the central notions of control point and control structure are discussed.
Advanced Fuzzy Logic Based Admission Control for UMTS System
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P. Kejik
2010-12-01
Full Text Available The capacity of CDMA (Code Division Multiple Access systems is interference limited. Therefore radio resources management (RRM functions are used. They are responsible for supplying optimum coverage, ensuring efficient use of physical resources, and providing the maximum planned capacity. This paper deals with admission control techniques for UMTS (Universal Mobile Telecommunication System. A UMTS system model and four fuzzy logic based admission control algorithms are presented in this paper. Two new versions of fuzzy logic based admission control algorithms are presented there. All algorithms are mutually compared via simulations. Simulations show that the novel advanced fuzzy algorithm outperforms the other simulated algorithms (in terms of blocking probability, dropping probability and the number of active UEs in cell.
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic.
Li, Ning; Martínez, José-Fernán; Hernández Díaz, Vicente
2015-01-01
Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters' dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively. PMID:26266412
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic.
Li, Ning; Martínez, José-Fernán; Hernández Díaz, Vicente
2015-08-10
Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters' dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively.
Stock and option portfolio using fuzzy logic approach
Sumarti, Novriana; Wahyudi, Nanang
2014-03-01
Fuzzy Logic in decision-making process has been widely implemented in various problems in industries. It is the theory of imprecision and uncertainty that was not based on probability theory. Fuzzy Logic adds values of degree between absolute true and absolute false. It starts with and builds on a set of human language rules supplied by the user. The fuzzy systems convert these rules to their mathematical equivalents. This could simplify the job of the system designer and the computer, and results in much more accurate representations of the way systems behave in the real world. In this paper we examine the decision making process of stock and option trading by the usage of MACD (Moving Average Convergence Divergence) technical analysis and Option Pricing with Fuzzy Logic approach. MACD technical analysis is for the prediction of the trends of underlying stock prices, such as bearish (going downward), bullish (going upward), and sideways. By using Fuzzy C-Means technique and Mamdani Fuzzy Inference System, we define the decision output where the value of MACD is high then decision is "Strong Sell", and the value of MACD is Low then the decision is "Strong Buy". We also implement the fuzzification of the Black-Scholes option-pricing formula. The stock and options methods are implemented on a portfolio of one stock and its options. Even though the values of input data, such as interest rates, stock price and its volatility, cannot be obtain accurately, these fuzzy methods can give a belief degree of the calculated the Black-Scholes formula so we can make the decision on option trading. The results show the good capability of the methods in the prediction of stock price trends. The performance of the simulated portfolio for a particular period of time also shows good return.
Modelling of Reservoir Operations using Fuzzy Logic and ANNs
Van De Giesen, N.; Coerver, B.; Rutten, M.
2015-12-01
Today, almost 40.000 large reservoirs, containing approximately 6.000 km3 of water and inundating an area of almost 400.000 km2, can be found on earth. Since these reservoirs have a storage capacity of almost one-sixth of the global annual river discharge they have a large impact on the timing, volume and peaks of river discharges. Global Hydrological Models (GHM) are thus significantly influenced by these anthropogenic changes in river flows. We developed a parametrically parsimonious method to extract operational rules based on historical reservoir storage and inflow time-series. Managing a reservoir is an imprecise and vague undertaking. Operators always face uncertainties about inflows, evaporation, seepage losses and various water demands to be met. They often base their decisions on experience and on available information, like reservoir storage and the previous periods inflow. We modeled this decision-making process through a combination of fuzzy logic and artificial neural networks in an Adaptive-Network-based Fuzzy Inference System (ANFIS). In a sensitivity analysis, we compared results for reservoirs in Vietnam, Central Asia and the USA. ANFIS can indeed capture reservoirs operations adequately when fed with a historical monthly time-series of inflows and storage. It was shown that using ANFIS, operational rules of existing reservoirs can be derived without much prior knowledge about the reservoirs. Their validity was tested by comparing actual and simulated releases with each other. For the eleven reservoirs modelled, the normalised outflow, , was predicted with a MSE of 0.002 to 0.044. The rules can be incorporated into GHMs. After a network for a specific reservoir has been trained, the inflow calculated by the hydrological model can be combined with the release and initial storage to calculate the storage for the next time-step using a mass balance. Subsequently, the release can be predicted one time-step ahead using the inflow and storage.
On enhancing on-line collaboration using fuzzy logic modeling
Directory of Open Access Journals (Sweden)
Leontios J. Hadjileontiadis
2004-04-01
Full Text Available Web-based collaboration calls for professional skills and competences to the benefit of the quality of the collaboration and its output. Within this framework, educational virtual environments may provide a means for training upon these skills and in particular the collaborative ones. On the basis of the existing technological means such training may be enhanced even more. Designing considerations towards this direction include the close follow-up of the collaborative activity and provision of support grounded upon a pedagogical background. To this vein, a fuzzy logic-based expert system, namely Collaboration/Reflection-Fuzzy Inference System (C/R-FIS, is presented in this paper. By means of interconnected FISs, the C/R-FIS expert system automatically evaluates the collaborative activity of two peers, during their asynchronous, written, web-based collaboration. This information is used for the provision of adaptive support to peers during their collaboration, towards equilibrium of their collaborative activity. In particular, this enhanced formative feedback aims at diminishing the possible dissonance between the individual collaborative skills by challenging self-adjustment procedures. The proposed model extents the evaluation system of a web-based collaborative tool namely Lin2k, which has served as a test-bed for the C/R-FIS experimental use. Results from its experimental use have proved the potentiality of the proposed model to significantly contribute to the enhancement of the collaborative activity and its transferability to other collaborative learning contexts, such as medicine, environmental engineering, law, and music education.
Fuzzy Logic Water Quality Index and Importance of Water Quality Parameters
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Raman Bai. V
2009-01-01
Full Text Available Determination of status of water quality of a river or any other water sources is highly indeterminate. It is necessary to have a competent model to predict the status of water quality and to advice for type of water treatment for meeting different demands. One such model (UNIQ2007 is developed as an application software in water quality engineering. The unit operates in a fuzzy logic mode including a fuzzification engine receiving a plurality of input variables on its input and being adapted to compute membership function parameters. A processor engine connected downstream of the fuzzification unit will produce fuzzy set, based on fuzzy variable viz. DO, BOD, COD, AN, SS and pH. It has a defuzzification unit operative to translate the inference results into a discrete crisp value of WQI. The UNIQ2007 contains a first memory device connected to the fuzzification unit and containing the set of membership functions, a secondary memory device connected to the defuzzification unit and containing the set of crisp value which appear in the THEN part of the fuzzy rules and an additional memory device connected to the defuzzification unit. More advantageously, UINQ2007 is constructed with control elements having dynamic fuzzy logic properties wherein target non-linearity can be input to result in a perfect evaluation of water quality. The development of the fuzzy model with one river system is explained in this paper. Further the model has been evaluated with the data from few rivers in Malaysia, India and Thailand. This water quality assessor probe can provide better quality index or identify the status of river with 90% perfection. Presently, WQI in most of the countries is referring to physic-chemical parameters only due to great efforts needed to quantify the biological parameters. This study ensures a better method to include pathogens into WQI due to superior capabilities of fuzzy logic in dealing with non-linear, complex and uncertain systems.
Indirect adaptive fuzzy control for a class of nonlinear discrete-time systems
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
An indirect adaptive fuzzy control scheme is developed for a class of nonlinear discrete-time systems. In this method, two fuzzy logic systems are used to approximate the unknown functions, and the parameters of membership functions in fuzzy logic systems are adjusted according to adaptive laws for the purpose of controlling the plant to track a reference trajectory. It is proved that the scheme can not only guarantee the boundedness of the input and output of the closed-loop system, but also make the tracking error converge to a small neighborhood of the origin. Simulation results indicate the effectiveness of this scheme.
Fuzzy Logic Decoupled Lateral Control for General Aviation Airplanes
Duerksen, Noel
1997-01-01
It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control different airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control aileron or roll spoiler position. This controller was used to control bank angle for both a piston powered single engine aileron equipped airplane simulation and a business jet simulation which used spoilers for primary roll control. Overspeed, stall and overbank protection were incorporated in the form of expert systems supervisors and weighted fuzzy rules. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic lateral controller could be successfully used on two general aviation aircraft types that have very different characteristics. These controllers worked for both airplanes over their entire flight envelopes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle ]ever travel, etc.). This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.
Fuzzy logic based variable speed wind generation system
Energy Technology Data Exchange (ETDEWEB)
Simoes, M.G. [Sao Paulo Univ., SP (Brazil). Escola Politecnica. PMC - Mecatronica; Bose, B.K. [Tennessee Univ., Knoxville, TN (United States). Dept. of Electrical Engineering; Spiegel, Ronal J. [Environmental Protection Agency, Research Triangle Park, NC (United States). Air and Energy Engineering Research Lab.
1996-12-31
This work demonstrates the successful application of fuzzy logic to enhance the performance and control of a variable speed wind generation system. A maximum power point tracker control is performed with three fuzzy controllers, without wind velocity measurement, and robust to wind vortex and turbine torque ripple. A squirrel cage induction generator feeds the power to a double-sided PWM converter system which pumps the power to a utility grid or supplies to an autonomous system. The fuzzy logic controller FLC-1 searches on-line the generator speed so that the aerodynamic efficiency of the wind turbine is optimized. A second fuzzy controller FLC-2 programs the machine flux by on-line search so as to optimize the machine-converter system wind vortex. Detailed analysis and simulation studies were performed for development of the control strategy and fuzzy algorithms, and a DSP TMS320C30 based hardware with C control software was built for the performance evaluation of a laboratory experimental set-up. The theoretical development was fully validated and the system is ready to be reproduced in a higher power installation. (author) 7 refs., 3 figs., 1 tab.
Fuzzy Logic Water Quality Index and Importance of Water Quality Parameters
Raman Bai. V; Reinier Bouwmeester; Mohan S
2009-01-01
Determination of status of water quality of a river or any other water sources is highly indeterminate. It is necessary to have a competent model to predict the status of water quality and to advice for type of water treatment for meeting different demands. One such model (UNIQ2007) is developed as an application software in water quality engineering. The unit operates in a fuzzy logic mode including a fuzzification engine receiving a plurality of input variables on its input and being adapte...
Fuzzy Logic Based Power System Contingency Ranking
Directory of Open Access Journals (Sweden)
A. Y. Abdelaziz
2013-02-01
Full Text Available Voltage stability is a major concern in planning and operations of power systems. It is well known that voltage instability and collapse have led to major system failures. Modern transmission networks are more heavily loaded than ever before to meet the growing demand. One of the major consequences resulted from such a stressed system is voltage collapse or instability. This paper presents maximum loadability identification of a load bus in a power transmission network. In this study, Fast Voltage Stability Index (FVSI is utilized as the indicator of the maximum loadability termed as Qmax. In this technique, reactive power loading will be increased gradually at particular load bus until the FVSI reaches close to unity. Therefore, a critical value of FVSI was set as the maximum loadability point. This value ensures the system from entering voltage-collapse region. The main purpose in the maximum loadability assessment is to plan for the maximum allowable load value to avoid voltage collapse; which is important in power system planning risk assessment.The most important task in security analysis is the problem of identifying the critical contingencies from a large list of credible contingencies and ranks them according to their severity. The condition of voltage stability in a power system can be characterized by the use of voltage stability indices. This paper presents fuzzy approach for ranking the contingencies using composite-index based on parallel operated fuzzy inference engine. The Line Flow index (L.F and bus Voltage Magnitude (VM of the load buses are expressed in fuzzy set notation. Further, they are evaluated using Fuzzy rules to obtain overall Criticality Index. Contingencies are ranked based on decreasing order of Criticality Index and then provides the comparison of ranking obtained with FVSI method.
Fuzzy logic estimator of rotor time constant in induction motors
Energy Technology Data Exchange (ETDEWEB)
Alminoja, J. [Tampere University of Technology (Finland). Control Engineering Laboratory; Koivo, H. [Helsinki University of Technology, Otaniemi (Finland). Control Engineering Laboratory
1997-12-31
Vector control of AC machines is a well-known and widely used technique in induction machine control. It offers an exact method for speed control of induction motors, but it is also sensitive to the changes in machine parameters. E.g. rotor time constant has a strong dependence on temperature. In this paper a fuzzy logic estimator is developed, with which the rotor time constant can be estimated when the machine has a load. It is more simple than the estimators proposed in the literature. The fuzzy estimator is tested by simulation when step-wise abrupt changes and slow drifting occurs. (orig.) 7 refs.
LA LÓGICA DIFUSA COMPENSATORIA / THE COMPENSATORY FUZZY LOGIC
Directory of Open Access Journals (Sweden)
Jesús Cejas-Montero
2011-06-01
Full Text Available
La Lógica Difusa Compensatoria es un modelo lógico que permite la modelación simultánea de los procesos deductivos y de toma de decisiones. Sus características más importantes son: la flexibilidad, la tolerancia con la imprecisión, la capacidad para moldear problemas no-lineales y su fundamento en el lenguaje de sentido común. El artículo pretende llevar a la comunidad académico-empresarial las ideas fundamentales de la Lógica Difusa Compensatoria, ilustrándola en sus posibles campos de aplicación para lograr la competitividad de una organización.
Abstract
The Compensatory Fuzzy Logic is a logical model that allows the simultaneous modeling of the deductive and decision-making processes. The most important characteristics of Compensatory Fuzzy Logic are: the flexibility, the tolerance with the inaccuracy, the capacity to model no-lineal problems and its foundation in the language of common sense. The article seeks to bring the basic ideas of the Compensatory Fuzzy Logic to the academic–managerial community, illustrating it in its possible fields of application, in order to achieve the competitiveness of an organization.
Content-addressable-memory for the three key operations of fuzzy logic
Jiang, Tao; Li, Yao
1999-03-01
Today, most fuzzy logic operations are performed via software means, which is inevitably slow. While searching for long term hardware solutions to realize analog fuzzy logic operations, the use of the well-developed Boolean logic hardware with analog to digital and digital to analog converters to implement the digitized fuzzy logic could provide an efficient solution. Similar to Boolean logic, digitized fuzzy logic operations can be written as a minimized sum-of-product term format, which can then be implemented based on programmable logic arrays. We address a fundamental issue of the computational complexity of this method. We derive the minimum number of the Boolean sum-of-product terms for some key fuzzy logic operations, such as Union, Intersection, and Complement operators. Our derivations provide ways to estimate the general computational complexity or memory capacity of using binary circuits, electronic or optoelectronic, to implement the digitized analog logic operations.
Fuzzy Logic Expert System-A Prescriptive Approach
Directory of Open Access Journals (Sweden)
Wahid palash
2015-08-01
Full Text Available A membership value of a fuzzy set has been defined as the degree to which an element belongs to this fuzzy set. It is possible to give other interpretations to the membership degree like a certainty factor, a degree of truth, a degree of satisfaction and a degree of possibility. In 1978 Zadeh extended the fuzzy set theory to a possibility theory where the membership values are considered as degrees of possibility. Zadeh justies the possibility theory by the fact that the imprecision that is intrinsic in natural languages is, in the main, possibility rather than probabilistic in nature. In contrast to the statistical perspective of the information which is involved in the coding, the transmission and the reception of the data, the theory of possibility focuses on the meaning of the information. One of the reasons the scientific community took an interest in the fuzzy logic theory is the financial success of fuzzy control in home appliances in the Japanese industry. In 1990, the consumer products market using fuzzy controllers was estimated to 2 billion dollars. Interestingly enough L. A. Zadeh is a major contributor of the modern control theory. The control theory is a very precise and strict approach in order to model systems or phenomena.
Answer Set Programming for Continuous Domains A Fuzzy Logic Approach
Janssen, Jeroen; Vermeir, Dirk
2012-01-01
"Answer set programming (ASP)" is a declarative language tailored towards solving combinatorial optimization problems. It has been successfully applied to e.g. planning problems, configuration and verification of software, diagnosis and database repairs. However, ASP is not directly suitable for modeling problems with continuous domains. Such problems occur naturally in diverse fields such as the design of gas and electricity networks, computer vision and investment portfolios. To overcome this problem we study FASP, a combination of ASP with fuzzy logic - a class of manyvalued logic
Fuzzy Logic Supervised Teleoperation Control for Mobile Robot
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
The supervised teleoperation control is presented for a mobile robot to implement the tasks by using fuzzy logic. The teleoperation control system includes joystick based user interaction mechanism, the high level instruction set and fuzzy logic behaviors integrated in a supervised autonomy teleoperation control system for indoor navigation. These behaviors include left wall following, right wall following, turn left, turn right, left obstacle avoidance, right obstacle avoidance and corridor following based on ultrasonic range finders data. The robot compares the instructive high level command from the operator and relays back a suggestive signal back to the operator in case of mismatch between environment and instructive command. This strategy relieves the operator's cognitive burden, handle unforeseen situations and uncertainties of environment autonomously. The effectiveness of the proposed method for navigation in an unstructured environment is verified by experiments conducted on a mobile robot equipped with only ultrasonic range finders for environment sensing.
CPU and memory allocation optimization using fuzzy logic
Zalevsky, Zeev; Gur, Eran; Mendlovic, David
2002-12-01
The allocation of CPU time and memory resources, are well known problems in organizations with a large number of users, and a single mainframe. Usually the amount of resources given to a single user is based on its own statistics, not on the entire statistics of the organization therefore patterns are not well identified and the allocation system is prodigal. In this work the authors suggest a fuzzy logic based algorithm to optimize the CPU and memory distribution between the users based on the history of the users. The algorithm works separately on heavy users and light users since they have different patterns to be observed. The result is a set of rules, generated by the fuzzy logic inference engine that will allow the system to use its computing ability in an optimized manner. Test results on data taken from the Faculty of Engineering in Tel Aviv University, demonstrate the abilities of the new algorithm.
Fuzzy-logic optical optimization of mainframe CPU and memory
Zalevsky, Zeev; Gur, Eran; Mendlovic, David
2006-07-01
The allocation of CPU time and memory resources is a familiar problem in organizations with a large number of users and a single mainframe. Usually the amount of resources allocated to a single user is based on the user's own statistics not on the statistics of the entire organization, therefore patterns are not well identified and the allocation system is prodigal. A fuzzy-logic-based algorithm to optimize the CPU and memory distribution among users based on their history is suggested. The algorithm works on heavy and light users separately since they present different patterns to be observed. The result is a set of rules generated by the fuzzy-logic inference engine that will allow the system to use its computing ability in an optimized manner. Test results on data taken from the Faculty of Engineering of Tel Aviv University demonstrate the capabilities of the new algorithm.
Fuzzy Logic Control for Suspension Systems of Tracked Vehicles
Institute of Scientific and Technical Information of China (English)
YU Yang; WEI Xue-xia; ZHANG Yong-fa
2009-01-01
A scheme of fuzzy logic control for the suspension system of a tracked vehicle is presented.A mechanical model for the whole body of a tracked vehicle,which is totally a fifteen-degree-of-freedom system,is established.The model includes the vertical motion,the pitch motion as well as the roll motion of the tracked vehicle.In contrast to most previous studies,the coupling effect among the vertical,the pitch and the roll motions of the suspension system of a tracked vehicle is considered simultaneously.The simulation of fuzzy logic control under road surface with random excitation shows that the acceleration,pitch angle and roll angle of suspension system can be efficiently controlled.
Control of a flexible beam using fuzzy logic
Mccullough, Claire L.
1991-01-01
The goal of this project, funded under the NASA Summer Faculty Fellowship program, was to evaluate control methods utilizing fuzzy logic for applicability to control of flexible structures. This was done by applying these methods to control of the Control Structures Interaction Suitcase Demonstrator developed at Marshall Space Flight Center. The CSI Suitcase Demonstrator is a flexible beam, mounted at one end with springs and bearing, and with a single actuator capable of rotating the beam about a pin at the fixed end. The control objective is to return the tip of the free end to a zero error position (from a nonzero initial condition). It is neither completely controllable nor completely observable. Fuzzy logic control was demonstrated to successfully control the system and to exhibit desirable robustness properties compared to conventional control.
Fuzzy temporal logic based railway passenger flow forecast model.
Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun
2014-01-01
Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models.
STATOR FLUX OPTIMIZATION ON DIRECT TORQUE CONTROL WITH FUZZY LOGIC
Directory of Open Access Journals (Sweden)
Fatih Korkmaz
2012-07-01
Full Text Available The Direct Torque Control (DTC is well known as an effective control technique for high performance drives in a wide variety of industrial applications and conventional DTC technique uses two constant reference value: torque and stator flux. In this paper, fuzzy logic based stator flux optimization technique for DTC drives that has been proposed. The proposed fuzzy logic based stator flux optimizer self-regulates the stator flux reference using induction motor load situation without need of any motor parameters. Simulation studies have been carried out with Matlab/Simulink to compare the proposed system behaviors at vary load conditions. Simulation results show that the performance of the proposed DTC technique has been improved and especially at low-load conditions torque ripple are greatly reduced with respect to the conventional DTC.
8-Valent Fuzzy Logic for Iris Recognition and Biometry
Popescu-Bodorin, N; Motoc, I M; 10.1109/ISCIII.2011.6069761
2011-01-01
This paper shows that maintaining logical consistency of an iris recognition system is a matter of finding a suitable partitioning of the input space in enrollable and unenrollable pairs by negotiating the user comfort and the safety of the biometric system. In other words, consistent enrollment is mandatory in order to preserve system consistency. A fuzzy 3-valued disambiguated model of iris recognition is proposed and analyzed in terms of completeness, consistency, user comfort and biometric safety. It is also shown here that the fuzzy 3-valued model of iris recognition is hosted by an 8-valued Boolean algebra of modulo 8 integers that represents the computational formalization in which a biometric system (a software agent) can achieve the artificial understanding of iris recognition in a logically consistent manner.
Rule based fuzzy logic approach for classification of fibromyalgia syndrome.
Arslan, Evren; Yildiz, Sedat; Albayrak, Yalcin; Koklukaya, Etem
2016-06-01
Fibromyalgia syndrome (FMS) is a chronic muscle and skeletal system disease observed generally in women, manifesting itself with a widespread pain and impairing the individual's quality of life. FMS diagnosis is made based on the American College of Rheumatology (ACR) criteria. However, recently the employability and sufficiency of ACR criteria are under debate. In this context, several evaluation methods, including clinical evaluation methods were proposed by researchers. Accordingly, ACR had to update their criteria announced back in 1990, 2010 and 2011. Proposed rule based fuzzy logic method aims to evaluate FMS at a different angle as well. This method contains a rule base derived from the 1990 ACR criteria and the individual experiences of specialists. The study was conducted using the data collected from 60 inpatient and 30 healthy volunteers. Several tests and physical examination were administered to the participants. The fuzzy logic rule base was structured using the parameters of tender point count, chronic widespread pain period, pain severity, fatigue severity and sleep disturbance level, which were deemed important in FMS diagnosis. It has been observed that generally fuzzy predictor was 95.56 % consistent with at least of the specialists, who are not a creator of the fuzzy rule base. Thus, in diagnosis classification where the severity of FMS was classified as well, consistent findings were obtained from the comparison of interpretations and experiences of specialists and the fuzzy logic approach. The study proposes a rule base, which could eliminate the shortcomings of 1990 ACR criteria during the FMS evaluation process. Furthermore, the proposed method presents a classification on the severity of the disease, which was not available with the ACR criteria. The study was not limited to only disease classification but at the same time the probability of occurrence and severity was classified. In addition, those who were not suffering from FMS were
Rule based fuzzy logic approach for classification of fibromyalgia syndrome.
Arslan, Evren; Yildiz, Sedat; Albayrak, Yalcin; Koklukaya, Etem
2016-06-01
Fibromyalgia syndrome (FMS) is a chronic muscle and skeletal system disease observed generally in women, manifesting itself with a widespread pain and impairing the individual's quality of life. FMS diagnosis is made based on the American College of Rheumatology (ACR) criteria. However, recently the employability and sufficiency of ACR criteria are under debate. In this context, several evaluation methods, including clinical evaluation methods were proposed by researchers. Accordingly, ACR had to update their criteria announced back in 1990, 2010 and 2011. Proposed rule based fuzzy logic method aims to evaluate FMS at a different angle as well. This method contains a rule base derived from the 1990 ACR criteria and the individual experiences of specialists. The study was conducted using the data collected from 60 inpatient and 30 healthy volunteers. Several tests and physical examination were administered to the participants. The fuzzy logic rule base was structured using the parameters of tender point count, chronic widespread pain period, pain severity, fatigue severity and sleep disturbance level, which were deemed important in FMS diagnosis. It has been observed that generally fuzzy predictor was 95.56 % consistent with at least of the specialists, who are not a creator of the fuzzy rule base. Thus, in diagnosis classification where the severity of FMS was classified as well, consistent findings were obtained from the comparison of interpretations and experiences of specialists and the fuzzy logic approach. The study proposes a rule base, which could eliminate the shortcomings of 1990 ACR criteria during the FMS evaluation process. Furthermore, the proposed method presents a classification on the severity of the disease, which was not available with the ACR criteria. The study was not limited to only disease classification but at the same time the probability of occurrence and severity was classified. In addition, those who were not suffering from FMS were
Fuzzy logic in fire control systems for air defence
Gelev, Saso; Gacovski, Zoran; Jiea-he, Xu; Yuan-wei, Jing; Deskovski, Stojce
2007-01-01
It is the necessity defense combat against modern offensive weapons from the air to apply the best and most efficient defense tactics and technology. The problems of shooting targets in air space are solved by appropriate design of a fire control system, and the latest developments employ computational intelligence models and techniques. In this paper, a fuzzy-logic knowledge-base system in the fire control system for missile based air defense has been investigated. The aim of this paper is t...
Fuzzy Logic and Mechanical Ventilation of COPD Patients
Hatzakis, George; Olivenstein, Ronald; Bates, Jason H. T.
2001-01-01
Weaning from mechanical ventilation typically follows a course determined by the experience of the attending physician. However, despite the currently subjective nature of the weaning process and the many factors involved in its success, there is a wide consensus that this procedure could be automated somehow. We have developed a fuzzy logic based controller of pressure support mechanical ventilation (AJRCCM, 1999 Aug 160:2 550-6) and are now evaluating its performance in a prospective trial ...
Implement Fuzzy Logic to Optimize Electronic Business Success
Fahim Akhter
2016-01-01
Customers are realizing the importance and benefits of shopping online such as convenience, comparison, product research, larger selection, and lower prices. The dynamic nature of e-commerce evokes online businesses to make alterations in their business processes and decisions making to satisfy customers’ needs. Online businesses are adopting Business Intelligence (BI) tools and systems with the collaboration of fuzzy logic system to forecast the future of the e-commerce. With the aid of BI, ...
Switch Reluctance Motor Control Based on Fuzzy Logic System
Directory of Open Access Journals (Sweden)
S. Aleksandrovsky
2012-01-01
Full Text Available Due to its intrinsic simplicity and reliability, the switched reluctance motor (SRM has now become a promising candidate for variable-speed drive applications as an alternative induction motor in various industrial application. However, the SRM has the disadvantage of nonlinear characteristic and control. It is suggested to use controller based on fuzzy logic system. Design of FLS controller and simulation model presented.
Hybrid fuzzy/crisp-logic control of manufacturing systems
Porter, B; Moi, H.
1996-01-01
In recent years, techniques such as dynamic programming, the maximum principle, linear programming, and genetic algorithms have been used to synthesise optimal control policies for manufacturing systems. However, such techniques are frequently rather opaque and often yield control policies that are implemented by open-loop rather than closed-loop control systems. In this paper, it is therefore shown that closed-loop systems incorporating hybrid fuzzy/crisp-logic controllers can be readily des...
Optimization of heat pump using fuzzy logic and genetic algorithm
Energy Technology Data Exchange (ETDEWEB)
Sahin, Arzu Sencan [Sueleyman Demirel University, Technology Faculty, Isparta (Turkey); Kilic, Bayram; Kilic, Ulas [Bucak Emin Guelmez Vocational School, Mehmet Akif Ersoy University, Bucak (Turkey)
2011-12-15
Heat pumps offer economical alternatives of recovering heat from different sources for use in various industrial, commercial and residential applications. In this study, single-stage air-source vapor compression heat pump system has been optimized using genetic algorithm (GA) and fuzzy logic (FL). The necessary thermodynamic properties for optimization were calculated by FL. Thermodynamic properties obtained with FL were compared with actual results. Then, the optimum working conditions of heat pump system were determined by the GA. (orig.)
Fuzzy logic controllers and chaotic natural convection loops
International Nuclear Information System (INIS)
The study of natural circulation loops is a subject of special concern for the engineering design of advanced nuclear reactors, as natural convection provides an efficient and completely passive heat removal system. However, under certain circumstances thermal-fluid-dynamical instabilities may appear, threatening the reactor safety as a whole.On the other hand, fuzzy logic controllers provide an ideal framework to approach highly non-linear control problems. In the present work, we develop a software-based fuzzy logic controller and study its application to chaotic natural convection loops.We numerically analyse the linguistic control of the loop known as the Welander problem in such conditions that, if the controller were not present, the circulation flow would be non-periodic unstable.We also design a Taka gi-Sugeno fuzzy controller based on a fuzzy model of a natural convection loop with a toroidal geometry, in order to stabilize a Lorenz-chaotic behaviour.Finally, we show experimental results obtained in a rectangular natural circulation loop
Spatially Adaptive Image Restoration Using Fuzzy Punctual Kriging
Institute of Scientific and Technical Information of China (English)
Anwar M. Mirza; Asmatullah Chaudhry; Badre Munir
2007-01-01
We present a general formulation based on punctual kriging and fuzzy concepts for image restoration in spatial domain. Gray-level images degraded with Gaussian white noise have been considered. Based on the pixel local neighborhood, fuzzy logic has been employed intelligently to avoid unnecessary estimation of a pixel. The intensity estimation of the selected pixels is then carried out by employing punctual kriging in conjunction with the method of Lagrange multipliers and estimates of local semi-variances. Application of such a hybrid technique performing both selection and intensity estimation of a pixel demonstrates substantial improvement in the image quality as compared to the adaptive Wiener filter and existing fuzzy- kriging approaches. It has been found that these filters achieve noise reduction without loss of structural detail information, as indicated by their higher structure similarity indices, peak signal to noise ratios and the new variogram based quality measures.
Implement Fuzzy Logic to Optimize Electronic Business Success
Directory of Open Access Journals (Sweden)
Fahim Akhter
2016-03-01
Full Text Available Customers are realizing the importance and benefits of shopping online such as convenience, comparison, product research, larger selection, and lower prices. The dynamic nature of e-commerce evokes online businesses to make alterations in their business processes and decisions making to satisfy customers’ needs. Online businesses are adopting Business Intelligence (BI tools and systems with the collaboration of fuzzy logic system to forecast the future of the e-commerce. With the aid of BI, businesses have more possibilities to choose types and structures of required information to serve customers. The fuzzy logic system and BI capabilities would allow both customers and vendors to make right decisions about online shopping. Many experts believe that trust and security are critical risk factors for the embracement of e-commerce. Online trust may be influenced by factors such as usability, familiarity and conducting business with unknown parties. This paper discusses fuzzy logic and BI approach to gauge the level of trust and security in online transactions. The paper further addresses the issues and concerns related to the equilibrium of trust, security, and usability in online shopping.
Fuzzy Logic Decoupled Longitudinal Control for General Aviation Airplanes
Duerksen, Noel
1996-01-01
It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control difference airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control throttle position and another to control elevator position. These two controllers were used to control flight path angle and airspeed for both a piston powered single engine airplane simulation and a business jet simulation. Overspeed protection and stall protection were incorporated in the form of expert systems supervisors. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic longitudinal controller could be successfully used on two general aviation aircraft types that have very difference characteristics. These controllers worked for both airplanes over their entire flight envelopes including configuration changes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle lever travel, etc.). The controllers also handled configuration changes without mode switching or knowledge of the current configuration. This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.
Electric Drive Control with Rotor Resistance and Rotor Speed Observers Based on Fuzzy Logic
Directory of Open Access Journals (Sweden)
C. Ben Regaya
2014-01-01
Full Text Available Many scientific researchers have proposed the control of the induction motor without speed sensor. These methods have the disadvantage that the variation of the rotor resistance causes an error of estimating the motor speed. Thus, simultaneous estimation of the rotor resistance and the motor speed is required. In this paper, a scheme for estimating simultaneously the rotor resistance and the rotor speed of an induction motor using fuzzy logic has been developed. We present a method which is based on two adaptive observers using fuzzy logic without affecting each other and a simple algorithm in order to facilitate the determination of the optimal values of the controller gains. The control algorithm is proved by the simulation tests. The results analysis shows the characteristic robustness of the two observers of the proposed method even in the case of variation of the rotor resistance.
A fuzzy logic intelligent diagnostic system for spacecraft integrated vehicle health management
Wu, G. Gordon
1995-01-01
Due to the complexity of future space missions and the large amount of data involved, greater autonomy in data processing is demanded for mission operations, training, and vehicle health management. In this paper, we develop a fuzzy logic intelligent diagnostic system to perform data reduction, data analysis, and fault diagnosis for spacecraft vehicle health management applications. The diagnostic system contains a data filter and an inference engine. The data filter is designed to intelligently select only the necessary data for analysis, while the inference engine is designed for failure detection, warning, and decision on corrective actions using fuzzy logic synthesis. Due to its adaptive nature and on-line learning ability, the diagnostic system is capable of dealing with environmental noise, uncertainties, conflict information, and sensor faults.
MANAGE OF CONCRETE BRIDGES’ ELEMENTS ON THE BASIS OF FUZZY LOGIC MODELS
Directory of Open Access Journals (Sweden)
L. P. Bodnar
2010-03-01
Full Text Available The refinement of estimation of operational state of the ferro-concrete bridge elements and the direction of substantiation of the bridge service levels on the basis of fuzzy set theory and fuzzy logic are offered.
Searching arousals: A fuzzy logic approach.
Chaparro-Vargas, Ramiro; Ahmed, Beena; Penzel, Thomas; Cvetkovic, Dean
2015-08-01
This paper presents a computational approach to detect spontaneous, chin tension and limb movement-related arousals by estimating neuronal and muscular activity. Features extraction is carried out by Time Varying Autoregressive Moving Average (TVARMA) models and recursive particle filtering. Classification is performed by a fuzzy inference system with rule-based decision scheme based upon the AASM scoring rules. Our approach yielded two metrics: arousal density and arousal index to comply with standardised clinical benchmarking. The obtained statistics achieved error deviation around ±1.5 to ±30. These results showed that our system can differentiate amongst 3 different types of arousals, subject to inter-subject variability and up-to-date scoring references. PMID:26736862
Distributed traffic signal control using fuzzy logic
Chiu, Stephen
1992-01-01
We present a distributed approach to traffic signal control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. Thus, the signal timing parameters evolve dynamically using only local information to improve traffic flow. This distributed approach provides for a fault-tolerant, highly responsive traffic management system. The signal timing at an intersection is defined by three parameters: cycle time, phase split, and offset. We use fuzzy decision rules to adjust these three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. We show the effectiveness of this method through simulation of the traffic flow in a network of controlled intersections.
Model Reduction of Fuzzy Logic Systems
Directory of Open Access Journals (Sweden)
Zhandong Yu
2014-01-01
Full Text Available This paper deals with the problem of ℒ2-ℒ∞ model reduction for continuous-time nonlinear uncertain systems. The approach of the construction of a reduced-order model is presented for high-order nonlinear uncertain systems described by the T-S fuzzy systems, which not only approximates the original high-order system well with an ℒ2-ℒ∞ error performance level γ but also translates it into a linear lower-dimensional system. Then, the model approximation is converted into a convex optimization problem by using a linearization procedure. Finally, a numerical example is presented to show the effectiveness of the proposed method.
Fuzzy Logic: A New Tool for the Analysis and Organization of International Business Communications.
Sondak, Norman E.; Sondak, Eileen M.
Classical western logic, built on a foundation of true/false, yes/no, right/wrong statements, leads to many difficulties and inconsistencies in the logical analysis and organization of international business communications. This paper presents the basic principles of classical logic and of fuzzy logic, a type of logic developed to allow for…
Maximizing Strength of Digital Watermarks Using Fuzzy Logic
Directory of Open Access Journals (Sweden)
Sameh Oueslati
2011-02-01
Full Text Available In this paper, we propose a novel digital watermarking scheme in DCT domain based fuzzy inferencesystem and the human visual system to adapt the embedding strength of different blocks. Firstly, theoriginal image is divided into some 8×8 blocks, and then fuzzy inference system according to differenttextural features and luminance of each block decide adaptively different embedding strengths. Thewatermark detection adopts correlation technology. Experimental results show that the proposed schemehas good imperceptibility and high robustness to common image processing operators.
Maximizing Strength of Digital Watermarks using Fuzzy Logic
Oueslati, Sameh; Solaiman, Bassel
2011-01-01
In this paper, we propose a novel digital watermarking scheme in DCT domain based fuzzy inference system and the human visual system to adapt the embedding strength of different blocks. Firstly, the original image is divided into some 8 \\times 8 blocks, and then fuzzy inference system according to different textural features and luminance of each block decide adaptively different embedding strengths. The watermark detection adopts correlation technology. Experimental results show that the proposed scheme has good imperceptibility and high robustness to common image processing operators.
Computational intelligence synergies of fuzzy logic, neural networks and evolutionary computing
Siddique, Nazmul
2013-01-01
Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspect
The research on high speed underwater target recognition based on fuzzy logic inference
Institute of Scientific and Technical Information of China (English)
JIANG Xiang-Dong; YANG De-Sen; SHI Sheng-guo; LI Si-Chun
2006-01-01
The underwater target recognition is a key technology in acoustic confrontation and underwater defence. In this article, a recognition system based on fuzzy logic inference (FLI) is set up. This system is mainly composed of three parts: the fuzzy input module, the fuzzy logic inference module with a set of inference rules and the de-fuzzy output module. The inference result shows the recognition system is effective in most conditions.
Noise-based logic: Binary, multi-valued, or fuzzy, with optional superposition of logic states
International Nuclear Information System (INIS)
A new type of deterministic (non-probabilistic) computer logic system inspired by the stochasticity of brain signals is shown. The distinct values are represented by independent stochastic processes: independent voltage (or current) noises. The orthogonality of these processes provides a natural way to construct binary or multi-valued logic circuitry with arbitrary number N of logic values by using analog circuitry. Moreover, the logic values on a single wire can be made a (weighted) superposition of the N distinct logic values. Fuzzy logic is also naturally represented by a two-component superposition within the binary case (N=2). Error propagation and accumulation are suppressed. Other relevant advantages are reduced energy dissipation and leakage current problems, and robustness against circuit noise and background noises such as 1/f, Johnson, shot and crosstalk noise. Variability problems are also non-existent because the logic value is an AC signal. A similar logic system can be built with orthogonal sinusoidal signals (different frequency or orthogonal phase) however that has an extra 1/N type slowdown compared to the noise-based logic system with increasing number of N furthermore it is less robust against time delay effects than the noise-based counterpart
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic
Ning Li; José-Fernán Martínez; Vicente Hernández Díaz
2015-01-01
Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters’ dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer paramete...
Medical application of fuzzy logic: fuzzy patient state in arterial hypertension analysis
Blinowska, Aleksandra; Duckstein, Lucien
1993-12-01
A few existing applications of fuzzy logic in medicine are briefly described and some potential applications are reviewed. The problem of classification of patient states and medical decision making is discussed more in detail and illustrated by the example of a fuzzy rule based model developed to elicit, analyze and reproduce the opinions of multiple medical experts in the case of arterial hypertension. The goal was to reproduce the average coded answers using an adequate fuzzy procedure, here a fuzzy rule. State categories and the initial set of experimental parameters were defined according to medical practice. The fuzzy set membership functions were then assessed for each parameter in each category and a small subset of representative and pertinent parameters selected for each question. The data were split into two sets of 50 patient files each, the calibration set and the validation set. Two evaluation criteria were used: the sum of squared deviations and the sum of deviations. Fuzzy rules were then sought that reproduced the target, which was the average coded answer. Only one fuzzy rule `and' appeared to be necessary to describe the patient state in a continuous way and to approach the target as closely as the majority of experts.
Moini, A
2002-01-01
In this paper, genetic algorithms are used in the design and robustification various mo el-ba ed/non-model-based fuzzy-logic controllers for robotic manipulators. It is demonstrated that genetic algorithms provide effective means of designing the optimal set of fuzzy rules as well as the optimal domains of associated fuzzy sets in a new class of model-based-fuzzy-logic controllers. Furthermore, it is shown that genetic algorithms are very effective in the optimal design and robustification of non-model-based multivariable fuzzy-logic controllers for robotic manipulators.
Directory of Open Access Journals (Sweden)
Raj kumar
2012-08-01
Full Text Available This paper presents a self-tuning method of fuzzy logic controllers. The consequence part of the fuzzy logic controller is self-tuned through the Q-learning algorithm of reinforcement learning. The off policy temporal difference algorithm is used for tuning which directly approximate the action value function which gives the maximum reward. In this way, the Q-learning algorithm is used for the continuous time environment. The approach considered is having the advantage of fuzzy logic controller in a way that it is robust under the environmental uncertainties and no expert knowledge is required to design the rule base of the fuzzy logic controller.
A Temporal Fuzzy Logic Formalism for Knowledge Based Systems
Directory of Open Access Journals (Sweden)
Vasile MAZILESCU
2012-11-01
Full Text Available This paper shows that the influence of knowledge on new forms of work organisation can be described as mutual relationships. Different changes in work organisation also have a strong influence on the increasing importance of knowledge of different individual and collective actors in working situations. After that, we characterize a piece of basic formal system, an Extended Fuzzy Logic System (EFLS with temporal attributes, to conceptualize future DKMSs based on human imprecise for distributed just in time decisions. The approximate reasoning is perceived as a derivation of new formulas with the corresponding temporal attributes, within a fuzzy theory defined by the fuzzy set of special axioms. In a management application, the reasoning is evolutionary because of unexpected events which may change the state of the DKMS. In this kind of situations it is necessary to elaborate certain mechanisms in order to maintain the coherence of the obtained conclusions, to figure out their degree of reliability and the time domain for which these are true. These last aspects stand as possible further directions of development at a basic logic level for future technologies that must automate knowledge organizational processes.
Trigueros, José Antonio; Piñero, David P; Ismail, Mahmoud M
2016-01-01
AIM To define the financial and management conditions required to introduce a femtosecond laser system for cataract surgery in a clinic using a fuzzy logic approach. METHODS In the simulation performed in the current study, the costs associated to the acquisition and use of a commercially available femtosecond laser platform for cataract surgery (VICTUS, TECHNOLAS Perfect Vision GmbH, Bausch & Lomb, Munich, Germany) during a period of 5y were considered. A sensitivity analysis was performed considering such costs and the countable amortization of the system during this 5y period. Furthermore, a fuzzy logic analysis was used to obtain an estimation of the money income associated to each femtosecond laser-assisted cataract surgery (G). RESULTS According to the sensitivity analysis, the femtosecond laser system under evaluation can be profitable if 1400 cataract surgeries are performed per year and if each surgery can be invoiced more than $500. In contrast, the fuzzy logic analysis confirmed that the patient had to pay more per surgery, between $661.8 and $667.4 per surgery, without considering the cost of the intraocular lens (IOL). CONCLUSION A profitability of femtosecond laser systems for cataract surgery can be obtained after a detailed financial analysis, especially in those centers with large volumes of patients. The cost of the surgery for patients should be adapted to the real flow of patients with the ability of paying a reasonable range of cost. PMID:27500115
Fuzzy Logic-Based Guaranteed Lifetime Protocol for Real-Time Wireless Sensor Networks.
Shah, Babar; Iqbal, Farkhund; Abbas, Ali; Kim, Ki-Il
2015-08-18
Few techniques for guaranteeing a network lifetime have been proposed despite its great impact on network management. Moreover, since the existing schemes are mostly dependent on the combination of disparate parameters, they do not provide additional services, such as real-time communications and balanced energy consumption among sensor nodes; thus, the adaptability problems remain unresolved among nodes in wireless sensor networks (WSNs). To solve these problems, we propose a novel fuzzy logic model to provide real-time communication in a guaranteed WSN lifetime. The proposed fuzzy logic controller accepts the input descriptors energy, time and velocity to determine each node's role for the next duration and the next hop relay node for real-time packets. Through the simulation results, we verified that both the guaranteed network's lifetime and real-time delivery are efficiently ensured by the new fuzzy logic model. In more detail, the above-mentioned two performance metrics are improved up to 8%, as compared to our previous work, and 14% compared to existing schemes, respectively.
Fuzzy Logic Approaches to Multi-Objective Decision-Making in Aerospace Applications
Hardy, Terry L.
1994-01-01
Fuzzy logic allows for the quantitative representation of multi-objective decision-making problems which have vague or fuzzy objectives and parameters. As such, fuzzy logic approaches are well-suited to situations where alternatives must be assessed by using criteria that are subjective and of unequal importance. This paper presents an overview of fuzzy logic and provides sample applications from the aerospace industry. Applications include an evaluation of vendor proposals, an analysis of future space vehicle options, and the selection of a future space propulsion system. On the basis of the results provided in this study, fuzzy logic provides a unique perspective on the decision-making process, allowing the evaluator to assess the degree to which each option meets the evaluation criteria. Future decision-making should take full advantage of fuzzy logic methods to complement existing approaches in the selection of alternatives.
Platz, M.; Rapp, J.; Groessler, M.; Niehaus, E.; Babu, A.; Soman, B.
2014-11-01
A Spatial Decision Support System (SDSS) provides support for decision makers and should not be viewed as replacing human intelligence with machines. Therefore it is reasonable that decision makers are able to use a feature to analyze the provided spatial decision support in detail to crosscheck the digital support of the SDSS with their own expertise. Spatial decision support is based on risk and resource maps in a Geographic Information System (GIS) with relevant layers e.g. environmental, health and socio-economic data. Spatial fuzzy logic allows the representation of spatial properties with a value of truth in the range between 0 and 1. Decision makers can refer to the visualization of the spatial truth of single risk variables of a disease. Spatial fuzzy logic rules that support the allocation of limited resources according to risk can be evaluated with measure theory on topological spaces, which allows to visualize the applicability of this rules as well in a map. Our paper is based on the concept of a spatial fuzzy logic on topological spaces that contributes to the development of an adaptive Early Warning And Response System (EWARS) providing decision support for the current or future spatial distribution of a disease. It supports the decision maker in testing interventions based on available resources and apply risk mitigation strategies and provide guidance tailored to the geo-location of the user via mobile devices. The software component of the system would be based on open source software and the software developed during this project will also be in the open source domain, so that an open community can build on the results and tailor further work to regional or international requirements and constraints. A freely available EWARS Spatial Fuzzy Logic Demo was developed wich enables a user to visualize risk and resource maps based on individual data in several data formats.
Fuzzy logic of quasi-truth an algebraic treatment
Di Nola, Antonio; Turunen, Esko
2016-01-01
This book presents the first algebraic treatment of quasi-truth fuzzy logic and covers the algebraic foundations of many-valued logic. It offers a comprehensive account of basic techniques and reports on important results showing the pivotal role played by perfect many-valued algebras (MV-algebras). It is well known that the first-order predicate Łukasiewicz logic is not complete with respect to the canonical set of truth values. However, it is complete with respect to all linearly ordered MV –algebras. As there are no simple linearly ordered MV-algebras in this case, infinitesimal elements of an MV-algebra are allowed to be truth values. The book presents perfect algebras as an interesting subclass of local MV-algebras and provides readers with the necessary knowledge and tools for formalizing the fuzzy concept of quasi true and quasi false. All basic concepts are introduced in detail to promote a better understanding of the more complex ones. It is an advanced and inspiring reference-guide for graduate s...
Optimization of adaptive fuzzy processor design
Baturone, I.; Sánchez-Solano, Santiago; Barriga, Angel; Huertas-Díaz, J. L.
1998-01-01
A fuzzy processor is programmed to provide anoptimum output for solving a given problem. It could theoretically solve any problem (from a static point of view) if it is an universal approximator. This paper addresses the design of fuzzy processors aiming at a twofold objective: efficient adaptive approximation of different and even dynamically changing surfaces and hardware simplicity. Adequate programmable parameters and a fully-parallel architecture are selected. Mixed-signal blocks b...
Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation
Masmoudi, Mohamed Slim; Masmoudi, Mohamed
2016-01-01
This paper describes the design and the implementation of a trajectory tracking controller using fuzzy logic for mobile robot to navigate in indoor environments. Most of the previous works used two independent controllers for navigation and avoiding obstacles. The main contribution of the paper can be summarized in the fact that we use only one fuzzy controller for navigation and obstacle avoidance. The used mobile robot is equipped with DC motor, nine infrared range (IR) sensors to measure the distance to obstacles, and two optical encoders to provide the actual position and speeds. To evaluate the performances of the intelligent navigation algorithms, different trajectories are used and simulated using MATLAB software and SIMIAM navigation platform. Simulation results show the performances of the intelligent navigation algorithms in terms of simulation times and travelled path. PMID:27688748
Application of fuzzy logic for determining of coal mine mechanization
Institute of Scientific and Technical Information of China (English)
HOSSEINI SAA; ATAEI M; HOSSEINI S M; AKHYANI M
2012-01-01
The fundamental task of mining engineers is to produce more coal at a given level of labour input and material costs,for optimum quality and maximum efficiency.To achieve these goals,it is necessary to automate and mechanize mining operations.Mechanization is an objective that can result in significant cost reduction and higher levels of profitability for underground mines.To analyze the potential of mechanization,some important factors such as seam inclination and thickness,geological disturbances,seam floor conditions and roof conditions should be considered.In this study we have used fuzzy logic,membership functions and created fuzzy rule-based methods and considered the ultimate objective:mechanization of mining.As a case study,the mechanization of the Tazare coal seams in Shahroud area of Iran was investigated.The results show a low potential for mechanization in most of the Tazare coal seams.
Determination of coal mine mechanization using fuzzy logic
Institute of Scientific and Technical Information of China (English)
Ataei M; Khalokakaei R; Hossieni M
2009-01-01
Two of the most important tasks in coal mines are to improve efficiency and to increase production besides keeping safety constantly in mind. In order to obtain these goals, mine mechanization is required. Mine mechanization needs high levels of investment and should therefore be studied carefully before final decisions about mechanization are made. When analysizing the potential for mechanization the following, rather imprecise, factors should be considered: seam inclination and thickness, geologi-cal disturbances, seam floor conditions, roof conditions, water at the working face and the extension of seams. In our study we have used fuzzy logic, membership functions and created fuzzy rule - based methods and to considered the ultimate objective: mechanization of mining. As a case study, the mechanization of the Takht coal seams in Iran was investigated. The results show a high potential for mechanization in most of the Takht coal seams.
A reinforcement learning-based architecture for fuzzy logic control
Berenji, Hamid R.
1992-01-01
This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.
Fuzzy Logic Trajectory Tracking Controller for a Tanker
Directory of Open Access Journals (Sweden)
Dur Muhammad Pathan
2012-04-01
Full Text Available This paper proposes a fuzzy logic controller for design of autopilot of a ship. Triangular membership functions have been use for fuzzification and the centroid method for defuzzification. A nonlinear mathematical model of an oil tanker has been considered whose parameters vary with the depth of water. The performance of proposed controller has been tested under both course changing and trajectory keeping mode of operations. It has been demonstrated that the performance is robust in shallow as well as deep waters.
Fuzzy Logic Approach to Diagnosis of Feedwater Heater Performance Degradation
Energy Technology Data Exchange (ETDEWEB)
Kang, Yeon Kwan; Kim, Hyeon Min; Heo, Gyun Young [Kyung Hee University, Yongin (Korea, Republic of); Sang, Seok Yoon [Engineering and Technical Center, Korea Hydro, Daejeon (Korea, Republic of)
2014-08-15
Since failure in, damage to, and performance degradation of power generation components in operation under harsh environment of high pressure and high temperature may cause both economic and human loss at power plants, highly reliable operation and control of these components are necessary. Therefore, a systematic method of diagnosing the condition of these components in its early stages is required. There have been many researches related to the diagnosis of these components, but our group developed an approach using a regression model and diagnosis table, specializing in diagnosis relating to thermal efficiency degradation of power plant. However, there was a difficulty in applying the method using the regression model to power plants with different operating conditions because the model was sensitive to value. In case of the method that uses diagnosis table, it was difficult to find the level at which each performance degradation factor had an effect on the components. Therefore, fuzzy logic was introduced in order to diagnose performance degradation using both qualitative and quantitative results obtained from the components' operation data. The model makes performance degradation assessment using various performance degradation variables according to the input rule constructed based on fuzzy logic. The purpose of the model is to help the operator diagnose performance degradation of components of power plants. This paper makes an analysis of power plant feedwater heater by using fuzzy logic. Feedwater heater is one of the core components that regulate life-cycle of a power plant. Performance degradation has a direct effect on power generation efficiency. It is not easy to observe performance degradation of feedwater heater. However, on the other hand, troubles such as tube leakage may bring simultaneous damage to the tube bundle and therefore it is the object of concern in economic aspect. This study explains the process of diagnosing and verifying typical
Institute of Scientific and Technical Information of China (English)
傅军; 张晓峰; 卞鸿魏; 许江宁; 朱涛
2007-01-01
The performance of duo-antenna GPS/INS integrated system can be improved by combining the GPS's vehicle attitude information with the INS information. In practical application, a GPS receiver may be unable to provide the heading information for ships as it may subject to disturbing, therefore the performance of conventional Kalman filter may be degraded. The paper presents a modified adaptive Kalman filter which uses two FLCs(fuzzy logic control) to adjust the integrating modes of the two systems. The calculation of Kalman gain is based on the internal states of Kalman filter, GPS working status and the vehicle's kinetic states. The new adaptive Kalman filter thus can select appropriate fusion mode, adjust the noise intensity in the filter and prevent it from divergence, thus improving the system's performance. The actual measurement data from the INS and GPS have verified the effectiveness of the proposed FLC-based adaptive Kalman filter.%双天线GPS提供的载体姿态信息与惯性导航系统信息进行融合可提高组合导航系统的性能.由于在实际应用中,GPS接收机可能会受到某种干扰无法提供舰船航向信息,从而降低传统卡尔曼滤波器的性能.因而提出了一种新的基于模糊逻辑控制的自适应卡尔曼滤波器.改进后的卡尔曼滤波器使用两个模糊逻辑控制器来调整两个系统的组合模式,并且根据卡尔曼滤波器的内部状态、GPS工作状态和舰船运动状态来计算卡尔曼增益.通过使用INS和GPS的实测数据验证,这种基于模糊逻辑控制的自适应卡尔曼滤波器能有效的提高INS/GPS组合导航系统的性能.
Wang, Chenhui
2016-01-01
In this paper, control of uncertain fractional-order financial chaotic system with input saturation and external disturbance is investigated. The unknown part of the input saturation as well as the system’s unknown nonlinear function is approximated by a fuzzy logic system. To handle the fuzzy approximation error and the estimation error of the unknown upper bound of the external disturbance, fractional-order adaptation laws are constructed. Based on fractional Lyapunov stability theorem, an adaptive fuzzy controller is designed, and the asymptotical stability can be guaranteed. Finally, simulation studies are given to indicate the effectiveness of the proposed method. PMID:27783648
Fuzzy Logic Controller Scheme for Floor Vibration Control
Directory of Open Access Journals (Sweden)
Nyawako Donald Steve
2015-01-01
Full Text Available The design of civil engineering floors is increasingly being governed by their vibration serviceability performance. This trend is the result of advancements in design technologies offering designers greater flexibilities in realising more lightweight, longer span and more open-plan layouts. These floors are prone to excitation from human activities. The present research work looks at analytical studies of active vibration control on a case study floor prototype that has been specifically designed to be representative of a real office floor structure. Specifically, it looks at tuning fuzzy control gains with the aim of adapting them to measured structural responses under human excitation. Vibration mitigation performances are compared with those of a general velocity feedback controller, and these are found to be identical in these sets of studies. It is also found that slightly less control force is required for the fuzzy controller scheme at moderate to low response levels and as a result of the adaptive gain, at very low responses the control force is close to zero, which is a desirable control feature. There is also saturation in the peak gain with the fuzzy controller scheme, with this gain tending towards the optimal feedback gain of the direct velocity feedback (DVF at high response levels for this fuzzy design.
A Development of Self-Organization Algorithm for Fuzzy Logic Controller
Energy Technology Data Exchange (ETDEWEB)
Park, Y.M.; Moon, U.C. [Seoul National Univ. (Korea, Republic of). Coll. of Engineering; Lee, K.Y. [Pennsylvania State Univ., University Park, PA (United States). Dept. of Electrical Engineering
1994-09-01
This paper proposes a complete design method for an on-line self-organizing fuzzy logic controller without using any plant model. By mimicking the human learning process, the control algorithm finds control rules of a system for which little knowledge has been known. To realize this, a concept of Fuzzy Auto-Regressive Moving Average(FARMA) rule is introduced. In a conventional fuzzy logic control, knowledge on the system supplied by an expert is required in developing control rules. However, the proposed new fuzzy logic controller needs no expert in making control rules. Instead, rules are generated using the history of input-output pairs, and new inference and defuzzification methods are developed. The generated rules are strode in the fuzzy rule space and updated on-line by a self-organizing procedure. The validity of the proposed fuzzy logic control method has been demonstrated numerically in controlling an inverted pendulum. (author). 28 refs., 16 figs.
Adaptive Fuzzy Attitude Control of Flexible Satellite
Institute of Scientific and Technical Information of China (English)
GUAN Ping; LIU Xiang-dong; CHEN Jia-bin
2005-01-01
The adaptive fuzzy control is applied in the attitude stabilization of flexible satellite. The detailed design procedure of the adaptive fuzzy control system is presented. Two T-S models are used as both controller and identifier. The parameters of the controller could be modified according to the information of the identifier. Simulation results show that the method can effectively cope with the uncertainty of flexible satellite by on-line learning and thus posses the good robustness. With the proposed method, the precise attitude control is accomplished.
Hu, Hong; Li, Su; Wang, YunJiu; Qi, XiangLin; Shi, ZhongZhi
2008-10-01
Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells' dynamical equations. Although there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.
Institute of Scientific and Technical Information of China (English)
2008-01-01
Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells’ dynamical equations. Al- though there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.
Institute of Scientific and Technical Information of China (English)
HU Hong; LI Su; WANG YunJiu; QI XiangLin; SHI ZhongZhi
2008-01-01
Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells' dynamical equations. Al-though there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.
Composite Fuzzy Logic Control Approach to a Flexible Joint Manipulator
Directory of Open Access Journals (Sweden)
Mohd Ashraf Ahmad
2013-01-01
Full Text Available The raised complicatedness of the dynamics of a robot manipulator considering joint elasticity makes conventional model‐based control strategies complex and hard to synthesize. This paper presents investigations into the development of hybrid intelligent control schemes for the trajectory tracking and vibration control of a flexible joint manipulator. To study the effectiveness of the controllers, a collocated proportional‐derivative (PD‐type Fuzzy Logic Controller (FLC is first developed for the tip angular position control of a flexible joint manipulator. This is then extended to incorporate a non‐collocated Fuzzy Logic Controller, a non‐collocated proportional‐ integral‐derivative (PID and an input‐shaping scheme for the vibration reduction of the flexible joint system. The positive zero‐vibration‐derivative‐derivative (ZVDD shaper is designed based on the properties of the system. The implementation results of the response of the flexible joint manipulator with the controllers are presented in time and frequency domains. The performances of the hybrid control schemes are examined in terms of input tracking capability, level of vibration reduction and time response specifications. Finally, a comparative assessment of the control techniques is presented and discussed.
Fuzzy logic control of vehicle suspensions with dry friction nonlinearity
Indian Academy of Sciences (India)
L Emir Sakman; Rahmi Guclu; Nurkan Yagiz
2005-10-01
We design and investigate the performance of fuzzy logic-controlled (FLC) active suspensions on a nonlinear vehicle model with four degrees of freedom, without causing any degeneration in suspension working limits. Force actuators were mounted parallel to the suspensions. In this new approach, linear combinations of the vertical velocities of the suspension ends and accelerations of the points of connection of the suspension to the body have been used as input variables. The study clearly demonstrates the effectiveness of the fuzzy logic controller for active suspension systems. Suspension working space degeneration is the most important problem in various applications. Decreasing the amplitudes of vehicle body vibrations improves ride comfort. Body bounce and pitch motion of the vehicle are presented both in time domain when travelling over a ramp-step road proﬁle and in frequency domain. The results are compared with those of uncontrolled systems. At the end of this study, the performance and the advantage of the suggested approach and the improvement in ride comfort are discussed.
Fuzzy logic association: performance, implementation issues, and automated resource allocation
Smith, James F., III
1999-07-01
A recursive multisensor association algorithm has been developed based on fuzzy logic. It associates data from the same target for multiple sensor types. The algorithm provides an estimate of the number of targets present and reduced noise estimates of the quantities being measured. Uncertain information from many sources including other algorithms can be easily incorporated. A comparison of the algorithm to a more conventional Bayesian association algorithm is provided. The algorithm is applied to a multitarget environment for simulated data. The data from both the ESM and radar systems is noisy and the ESM data is intermittent. The radar data has probability of detection less than unity. The effects on parameter estimation, determination of the number of targets, and multisensor data association is examined for the case of a large number of targets closely spaced in the RF-PRI plane. When a sliding window is introduced to minimize memory and CPU requirements the algorithm is shown to lose little in performance, while gaining significantly in speed. The algorithm's CPU usage, computational complexity, and real-time implementation requirements are examined. Finally, the algorithm will be considered as an association algorithm for a multifunction antenna that makes use of fuzzy logic for resource allocation.
Controlling Smart Green House Using Fuzzy Logic Method
Directory of Open Access Journals (Sweden)
Rafiuddin Syam
2015-10-01
Full Text Available To increase agricultural output it is needed a system that can help the environmental conditions for optimum plant growth. Smart greenhouse allows for plants to grow optimally, because the temperature and humidity can be controlled so that no drastic changes. It is necessary for optimal smart greenhouse needed a system to manipulate the environment in accordance with the needs of the plant. In this case the setting temperature and humidity in the greenhouse according to the needs of the plant. So using an automated system for keeping such environmental condition is important. In this study, the authors use fuzzy logic to make the duration of watering the plants more dynamic in accordance with the input temperature and humidity so that the temperature and humidity in the green house plants maintained in accordance to the reference condition. Based on the experimental results using fuzzy logic method is effective to control the duration of watering and to maintain the optimum temperature and humidity inside the greenhouse
Software Operational Profile Based Test Case Allocation Using Fuzzy Logic
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Software operational profile (SOP) is used in software reliability prediction, software quality assessment, performance analysis of software, test case allocation, determination of "when to stop testing," etc. Due to the limited data resources and large efforts required to collect and convert the gathered data into point estimates, reluctance is observed by the software professionals to develop the SOP. A framework is proposed to develop SOP using fuzzy logic, which requires usage data in the form of linguistics. The resulting profile is named fuzzy software operational profile (FSOP). Based on this work, this paper proposes a generalized approach for the allocation of test cases, in which occurrence probability of operations obtained from FSOP are combined with the criticality of the operations using fuzzy inference system (FIS). Traditional methods for the allocation of test cases do not consider the application in which software operates. This is intuitively incorrect. To solve this problem, allocation of test cases with respect to software application using the FIS model is also proposed in this paper.
Sensor Network Self-Localization Using Fuzzy Logic
Directory of Open Access Journals (Sweden)
Arash Dana
2007-12-01
Full Text Available Location awareness is an important capability for a series of enhanced wireless businesses. sensor networks are dense wireless networks of small low cost sensors, which collect and disseminate environmental data, for monitoring, military application and so on. Localization is an unconstrained optimization problem. position estimation is based on various, distance / path measures, which include anchor and non-anchor nodes. Anchor positions, have been predetermined to help us localize other nodes. This study proposes using a combination of fuzzy techniques, and advanced APS method, to estimate unknown nodes. In a network with twenty hundred nodes of which twenty percent operates as anchors. These nodes localize the other one hundred and sixties. It is necessary to select the best four anchors for localizing. We suppose that the anchors neighbor to unknown nodes are the best. It is time-consuming to find the distance of unknown anchors in such a widespread network. Using the fuzzy logic, putting the limitation of distance, and selecting the nearest anchor to the unknown node, the nearest four anchoress can be selected. In this case the rate of localization error will be decreased due to selecting neighbor anchors. Therefore, we can localize nodes by using ad-hoc positioning system. Fuzzy rules help us to estimate position in less than 2.4 seconds with mean normal positioning deviation of z =0.4597.
Adaptive Neuro-fuzzy approach in friction identification
Zaiyad Muda @ Ismail, Muhammad
2016-05-01
Friction is known to affect the performance of motion control system, especially in terms of its accuracy. Therefore, a number of techniques or methods have been explored and implemented to alleviate the effects of friction. In this project, the Artificial Intelligent (AI) approach is used to model the friction which will be then used to compensate the friction. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is chosen among several other AI methods because of its reliability and capabilities of solving complex computation. ANFIS is a hybrid AI-paradigm that combines the best features of neural network and fuzzy logic. This AI method (ANFIS) is effective for nonlinear system identification and compensation and thus, being used in this project.
Fuzzy Logic-Based Secure and Fault Tolerant Job Scheduling in Grid
Institute of Scientific and Technical Information of China (English)
WANG Cheng; JIANG Congfeng; LIU Xiaohu
2007-01-01
The uncertainties of grid sites security are main hurdle to make the job scheduling secure, reliable and fault-tolerant. Most existing scheduling algorithms use fixed-number job replications to provide fault tolerant ability and high scheduling success rate, which consume excessive resources or can not provide sufficient fault tolerant functions when grid security conditions change. In this paper a fuzzy-logic-based self-adaptive replication scheduling (FSARS) algorithm is proposed to handle the fuzziness or uncertainties of job replication number which is highly related to trust factors behind grid sites and user jobs. Remote sens-ing-based soil moisture extraction (RSBSME) workload experiments in real grid environment are performed to evaluate the proposed approach and the results show that high scheduling success rate of up to 95% and less grid resource utilization can be achieved through FSARS. Extensive experiments show that FSARS scales well when user jobs and grid sites increase.
AN INTELLIGENT METHOD FOR REAL-TIME DETECTION OF DDOS ATTACK BASED ON FUZZY LOGIC
Institute of Scientific and Technical Information of China (English)
Wang Jiangtao; Yang Geng
2008-01-01
The paper puts forward a variance-time plots method based on slide-window mechanism to calculate the Hurst parameter to detect Distribute Denial of Service (DDoS) attack in real time. Based on fuzzy logic technology that can adjust itself dynamically under the fuzzy rules, an intelligent DDoS judgment mechanism is designed. This new method calculates the Hurst parameter quickly and detects DDoS attack in real time. Through comparing the detecting technologies based on statistics and feature-packet respectively under different experiments, it is found that the new method can identify the change of the Hurst parameter resulting from DDoS attack traffic with different intensities, and intelligently judge DDoS attack self-adaptively in real time.
Hot metal temperature prediction and simulation by fuzzy logic in a blast furnace
International Nuclear Information System (INIS)
This work describes the development and further validation of a model devoted to blast furnace hot metal temperature forecast, based on Fuzzy logic principles. The model employs as input variables, the control variables of an actual blast furnace: Blast volume, moisture, coal injection, oxygen addition, etc. and it yields as a result the hot metal temperature with a forecast horizon of forty minutes. As far as the variables used to develop the model have been obtained from data supplied by an actual blast furnaces sensors, it is necessary to properly analyse and handle such data. Especial attention was paid to data temporal correlation, fitting by interpolation the different sampling rates. In the training stage of the model the ANFIS (Adaptive Neuro-Fuzzy Inference System) and the Subtractive Clustering algorithms have been used. (Author) 9 refs
Directory of Open Access Journals (Sweden)
Pritha Agrawal
2014-02-01
Full Text Available This paper presents a comparative analysis of speed control of brushless DC motor (BLDC drive fed with conventional two-level, three and five level diode clamped multilevel inverter (DC-MLI. The performance of the drive system is successfully evaluated using Fuzzy Logic (FL based speed controller. The control structure of the proposed drive system is described. The speed and torque characteristic of conventional two-level inverter is compared with the three and five-level multilevel inverter (MLI for various operating conditions. The three and five level diode clamped multilevel inverters are simulated using IGBT’s and the mathematical model of BLDC motor has been developed in MATLAB/SIMULINK environment. The simulation results show that the Fuzzy based speed controller eliminate torque ripples and provides fast speed response. The developed Fuzzy Logic model has the ability to learn instantaneously and adapt its own controller parameters based on disturbances with minimum steady state error, overshoot and rise time of the output voltage.
Ali, Mohd.Hasan; MURATA, Toshiaki; Tamura, Junji
2007-01-01
This paper presents a fuzzy logic-controlled superconducting magnetic energy storage (SMES) to improve the transient stability of an electric power system. In order to see how effective the proposed fuzzy controlled SMES in improving the transient stability is, its performance is compared to that of a conventional proportional-integral (PI) controlled SMES. Furthermore, a comparative study between the fuzzy controlled SMES and fuzzy controlled braking resistor (BR) is carried out. Simulation ...
Application of the fuzzy logic in content-based image retrieval
Xiaoling, Wang; Kanglin, Xie
2005-01-01
This paper imports the fuzzy logic into image retrieval to deal with the vagueness and ambiguity of human judgment of image similarity. Our retrieval system has the following properties: firstly adopting the fuzzy language variables to describe the similarity degree of image features, not the features themselves; secondly making use of the fuzzy inference to instruct the weights assignment among various image features; thirdly expressing the subjectivity of human perceptions by fuzzy rules im...
Landslide Susceptibility Assessment Through Fuzzy Logic Inference System (flis)
Bibi, T.; Gul, Y.; Rahman, A. Abdul; Riaz, M.
2016-09-01
Landslide is among one of the most important natural hazards that lead to modification of the environment. It is a regular feature of a rapidly growing district Mansehra, Pakistan. This caused extensive loss of life and property in the district located at the foothills of Himalaya. Keeping in view the situation it is concluded that besides structural approaches the non-structural approaches such as hazard and risk assessment maps are effective tools to reduce the intensity of damage. A landslide susceptibility map is base for engineering geologists and geomorphologists. However, it is not easy to produce a reliable susceptibility map due to complex nature of landslides. Since 1980s, several mathematical models have been developed to map landslide susceptibility and hazard. Among various models this paper is discussing the effectiveness of fuzzy logic approach for landslide susceptibility mapping in District Mansehra, Pakistan. The factor maps were modified as landslide susceptibility and fuzzy membership functions were assessed for each class. Likelihood ratios are obtained for each class of contributing factors by considering the expert opinion. The fuzzy operators are applied to generate landslide susceptibility maps. According to this map, 17% of the study area is classified as high susceptibility, 32% as moderate susceptibility, 51% as low susceptibility and areas. From the results it is found that the fuzzy model can integrate effectively with various spatial data for landslide hazard mapping, suggestions in this study are hope to be helpful to improve the applications including interpretation, and integration phases in order to obtain an accurate decision supporting layer.
Energy Technology Data Exchange (ETDEWEB)
Vasconcelos, Wagner Eustaquio de; Lira, Carlos Alberto Brayner de Oliveira; Brito, Thiago Souza Pereira de; Afonso, Antonio Claudio Marques, E-mail: wagner@unicap.br, E-mail: cabol@ufpe.br, E-mail: afonsofisica@gmail.com, E-mail: thiago.brito86@yahoo.com.br [Universidade Federal de Pernambuco (UFPE), Recife, PE (Brazil). Centro de Tecnologia e Geociencias. Departamento de Energia Nuclear; Cruz Filho, Antonio Jose da; Marques, Jose Antonio, E-mail: antonio.jscf@gmail.com, E-mail: jamarkss@uol.com.br [Universidade Catolica de Pernambuco (CCT/PUC-PE), Recife, PE (Brazil). Centro de Ciencias e Tecnologia; Teixeira, Marcello Goulart, E-mail: marcellogt@dcc.ufrj.br [Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ (Brazil). Instituto de Matematica. Dept. de Matematica
2013-07-01
Nuclear reactors are in nature nonlinear systems and their parameters vary with time as a function of power level. These characteristics must be considered if large power variations occur in power plant operational regimes, such as in load-following conditions. A PWR reactor has a component called pressurizer, whose function is to supply the necessary high pressure for its operation and to contain pressure variations in the primary cooling system. The use of control systems capable of reducing fast variations of the operation variables and to maintain the stability of this system is of fundamental importance. The best-known controllers used in industrial control processes are proportional-integral-derivative (PID) controllers due to their simple structure and robust performance in a wide range of operating conditions. However, designing a fuzzy controller is seen to be a much less difficult task. Once a Fuzzy Logic controller is designed for a particular set of parameters of the nonlinear element, it yields satisfactory performance for a range of these parameters. The objective of this work is to develop fuzzy proportional-integral-derivative (fuzzy-PID) control strategies to control the level of water in the reactor. In the study of the pressurizer, several computer codes are used to simulate its dynamic behavior. At the fuzzy-PID control strategy, the fuzzy logic controller is exploited to extend the finite sets of PID gains to the possible combinations of PID gains in stable region. Thus the fuzzy logic controller tunes the gain of PID controller to adapt the model with changes in the water level of reactor. The simulation results showed a favorable performance with the use to fuzzy-PID controllers. (author)
A Novel Robust Adaptive Fuzzy Controller
Institute of Scientific and Technical Information of China (English)
LIU Xiao-hua; WANG Xiu-hong; FEN En-min
2002-01-01
For a class of continuous-time nonlinear system, a novel robust adaptive fuzzy controller is proposed by using of Lyapunov method. It is proven that the control algorithm is globally stable, the output tracking-error can convergence to a domain of zero under the assumptions. As a result, the system controlled has stronger robustness for disturbance and modeling error.
Syllogistic reasoning in fuzzy logic and its application to usuality and reasoning with dispositions
Zadeh, L. A.
1985-01-01
A fuzzy syllogism in fuzzy logic is defined to be an inference schema in which the major premise, the minor premise and the conclusion are propositions containing fuzzy quantifiers. A basic fuzzy syllogism in fuzzy logic is the intersection/product syllogism. Several other basic syllogisms are developed that may be employed as rules of combination of evidence in expert systems. Among these is the consequent conjunction syllogism. Furthermore, it is shown that syllogistic reasoning in fuzzy logic provides a basis for reasoning with dispositions; that is, with propositions that are preponderantly but not necessarily always true. It is also shown that the concept of dispositionality is closely related to the notion of usuality and serves as a basis for what might be called a theory of usuality - a theory which may eventually provide a computational framework for commonsense reasoning.
A fuzzy logic system based on Schweizer-Sklar t-norm
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Based on the Schweizer-Sklar t-norm, a fuzzy logic system UL* is established, and its soundness theorem and completeness theorem are proved. The following facts are pointed out: the well-known formal system SBL(~) is a semantic extension of UL*; the fuzzy logic system IMTLΔ is a special case of UL* when two negations in UL* coincide. Moreover, the connections between the system UL* and some fuzzy logic formal systems are investigated. Finally, starting from the concepts of "the strength of an 'AND' operator" by R.R. Yager and "the strength of fuzzy rule interaction" by T. Whalen, the essential meaning of a parameter p in UL* is explained and the use of fuzzy logic system UL* in approximate reasoning is presented.
Multi-objective decision-making under uncertainty: Fuzzy logic methods
Hardy, Terry L.
1995-01-01
Fuzzy logic allows for quantitative representation of vague or fuzzy objectives, and therefore is well-suited for multi-objective decision-making. This paper presents methods employing fuzzy logic concepts to assist in the decision-making process. In addition, this paper describes software developed at NASA Lewis Research Center for assisting in the decision-making process. Two diverse examples are used to illustrate the use of fuzzy logic in choosing an alternative among many options and objectives. One example is the selection of a lunar lander ascent propulsion system, and the other example is the selection of an aeration system for improving the water quality of the Cuyahoga River in Cleveland, Ohio. The fuzzy logic techniques provided here are powerful tools which complement existing approaches, and therefore should be considered in future decision-making activities.
A Modern Syllogistic Method in Intuitionistic Fuzzy Logic with Realistic Tautology.
Rushdi, Ali Muhammad; Zarouan, Mohamed; Alshehri, Taleb Mansour; Rushdi, Muhammad Ali
2015-01-01
The Modern Syllogistic Method (MSM) of propositional logic ferrets out from a set of premises all that can be concluded from it in the most compact form. The MSM combines the premises into a single function equated to 1 and then produces the complete product of this function. Two fuzzy versions of MSM are developed in Ordinary Fuzzy Logic (OFL) and in Intuitionistic Fuzzy Logic (IFL) with these logics augmented by the concept of Realistic Fuzzy Tautology (RFT) which is a variable whose truth exceeds 0.5. The paper formally proves each of the steps needed in the conversion of the ordinary MSM into a fuzzy one. The proofs rely mainly on the successful replacement of logic 1 (or ordinary tautology) by an RFT. An improved version of Blake-Tison algorithm for generating the complete product of a logical function is also presented and shown to be applicable to both crisp and fuzzy versions of the MSM. The fuzzy MSM methodology is illustrated by three specific examples, which delineate differences with the crisp MSM, address the question of validity values of consequences, tackle the problem of inconsistency when it arises, and demonstrate the utility of the concept of Realistic Fuzzy Tautology. PMID:26380357
A Modern Syllogistic Method in Intuitionistic Fuzzy Logic with Realistic Tautology
Directory of Open Access Journals (Sweden)
Ali Muhammad Rushdi
2015-01-01
Full Text Available The Modern Syllogistic Method (MSM of propositional logic ferrets out from a set of premises all that can be concluded from it in the most compact form. The MSM combines the premises into a single function equated to 1 and then produces the complete product of this function. Two fuzzy versions of MSM are developed in Ordinary Fuzzy Logic (OFL and in Intuitionistic Fuzzy Logic (IFL with these logics augmented by the concept of Realistic Fuzzy Tautology (RFT which is a variable whose truth exceeds 0.5. The paper formally proves each of the steps needed in the conversion of the ordinary MSM into a fuzzy one. The proofs rely mainly on the successful replacement of logic 1 (or ordinary tautology by an RFT. An improved version of Blake-Tison algorithm for generating the complete product of a logical function is also presented and shown to be applicable to both crisp and fuzzy versions of the MSM. The fuzzy MSM methodology is illustrated by three specific examples, which delineate differences with the crisp MSM, address the question of validity values of consequences, tackle the problem of inconsistency when it arises, and demonstrate the utility of the concept of Realistic Fuzzy Tautology.
A Modern Syllogistic Method in Intuitionistic Fuzzy Logic with Realistic Tautology.
Rushdi, Ali Muhammad; Zarouan, Mohamed; Alshehri, Taleb Mansour; Rushdi, Muhammad Ali
2015-01-01
The Modern Syllogistic Method (MSM) of propositional logic ferrets out from a set of premises all that can be concluded from it in the most compact form. The MSM combines the premises into a single function equated to 1 and then produces the complete product of this function. Two fuzzy versions of MSM are developed in Ordinary Fuzzy Logic (OFL) and in Intuitionistic Fuzzy Logic (IFL) with these logics augmented by the concept of Realistic Fuzzy Tautology (RFT) which is a variable whose truth exceeds 0.5. The paper formally proves each of the steps needed in the conversion of the ordinary MSM into a fuzzy one. The proofs rely mainly on the successful replacement of logic 1 (or ordinary tautology) by an RFT. An improved version of Blake-Tison algorithm for generating the complete product of a logical function is also presented and shown to be applicable to both crisp and fuzzy versions of the MSM. The fuzzy MSM methodology is illustrated by three specific examples, which delineate differences with the crisp MSM, address the question of validity values of consequences, tackle the problem of inconsistency when it arises, and demonstrate the utility of the concept of Realistic Fuzzy Tautology.
Adaptive Fuzzy Output-Feedback Method Applied to Fin Control for Time-Delay Ship Roll Stabilization
Directory of Open Access Journals (Sweden)
Rui Bai
2014-01-01
Full Text Available The ship roll stabilization by fin control system is considered in this paper. Assuming that angular velocity in roll cannot be measured, an adaptive fuzzy output-feedback control is investigated. The fuzzy logic system is used to approximate the uncertain term of the controlled system, and a fuzzy state observer is designed to estimate the unmeasured states. By utilizing the fuzzy state observer and combining the adaptive backstepping technique with adaptive fuzzy control design, an observer-based adaptive fuzzy output-feedback control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB, and the control strategy is effective to decrease the roll motion. Simulation results are included to illustrate the effectiveness of the proposed approach.
First-Order ARMA Type Fuzzy Time Series Method Based on Fuzzy Logic Relation Tables
Directory of Open Access Journals (Sweden)
Cem Kocak
2013-01-01
Full Text Available Fuzzy time series approaches have an important deficiency according to classical time series approaches. This deficiency comes from the fact that all of the fuzzy time series models developed in the literature use autoregressive (AR variables, without any studies that also make use of moving averages (MAs variables with the exception of only one study (Egrioglu et al. (2013. In order to eliminate this deficiency, it is necessary to have many of daily life time series be expressed with Autoregressive Moving Averages (ARMAs models that are based not only on the lagged values of the time series (AR variables but also on the lagged values of the error series (MA variables. To that end, a new first-order fuzzy ARMA(1,1 time series forecasting method solution algorithm based on fuzzy logic group relation tables has been developed. The new method proposed has been compared against some methods in the literature by applying them on Istanbul Stock Exchange national 100 index (IMKB and Gold Prices time series in regards to forecasting performance.
Adaptive fuzzy multivariable controller design based on genetic algorithm for an air handling unit
International Nuclear Information System (INIS)
In HVAC (Heating, Ventilation and Air Conditioning systems, effective thermal management is required because energy and operation costs of buildings are directly influenced by how well an air-conditioning system performs. HVAC systems are typically nonlinear time varying with disturbances, where conventional PID controllers may trade-off between stability and rise time. To overcome this limitation, a Genetic Algorithm based AFLC (Adaptive Fuzzy Logic Controller design has been proposed for the multivariable control of temperature and humidity of a typical AHU (air handling unit by manipulating valve positions to adjust the water and steam flow rates. Modulating equal percentage Globe valves for chilled water and steam have been modeled according to exact flow rates of water and steam. A novel method for the adaptation of FLC (Fuzzy Logic Controller by modifying FRM (Fuzzy Rule Matrix based on GA (genetic algorithm) has been proposed. This requires re-designing the complete FLC in MATLAB/Simulink whose procedure has also been proposed. The proposed adaptive controller outperforms the existing fuzzy controller in terms of steady state error, rise time and settling time. - Highlights: • GA based Adaptive Fuzzy Logic Controller to improve performance of HVAC system. • Multivariable control of an air handling unit to adjust the water and steam flow rates. • Significant improvement in steady state error, rise time and settling time of the control system
Robust observer-based adaptive fuzzy sliding mode controller
Oveisi, Atta; Nestorović, Tamara
2016-08-01
In this paper, a new observer-based adaptive fuzzy integral sliding mode controller is proposed based on the Lyapunov stability theorem. The plant is subjected to a square-integrable disturbance and is assumed to have mismatch uncertainties both in state- and input-matrices. Based on the classical sliding mode controller, the equivalent control effort is obtained to satisfy the sufficient requirement of sliding mode controller and then the control law is modified to guarantee the reachability of the system trajectory to the sliding manifold. In order to relax the norm-bounded constrains on the control law and solve the chattering problem of sliding mode controller, a fuzzy logic inference mechanism is combined with the controller. An adaptive law is then introduced to tune the parameters of the fuzzy system on-line. Finally, for evaluating the controller and the robust performance of the closed-loop system, the proposed regulator is implemented on a real-time mechanical vibrating system.
Adaptive fuzzy logic control for solar buildings
El-Deen, M. M. G. Naser
2002-01-01
Significant progress has been made on maximising passive solar heating loads through the careful selection of glazing, orientation and internal mass within building spaces. Control of space heating in buildings of this type has become a complex problem. Additionally, and in common with most building control applications, there is a need to develop control solutions that permit simple and transparent set up and commissioning procedures. This work concerns the development and testing of an adap...
Cheap diagnosis using structural modelling and fuzzy-logic based detection
DEFF Research Database (Denmark)
Izadi-Zamanabadi, Roozbeh; Blanke, Mogens; Katebi, Serajeddin
2003-01-01
relations for linear or non-linear dynamic behaviour, and combine this with fuzzy output observer design to provide an effective diagnostic approach. An adaptive neuro-fuzzy inference method is used. A fuzzy adaptive threshold is employed to cope with practical uncertainty. The methods are demonstrated...
Fuzzy Logic Based Method for Improving Text Summarization
Suanmali, Ladda; Binwahlan, Mohammed Salem
2009-01-01
Text summarization can be classified into two approaches: extraction and abstraction. This paper focuses on extraction approach. The goal of text summarization based on extraction approach is sentence selection. One of the methods to obtain the suitable sentences is to assign some numerical measure of a sentence for the summary called sentence weighting and then select the best ones. The first step in summarization by extraction is the identification of important features. In our experiment, we used 125 test documents in DUC2002 data set. Each document is prepared by preprocessing process: sentence segmentation, tokenization, removing stop word, and word stemming. Then, we use 8 important features and calculate their score for each sentence. We propose text summarization based on fuzzy logic to improve the quality of the summary created by the general statistic method. We compare our results with the baseline summarizer and Microsoft Word 2007 summarizers. The results show that the best average precision, rec...
Forest fire autonomous decision system based on fuzzy logic
Lei, Z.; Lu, Jianhua
2010-11-01
The proposed system integrates GPS / pseudolite / IMU and thermal camera in order to autonomously process the graphs by identification, extraction, tracking of forest fire or hot spots. The airborne detection platform, the graph-based algorithms and the signal processing frame are analyzed detailed; especially the rules of the decision function are expressed in terms of fuzzy logic, which is an appropriate method to express imprecise knowledge. The membership function and weights of the rules are fixed through a supervised learning process. The perception system in this paper is based on a network of sensorial stations and central stations. The sensorial stations collect data including infrared and visual images and meteorological information. The central stations exchange data to perform distributed analysis. The experiment results show that working procedure of detection system is reasonable and can accurately output the detection alarm and the computation of infrared oscillations.
Access Network Selection Based on Fuzzy Logic and Genetic Algorithms
Directory of Open Access Journals (Sweden)
Mohammed Alkhawlani
2008-01-01
Full Text Available In the next generation of heterogeneous wireless networks (HWNs, a large number of different radio access technologies (RATs will be integrated into a common network. In this type of networks, selecting the most optimal and promising access network (AN is an important consideration for overall networks stability, resource utilization, user satisfaction, and quality of service (QoS provisioning. This paper proposes a general scheme to solve the access network selection (ANS problem in the HWN. The proposed scheme has been used to present and design a general multicriteria software assistant (SA that can consider the user, operator, and/or the QoS view points. Combined fuzzy logic (FL and genetic algorithms (GAs have been used to give the proposed scheme the required scalability, flexibility, and simplicity. The simulation results show that the proposed scheme and SA have better and more robust performance over the random-based selection.
Transient Stability Assessment using Decision Trees and Fuzzy Logic Techniques
Directory of Open Access Journals (Sweden)
A. Y. Abdelaziz
2013-09-01
Full Text Available Many techniques are used for Transient Stability assessment (TSA of synchronous generators encompassing traditional time domain state numerical integration, Lyapunov based methods, probabilistic approaches and Artificial Intelligence (AI techniques like pattern recognition and artificial neural networks.This paper examines another two proposed artificial intelligence techniques to tackle the transient stability problem. The first technique is based on the Inductive Inference Reasoning (IIR approach which belongs to a particular family of machine learning from examples. The second presents a simple fuzzy logic classifier system for TSA. Not only steady state but transient attributes are used for transient stability estimation so as to reflect machine dynamics and network changes due to faults.The two techniques are tested on a standard test power system. The performance evaluation demonstrated satisfactory results in early detection of machine instability. The advantage of the two techniques is that they are straightforward and simple for on-line implementation.
Pneumatic motor speed control by trajectory tracking fuzzy logic controller
Indian Academy of Sciences (India)
Cengiz Safak; Vedat Topuz; A Fevzi Baba
2010-02-01
In this study, trajectory tracking fuzzy logic controller (TTFLC) is proposed for the speed control of a pneumatic motor (PM). A third order trajectory is deﬁned to determine the trajectory function that has to be tracked by the PM speed. Genetic algorithm (GA) is used to ﬁnd the TTFLC boundary values of membership functions (MF) and weights of control rules. In addition, artiﬁcial neural networks (ANN) modelled dynamic behaviour of PM is given. This ANN model is used to ﬁnd the optimal TTFLC parameters by ofﬂine GA approach. The experimental results show that designed TTFLC successfully enables the PM speed track the given trajectory under various working conditions. The proposed approach is superior to PID controller. It also provides simple and easy design procedure for the PM speed control problem.
A framework for analysis of extended fuzzy logic
Institute of Scientific and Technical Information of China (English)
Farnaz SABAHI; M.-R.AKBARZADEH-T
2014-01-01
We address a framework for the analysis of extended fuzzy logic (FLe) and elaborate mainly the key characteris-tics of FLe by proving several qualification theorems and proposing a new mathematical tool named the A-granule. Specifically, we reveal that within FLe a solution in the presence of incomplete information approaches the one gained by complete infor-mation. It is also proved that the answers and their validities have a structural isomorphism within the same context. This rela-tionship is then used to prove the representation theorem that addresses the rationality of FLe-based reasoning. As a conse-quence of the developed theoretical description of FLe, we assert that in order to solve a problem, having complete information is not a critical need; however, with more information, the answers achieved become more specific. Furthermore, reasoning based on FLe has the advantage of being computationally less expensive in the analysis of a given problem and is faster.
Assessment of nuclear energy sustainability index using fuzzy logic
International Nuclear Information System (INIS)
Nuclear energy is increasingly perceived as an attractive mature energy generation technology that can deliver an answer to the worldwide increasing energy demand while respecting environmental concerns as well as contributing to a reduced dependence on fossil fuel. Advancing nuclear energy deployment demands an assessment of nuclear energy with respect to all sustainability dimensions. In this paper, the nuclear energy, whose sustainability will be assessed, is governed by the dynamics of three subsystems: environmental, economic, and sociopolitical. The overall sustainability is then a non-linear function of the individual sustainabilities. Each subsystem is evaluated by means of many components (pressure, status, and response). The combination of each group of indicators by means of fuzzy logic provides a measurement of sustainability for each subsystem.
Different control applications on a vehicle using fuzzy logic control
Indian Academy of Sciences (India)
Nurkan Yagiz; L Emir Sakman; Rahmi Guclu
2008-02-01
In this paper, the active suspension control of a vehicle model that has ﬁve degrees of freedom with a passenger seat using a fuzzy logic controller is studied. Three cases are taken into account as different control applications. In the ﬁrst case, the vehicle model having passive suspensions with an active passenger seat is controlled. In the second case, active suspensions with passive passenger seat combination are controlled. In the third case, both the passenger seat and suspensions have active controllers. Vibrations of the passenger seat in the three cases due to road bump input are simulated. At the end of the study, the results are compared in order to select the combination that supplies the best ride comfort.
A New Approach for Lossless Image Compression Based on Fuzzy Adaptive Prediction
Institute of Scientific and Technical Information of China (English)
Wu Yingqian(吴颖谦); Fang Tao; Shi Pengfei
2004-01-01
This paper proposes a novel approach for image lossless compression based on fuzzy logic and adaptive prediction. By a flexible strategy, the method can acquire a set of original predictors describing the more detail characteristic. Using a neural network, the proposed method can more efficiently organize the training of original predictors and implement adaptive prediction in fuzzy style. In entropy coding phase, the context-based conditional adaptive arithmetic encoding is adopted. The experiments demonstrate the characteristics make the approach achieve good tradeoff between computational complexity and efficiency of prediction and good performance for lossless compression.
Efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control
Directory of Open Access Journals (Sweden)
Allaoua Boumediene
2008-01-01
Full Text Available This paper presents the application of Fuzzy Logic for DC motor speed control using Particle Swarm Optimization (PSO. Firstly, the controller designed according to Fuzzy Logic rules is such that the systems are fundamentally robust. Secondly, the Fuzzy Logic controller (FLC used earlier was optimized with PSO so as to obtain optimal adjustment of the membership functions only. Finally, the FLC is completely optimized by Swarm Intelligence Algorithms. Digital simulation results demonstrate that in comparison with the FLC the designed FLC-PSO speed controller obtains better dynamic behavior and superior performance of the DC motor, as well as perfect speed tracking with no overshoot.
Identiﬁcation of Optimal Operating Point Of PV Modules Using Fuzzy Logic Control
Directory of Open Access Journals (Sweden)
Hadi nabizadeh
2013-11-01
Full Text Available This paper introduces an intelligent control method for maximum power point tracking in solar array in dealing with the rapid variations in temperature and radiation. Fuzzy logic controller and DC/DC boost converter are the most important components of this system. The simulation results of fuzzy logic controller are compared with simulation results of PI controller in both cases without noise and with Gaussian noise in solar cell voltage. The results show that fuzzy logic controller performance is better than PI controller especially in the presence of noise.
Virtual Reality Simulation of Fuzzy-logic Control during Underwater Dynamic Positioning
Institute of Scientific and Technical Information of China (English)
Midhin Das Thekkedan; Cheng Siong Chin; Wai Lok Woo
2015-01-01
In this paper, graphical-user-interface (GUI) software for simulation and fuzzy-logic control of a remotely operated vehicle (ROV) using MATLABTM GUI Designing Environment is proposed. The proposed ROV’s GUI platform allows the controller such as fuzzy-logic control systems design to be compared with other controllers such as proportional-integral-derivative (PID) and sliding-mode controller (SMC) systematically and interactively. External disturbance such as sea current can be added to improve the modelling in actual underwater environment. The simulated results showed the position responses of the fuzzy-logic control exhibit reasonable performance under the sea current disturbance.
Directory of Open Access Journals (Sweden)
Mr.M.Senthilkumar
2010-08-01
Full Text Available This paper aims to determine the optimal capacitors required for maximum output power of a single phase self excited induction generator (SEIG. This paper deals with theoretical, fuzzy logic and practical approach in order to extract the values of optimal capacitor for maximum output power .To find this capacitor value, nonlinear equations have to be solved from the equivalent circuit of SEIG. The advantages of using fuzzy logic approach are universal control algorithm, fast converging, accepting of noise and inaccurate signals. At the end of the paper the theoretical and fuzzy logic results are verified with experimental values.
A study on the application of the fuzzy logic controllers(III)
International Nuclear Information System (INIS)
In this paper, we first present mathematical analysis of what causes the fuzzy logic controllers perform better than PI controllers and an experimental verifications of it by using a model nuclear steam generator. Next, we developed a fuzzy algorithm for tuning of a PI controller and confirmed that the fuzzy logic can turn out to be more useful than Boolean logic. Third, we developed a one-node model for the prediction of the steam generator water level during its swell and shrink due to the steam dump valve operations. 30 figs, 9 tabs, 12 refs. (Author)
Virtual reality simulation of fuzzy-logic control during underwater dynamic positioning
Thekkedan, Midhin Das; Chin, Cheng Siong; Woo, Wai Lok
2015-03-01
In this paper, graphical-user-interface (GUI) software for simulation and fuzzy-logic control of a remotely operated vehicle (ROV) using MATLAB™ GUI Designing Environment is proposed. The proposed ROV's GUI platform allows the controller such as fuzzy-logic control systems design to be compared with other controllers such as proportional-integral-derivative (PID) and sliding-mode controller (SMC) systematically and interactively. External disturbance such as sea current can be added to improve the modelling in actual underwater environment. The simulated results showed the position responses of the fuzzy-logic control exhibit reasonable performance under the sea current disturbance.
Using fuzzy logic for automatic control: Case study of a problem of cereals samples classification
Directory of Open Access Journals (Sweden)
Lakhoua Najeh Mohamed
2009-01-01
Full Text Available The aim of this paper is to present the use of fuzzy logic for automatic control of industrial systems particularly the way to approach a problem of classification. We present a case study of a grading system of cereals that allows us to determine the price of transactions of cereals in Tunisia. Our contribution in this work consists in proposing not only an application of the fuzzy logic on the grading system of cereals but also a methodology enabling the proposing of a new grading system based on the concept of 'Grade' while using the fuzzy logic techniques. .
A Fuzzy Logic Framework for Integrating Multiple Learned Models
Energy Technology Data Exchange (ETDEWEB)
Bobi Kai Den Hartog
1999-03-01
The Artificial Intelligence field of Integrating Multiple Learned Models (IMLM) explores ways to combine results from sets of trained programs. Aroclor Interpretation is an ill-conditioned problem in which trained programs must operate in scenarios outside their training ranges because it is intractable to train them completely. Consequently, they fail in ways related to the scenarios. We developed a general-purpose IMLM solution, the Combiner, and applied it to Aroclor Interpretation. The Combiner's first step, Scenario Identification (M), learns rules from very sparse, synthetic training data consisting of results from a suite of trained programs called Methods. S1 produces fuzzy belief weights for each scenario by approximately matching the rules. The Combiner's second step, Aroclor Presence Detection (AP), classifies each of three Aroclors as present or absent in a sample. The third step, Aroclor Quantification (AQ), produces quantitative values for the concentration of each Aroclor in a sample. AP and AQ use automatically learned empirical biases for each of the Methods in each scenario. Through fuzzy logic, AP and AQ combine scenario weights, automatically learned biases for each of the Methods in each scenario, and Methods' results to determine results for a sample.
Directory of Open Access Journals (Sweden)
Darko I. Božanić
2010-01-01
pontoon bridge location for the purpose of overcoming water obstacles. The decision making process includes a higher or lower level of indefiniteness of criteria needed for making a relevant decision. Since the fuzzy logic is very suitable for expressing indefiniteness and uncertainty, the decision making process using a fuzzy logic approach is shown in the paper. Characteristics of multi-criteria methods and selection of methods for evaluation With the development of the evaluation theory, evaluation models were being developed as well. Different objectives of evaluation and other differences in the whole procedure had an impact on the development of the majority of evaluation models adapted to different requests. The main objective of multi-criteria methods is to define the priority between particular variants or criteria in the situation with a large number of decision makers and a large number of decision making criteria in repeated periods of time. Main notions of fuzzy logic and fuzzy sets In a larger sense, the fuzzy logic is a synonym for the fuzzy sets theory which refers to the class of objects with unclear borders the membership of which is measured by certain value. It is important to realize that the essence of the fuzzy logic is different from the essence of the traditional logic system. This logic, based on clear and precisely defined rules, has its foundation in the set theory. An element can or cannot be a part of a set, which means that sets have clearly determined borders. Contrary to the conventional logic, the fuzzy logic does not define precisely the membership of an element to a set. The membership value is expressed in percentage, for example. The fuzzy logic is very close to human perception. Fuzzy system modeling for evaluation of selected locations The fuzzy logic is usually used for complex system modeling, when it is difficult to define interdependences between certain variables by other methods. The criteria for the selection of locations for
A New Fuzzy Adaptive Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
FANG Lei; ZHANG Huan-chun; JING Ya-zhi
2005-01-01
Multiple genetic algorithms (GAs) need a large population size, which will take a long time for evolution.A new fuzzy adaptive GA is proposed in this paper. This algorithm is more effective in global search while keeping the overall population size constant. The simulation results of function optimization show that with the proposed algorithm, the phenomenon of premature convergence can be overcome effectively, and a satisfying optimization result is obtained.
Neuro-fuzzy Logic Control of Single Phase Matrix Converter Fed Induction Heating System
Directory of Open Access Journals (Sweden)
P. Umasankar
2015-02-01
Full Text Available This study presents a design and simulation of Neuro-Fuzzy Logic Controlled (NFLC Single Phase Matrix Converter (SPMC fed Induction Heating (IH system. Single phase matrix converter system is an AC-AC converter which eliminates the usage of reactive storage elements and its performance over varying operating frequencies can be controlled by varying the Pulse Width Modulation (PWM signal fed to the switches of single phase matrix converter. In the existing system a Fuzzy Logic Controller (FLC was designed to control the matrix converter which yielded low Total Harmonic Distortion (THD values when compared to previous systems. In this study a Neuro-Fuzzy Logic Controller was designed to control the single phase matrix converter and the results obtained prove its advantage over the existing Fuzzy Logic based control system.
Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.
Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko
2016-03-01
In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. PMID:26774211
Control of Single Axis Magnetic Levitation System Using Fuzzy Logic Control
Directory of Open Access Journals (Sweden)
Tania Tariq Salim
2013-12-01
Full Text Available This paper presents a fuzzy logic controller design for the stabilization of magnetic levitation system (Maglev 's.Additionally, the investigation on Linear Quadratic Regulator Controller (LQRC also mentioned here. This paper presents the difference between the performance of fuzzy logic control (FLC and LQRC for the same linear model of magnetic levitation system .A magnetic levitation is a nonlinear unstable system and the fuzzy logic controller brings the magnetic levitation system to a stable region by keeping a magnetic ball suspended in the air. The modeling of the system is simulated using Matlab Simulink and connected to Hilink platform and the maglev model of Zeltom company. This paper presents a comparison for both LQRC and FLC to control a ball suspended on the air. The performance results of simulation shows that the fuzzy logic controller had better performance than the LQR control.
Fuzzy logic feedback control for fed-batch enzymatic hydrolysis of lignocellulosic biomass.
Tai, Chao; Voltan, Diego S; Keshwani, Deepak R; Meyer, George E; Kuhar, Pankaj S
2016-06-01
A fuzzy logic feedback control system was developed for process monitoring and feeding control in fed-batch enzymatic hydrolysis of a lignocellulosic biomass, dilute acid-pretreated corn stover. Digested glucose from hydrolysis reaction was assigned as input while doser feeding time and speed of pretreated biomass were responses from fuzzy logic control system. Membership functions for these three variables and rule-base were created based on batch hydrolysis data. The system response was first tested in LabVIEW environment then the performance was evaluated through real-time hydrolysis reaction. The feeding operations were determined timely by fuzzy logic control system and efficient responses were shown to plateau phases during hydrolysis. Feeding of proper amount of cellulose and maintaining solids content was well balanced. Fuzzy logic proved to be a robust and effective online feeding control tool for fed-batch enzymatic hydrolysis.
Fuzzy Logic Control for Semi-Active Suspension System of Tracked Vehicle
Institute of Scientific and Technical Information of China (English)
管继富; 顾亮; 侯朝桢; 王国丽
2004-01-01
The model of half a tracked vehicle semi-active suspension is established. The fuzzy logic controller of the semi-active suspension system is constructed. The acceleration of driver's seat and its time derivative are used as the inputs of the fuzzy logic controller, and the fuzzy logic controller output determines the semi-active suspension controllable damping force. The fuzzy logic controller is to minimize the mean square root of acceleration of the driver's seat. The control forces of controllable dampers behind the first road wheel are obtained by time delay, and the delay times are determined by the vehicle speed and axles distances. The simulation results show that this control method can decrease the acceleration of driver's seat and the suspension travel of the first road wheel, the ride quality is improved obviously.
Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.
Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko
2016-03-01
In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique.
Smart handover based on fuzzy logic trend in IEEE802.11 mobile IPv6 networks
Lim, Joanne Mun-Yee
2012-01-01
A properly designed handoff algorithm is essential in reducing the connection quality deterioration when a mobile node moves across the cell boundaries. Therefore, to improve communication quality, we identified three goals in our paper. The first goal is to minimize unnecessary handovers and increase communication quality by reducing misrepresentations of RSSI readings due to multipath and shadow effect with the use of additional parameters. The second goal is to control the handover decisions depending on the users' mobility by utilizing location factors as one of the input parameters in a fuzzy logic handover algorithm. The third goal is to minimize false handover alarms caused by sudden fluctuations of parameters by monitoring the trend of fuzzy logic outputs for a period of time before making handover decision. In this paper, we use RSSI, speed and distance as the input decision criteria of a handover trigger algorithm by means of fuzzy logic. The fuzzy logic output trend is monitored for a period of tim...
Fuzzy Logic Control of Single Phase Matrix Converter Fed Induction Heating System
P. Umasankar; Dr.S.Senthilkumar
2014-01-01
This article represents the modeling and simulation of a Single Phase Matrix Converter (SPMC) fed Induction Heating (IH) system. The working principle and the control system using Fuzzy Logic Controller (FLC) are elucidated in detail. The performance of the system and their harmonic content analysis of Single Phase Matrix Converter are carried out in MATLAB/Simulink environment. Pulse Width Modulation (PWM) switching strategy by varying the duty cycle based on Fuzzy Logic Control is employed ...
FUZZY LOGIC CONTROLLER FOR CASCADED H-BRIDGE MULTI LEVEL INVERTER
A. CHITRA,; T. MEENAKSHI,; Asha, J.
2011-01-01
This paper describes the design of a rule based Fuzzy Logic Controller (FLC) for multilevel inverter. A multilevel inverter is controlled by varying the modulation index of the inverter by keepingthe DC link voltage constant. The nine level Cascaded H Bridge multilevel inverter topology is designed as the test system for the design of fuzzy logic controller after a thorough evaluation of its advantages. The conventional control methods are mainly restricted to the direct and indirect control ...
Energy Technology Data Exchange (ETDEWEB)
Voorter, P.H.C.
1995-05-01
The application of fuzzy logic in the process control of a cement furnace at a Dutch cement industry (Enci in Maastricht) proved to be successful: the production increased by 4% and the energy consumption was reduced by 3% per ton product. Fuzzy logic can also be used in smaller energy equipment. Applications in a burner of a central heating boiler, a solar water heater and a catalytic converter in a motorcycle are discussed. 5 figs., 1 tab., 2 refs.
MRI and PET image fusion using fuzzy logic and image local features.
Javed, Umer; Riaz, Muhammad Mohsin; Ghafoor, Abdul; Ali, Syed Sohaib; Cheema, Tanveer Ahmed
2014-01-01
An image fusion technique for magnetic resonance imaging (MRI) and positron emission tomography (PET) using local features and fuzzy logic is presented. The aim of proposed technique is to maximally combine useful information present in MRI and PET images. Image local features are extracted and combined with fuzzy logic to compute weights for each pixel. Simulation results show that the proposed scheme produces significantly better results compared to state-of-art schemes.
Simulation of Interleaved Boost Converter Using Closed Loop Fuzzy Logic Controller
Karthikeyan, R; Argha Paul2; Balamurugan, P.
2014-01-01
Interleaved power converters can be very beneficial for high performance electrical equipment applications. Reductions in size and electromagnetic emission along with an increase in efficiency, transient response, and reliability are among the many advantages to using such converters. Fuzzy Logic is a linguistic approach which emerges to design simple, complex and embedded systems with control inputs. A fuzzy logic uses linguistic variables which states IF A AND B THEN C” this...
Directory of Open Access Journals (Sweden)
Y. N. Petrenko
2011-01-01
Full Text Available The purpose of a crane control system is to provide load transfer with minimum swinging. The paper presents a developed three-dimensional simulation model of a bridge crane with fuzzy logic controller designed with application of genetic algorithms. Comparative indices of oscillation while load transferring are given in the paper. The indices have been obtained at various parameters of the fuzzy logic controller.
Prescribed Performance Fuzzy Adaptive Output-Feedback Control for Nonlinear Stochastic Systems
Directory of Open Access Journals (Sweden)
Lili Zhang
2014-01-01
Full Text Available A prescribed performance fuzzy adaptive output-feedback control approach is proposed for a class of single-input and single-output nonlinear stochastic systems with unmeasured states. Fuzzy logic systems are used to identify the unknown nonlinear system, and a fuzzy state observer is designed for estimating the unmeasured states. Based on the backstepping recursive design technique and the predefined performance technique, a new fuzzy adaptive output-feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded in probability and the tracking error remains an adjustable neighborhood of the origin with the prescribed performance bounds. A simulation example is provided to show the effectiveness of the proposed approach.
DEFF Research Database (Denmark)
Achiche, Sofiane; Ahmed, Saeema
2009-01-01
with a different set of geometric features and shapes. In this paper the authors propose an automatic approach to formalize the relationships between geometric information of 3D objects and the intended emotion using fuzzy logic. In addition automatically generated fuzzy rules and sets are developed and compared...
Fuzzy-logic approach to HTR nuclear power plant model control
Energy Technology Data Exchange (ETDEWEB)
Bubak, M.; Moscinski, J. (Akademia Gorniczo-Hutnicza, Krakow (Poland)); Jewulski, J. (Institute of Physical Chemistry, Krakow (Poland))
1983-01-01
The fuzzy-set theory is used to incorporate linguistic 'rules of the thumb' of a human operator in the HTR nuclear power plant controller. The results of the extensive computer simulations are encouraging and confirm the usefulness of this approach in nuclear power plant control. In the Appendix, a short introduction to fuzzy logic is given.
Richardson, Albert O.
1997-01-01
This research has investigated the use of fuzzy logic, via the Matlab Fuzzy Logic Tool Box, to design optimized controller systems. The engineering system for which the controller was designed and simulate was the container crane. The fuzzy logic algorithm that was investigated was the 'predictive control' algorithm. The plant dynamics of the container crane is representative of many important systems including robotic arm movements. The container crane that was investigated had a trolley motor and hoist motor. Total distance to be traveled by the trolley was 15 meters. The obstruction height was 5 meters. Crane height was 17.8 meters. Trolley mass was 7500 kilograms. Load mass was 6450 kilograms. Maximum trolley and rope velocities were 1.25 meters per sec. and 0.3 meters per sec., respectively. The fuzzy logic approach allowed the inclusion, in the controller model, of performance indices that are more effectively defined in linguistic terms. These include 'safety' and 'cargo swaying'. Two fuzzy inference systems were implemented using the Matlab simulation package, namely the Mamdani system (which relates fuzzy input variables to fuzzy output variables), and the Sugeno system (which relates fuzzy input variables to crisp output variable). It is found that the Sugeno FIS is better suited to including aspects of those plant dynamics whose mathematical relationships can be determined.
Barbosa, A Márcia; Real, Raimundo
2012-01-01
We modelled the distributions of two toads (Bufo bufo and Epidalea calamita) in the Iberian Peninsula using the favourability function, which makes predictions directly comparable for different species and allows fuzzy logic operations to relate different models. The fuzzy intersection between individual models, representing favourability for the presence of both species simultaneously, was compared with another favourability model built on the presences shared by both species. The fuzzy union between individual models, representing favourability for the presence of any of the two species, was compared with another favourability model based on the presences of either or both of them. The fuzzy intersections between favourability for each species and the complementary of favourability for the other (corresponding to the logical operation "A and not B") were compared with models of exclusive presence of one species versus the exclusive presence of the other. The results of modelling combined species data were highly similar to those of fuzzy logic operations between individual models, proving fuzzy logic and the favourability function valuable for comparative distribution modelling. We highlight several advantages of fuzzy logic over other forms of combining distribution models, including the possibility to combine multiple species models for management and conservation planning.
Fuzzy logic based control system for fresh water aquaculture: A MATLAB based simulation approach
Directory of Open Access Journals (Sweden)
Rana Dinesh Singh
2015-01-01
Full Text Available Fuzzy control is regarded as the most widely used application of fuzzy logic. Fuzzy logic is an innovative technology to design solutions for multiparameter and non-linear control problems. One of the greatest advantages of fuzzy control is that it uses human experience and process information obtained from operator rather than a mathematical model for the definition of a control strategy. As a result, it often delivers solutions faster than conventional control design techniques. The proposed system is an attempt to apply fuzzy logic techniques to predict the stress factor on the fish, based on line data and rule base generated using domain expert. The proposed work includes a use of Data acquisition system, an interfacing device for on line parameter acquisition and analysis, fuzzy logic controller (FLC for inferring the stress factor. The system takes stress parameters on the fish as inputs, fuzzified by using FLC with knowledge base rules and finally provides single output. All the parameters are controlled and calibrated by the fuzzy logic toolbox and MATLAB programming.
Li, Yongming; Tong, Shaocheng
2016-10-01
In this paper, a fuzzy adaptive switched control approach is proposed for a class of uncertain nonholonomic chained systems with input nonsmooth constraint. In the control design, an auxiliary dynamic system is designed to address the input nonsmooth constraint, and an adaptive switched control strategy is constructed to overcome the uncontrollability problem associated with x0(t0) = 0. By using fuzzy logic systems to tackle unknown nonlinear functions, a fuzzy adaptive control approach is explored based on the adaptive backstepping technique. By constructing the combination approximation technique and using Young's inequality scaling technique, the number of the online learning parameters is reduced to n and the 'explosion of complexity' problem is avoid. It is proved that the proposed method can guarantee that all variables of the closed-loop system converge to a small neighbourhood of zero. Two simulation examples are provided to illustrate the effectiveness of the proposed control approach.
Sliding mode control of wind-induced vibrations using fuzzy sliding surface and gain adaptation
Thenozhi, Suresh; Yu, Wen
2016-04-01
Although fuzzy/adaptive sliding mode control can reduce the chattering problem in structural vibration control applications, they require the equivalent control and the upper bounds of the system uncertainties. In this paper, we used fuzzy logic to approximate the standard sliding surface and designed a dead-zone adaptive law for tuning the switching gain of the sliding mode control. The stability of the proposed controller is established using Lyapunov stability theory. A six-storey building prototype equipped with an active mass damper has been used to demonstrate the effectiveness of the proposed controller towards the wind-induced vibrations.
Fuzzy Control Strategies in Human Operator and Sport Modeling
Ivancevic, Tijana T; Markovic, Sasa
2009-01-01
The motivation behind mathematically modeling the human operator is to help explain the response characteristics of the complex dynamical system including the human manual controller. In this paper, we present two different fuzzy logic strategies for human operator and sport modeling: fixed fuzzy-logic inference control and adaptive fuzzy-logic control, including neuro-fuzzy-fractal control. As an application of the presented fuzzy strategies, we present a fuzzy-control based tennis simulator.
Derrouazin, A.; Aillerie, M.; Mekkakia-Maaza, N.; Charles, J. P.
2016-07-01
Several researches for management of diverse hybrid energy systems and many techniques have been proposed for robustness, savings and environmental purpose. In this work we aim to make a comparative study between two supervision and control techniques: fuzzy and classic logics to manage the hybrid energy system applied for typical housing fed by solar and wind power, with rack of batteries for storage. The system is assisted by the electric grid during energy drop moments. A hydrogen production device is integrated into the system to retrieve surplus energy production from renewable sources for the household purposes, intending the maximum exploitation of these sources over years. The models have been achieved and generated signals for electronic switches command of proposed both techniques are presented and discussed in this paper.
Chen, Shyi-Ming; Manalu, Gandhi Maruli Tua; Pan, Jeng-Shyang; Liu, Hsiang-Chuan
2013-06-01
In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization (PSO) techniques. First, we fuzzify the historical training data of the main factor and the secondary factor, respectively, to form two-factors second-order fuzzy logical relationships. Then, we group the two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, we obtain the optimal weighting vector for each fuzzy-trend logical relationship group by using PSO techniques to perform the forecasting. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index and the NTD/USD exchange rates. The experimental results show that the proposed method gets better forecasting performance than the existing methods.
Control of convergence in a computational fluid dynamic simulation using fuzzy logic
Institute of Scientific and Technical Information of China (English)
LIU; Xunliang; (刘训良); TAO; Wenquan; (陶文铨); ZHENG; Ping; (郑平); HE; Yaling; (何雅玲); WANG; Qiuwang; (王秋旺)
2002-01-01
A fuzzy control method was used to accelerate iteration convergence in numerical fluid dynamic simulation using SIMPLER algorithm. The residual ratio of momentum or energy equation between two successive iterations was used as the input variable. A fuzzy logic algorithm was developed in order to obtain the relative increment of the under-relaxation factor and its new value was then used for the next iteration. The algorithm was tested by four benchmark problems. In all cases considered, when the fuzzy control logic was used, convergence was achieved with nearly the minimum number of iterations, showing the feasibility of the proposed method.
Risk analysis with a fuzzy-logic approach of a complex installation
Peikert, Tim; Garbe, Heyno; Potthast, Stefan
2016-09-01
This paper introduces a procedural method based on fuzzy logic to analyze systematic the risk of an electronic system in an intentional electromagnetic environment (IEME). The method analyzes the susceptibility of a complex electronic installation with respect to intentional electromagnetic interference (IEMI). It combines the advantages of well-known techniques as fault tree analysis (FTA), electromagnetic topology (EMT) and Bayesian networks (BN) and extends the techniques with an approach to handle uncertainty. This approach uses fuzzy sets, membership functions and fuzzy logic to handle the uncertainty with probability functions and linguistic terms. The linguistic terms add to the risk analysis the knowledge from experts of the investigated system or environment.
Maximum Power Search in Wind Turbine Based on Fuzzy Logic Control
Directory of Open Access Journals (Sweden)
Evgenije Adzic
2009-03-01
Full Text Available This paper describes fuzzy logic control of induction generator speed in windturbine application. The aim of fuzzy controller is to establishe maximum power delivery tothe grid from available wind power. Fully-controlled wind turbine which consists ofinduction generator and back-to-back converter is under estimate. This configuration hasfull control over the electrical torque, full control of the speed, and also supports reactivepower compensation and operation under grid disturbances. Fuzzy logic control alorithmhas been aplied and validated by detailed simulation in MATLAB/Simulink. All systemcomponents have been described in detail.
Design and Simulation of Fuzzy Logic controller for DSTATCOM In Power System
Directory of Open Access Journals (Sweden)
Anju Gupta
2011-10-01
Full Text Available In this paper design of self tuned fuzzy set theory based PI controller is incorporated in typical FACTS device DSTATCOM. Its effects are tested in power systems. The modeling and the controller block diagram for DSTATCOM with detailed design of self tuned fuzzy logic controller is presented. The performance of proposed fuzzy logic DSTATCOM has been simulated for current balancing and harmonic compensation for both linear and non-linear loads. The results show the capability of proposed model in enhancing the dynamic behavior ofinterconnected systems. The simulation is carried out in MATLAB SIMULINK and the results shows the results confirm the feasibility of proposed system.
Transient Stability of A.C Generator Controlled By Using Fuzzy Logic Controller
Directory of Open Access Journals (Sweden)
Srinivas Singirikonda
2014-03-01
Full Text Available This article is focused on the implementation of fuzzy logic controller for a.c generator; a power system is highly nonlinear system. At present, power system can be simulated and analyzed based on a mathematical model however, uncertainty still exists due to change of loads and an occurrence of fault. Recently, fuzzy theory highly flexible easily operated and revised, theory is a better choice, especially for a complicated system with many variables. Hence, this work aims to develop a controller based on fuzzy logic to simulate an automatic voltage regulator in transient stability power system analysis. By adding power system stabilizer for tuning of fuzzy logic stabilizing controller there is no need for exact knowledge of power system mathematical model. The fuzzy controller parameters settings are independent due to nonlinear changes in generator and transmission lines operating conditions. Because of that proposed fuzzy controlled power system stabilizer should perform better than the conventional controller. To overcome the drawbacks of conventional power system stabilizer (CPSS, numerous techniques have been proposed in the article. The conventional PSS's effect on the system damping is then compared with a fuzzy logic based PSS while applied to a single machine infinite bus power system.
Development of Fuzzy Logic System to Predict the SAW Weldment Shape Profiles
Institute of Scientific and Technical Information of China (English)
H.K.Narang; M.M.Mahapatra; P.K.Jha; P.Biswas
2012-01-01
A fuzzy model was presented to predict the weldment shape profile of submerged arc welds (SAW)including the shape of heat affected zone (HAZ).The SAW bead-on-plates were welded by following a full factorial design matrix.The design marx consisted of three levels of input welding process parameters.The welds were cross-sectioned and etched,and the zones were measured.A mapping technique was used to measure the various segments of the weld zones.These mapped zones were used to build a fuzzy logic model.The membership functions of the fuzzy model were chosen for the accurate prediction of the weld zone.The fuzzy model was further tested for a set of test case data.The weld zone predicted by the fuzzy logic model was compared with the experimentally obtained shape profiles and close agreement between the two was noted.The mapping technique developed for the weld zones and the fuzzy logic model can be used for on-line control of the SAW process.From the SAW fuzzy logic model an estimation of the fusion and HAZ can also be developed.
Monitoring of Air Polution by Using Fuzzy Logic
Directory of Open Access Journals (Sweden)
Dr. Gopal Upadhyaya,
2010-10-01
Full Text Available The Air Quality Index is a simple and generalized way to describe the air quality in China, Hong Kong, Malaysia and now in India. Indian Air Quality Index (IND-AQI is mainly a health related index with the descriptor words: “Good (0- 100”, “Moderate (101-200 ”, “Poor (201-300”, “Very Poor (301-400”, “Severe (401-500”. State Environment Protection Agency (SEPA is responsible for measuring the level of air pollution in China . In China the AQI is based on the level of 5 atmospheric pollutants, namely sulferdioxide(SO2, nitrogen dioxide (NO2, suspended particulates (PM10, carbon monoxide (CO, and ozone (O3 measured at the monitoring stations throughout each city (USEPA et al. 1998. An individual score is assigned to the level of each pollutant and the final AQI is the Highest of those scores. Air quality measurement are commonly reported in terms of micrograms per cubic meter (μgm/m3 or parts per million (ppm (http://en.wikipedia.org. The Conventional method used Linear Interpolation for calculating AQI . We applied a real time Fuzzy Logic System with Simulink to calculate AQI. This method gives satisfactory result and it is efficient to work under continuous working mode .
Fuzzy Logic Based Group Maturity Rating for Software Performance Prediction
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Driven by market requirements, software services organizations have adopted various software engineering process models (such as capability maturity model (CMM), capability maturity model integration (CMMI), ISO 9001:2000, etc.) and practice of the project management concepts defined in the project management body of knowledge. While this has definitely helped organizations to bring some methods into the software development madness, there always exists a demand for comparing various groups within the organization in terms of the practice of these defined process models. Even though there exist many metrics for comparison, considering the variety of projects in terms of technology, life cycle, etc., finding a single metric that caters to this is a difficult task. This paper proposes a model for arriving at a rating on group maturity within the organization. Considering the linguistic or imprecise and uncertain nature of software measurements, fuzzy logic approach is used for the proposed model. Without the barriers like technology or life cycle difference, the proposed model helps the organization to compare different groups within it with reasonable precision.
Automated mango fruit assessment using fuzzy logic approach
Hasan, Suzanawati Abu; Kin, Teoh Yeong; Sauddin@Sa'duddin, Suraiya; Aziz, Azlan Abdul; Othman, Mahmod; Mansor, Ab Razak; Parnabas, Vincent
2014-06-01
In term of value and volume of production, mango is the third most important fruit product next to pineapple and banana. Accurate size assessment of mango fruits during harvesting is vital to ensure that they are classified to the grade accordingly. However, the current practice in mango industry is grading the mango fruit manually using human graders. This method is inconsistent, inefficient and labor intensive. In this project, a new method of automated mango size and grade assessment is developed using RGB fiber optic sensor and fuzzy logic approach. The calculation of maximum, minimum and mean values based on RGB fiber optic sensor and the decision making development using minimum entropy formulation to analyse the data and make the classification for the mango fruit. This proposed method is capable to differentiate three different grades of mango fruit automatically with 77.78% of overall accuracy compared to human graders sorting. This method was found to be helpful for the application in the current agricultural industry.
Bioimpedance-based identification of malnutrition using fuzzy logic
International Nuclear Information System (INIS)
Protein-energy malnutrition reduces the quality of life, lengthens the time in hospital and dramatically increases mortality. Currently there is no simple and objective method available for assessing nutritional status and identifying malnutrition. The aim of this work is to develop a novel assistance system that supports the physician in the assessment of the nutritional status. Therefore, three subject groups were investigated: the first group consisted of 688 healthy subjects. Two additional groups consisted of 707 patients: 94 patients with primary diseases that are known to cause malnutrition, and 613 patients from a hospital admission screening. In all subjects bioimpedance spectroscopy measurements were performed, and the body composition was calculated. Additionally, in all patients the nutritional status was assessed by the subjective global assessment score. These data are used for the development and validation of the assistance system. The basic idea of the system is that nutritional status is reflected by body composition. Hence, features of the nutritional status, based on the body composition, are determined and compared with reference ranges, derived from healthy subjects' data. The differences are evaluated by a fuzzy logic system or a decision tree in order to identify malnourished patients. The novel assistance system allows the identification of malnourished patients, and it can be applied for screening and monitoring of the nutritional status of hospital patients
Structural Health Monitoring of Transport Aircraft with Fuzzy Logic Modeling
Directory of Open Access Journals (Sweden)
Ray C. Chang
2013-01-01
Full Text Available A structural health monitoring method based on the concept of static aeroelasticity is presented in this paper. This paper focuses on the estimation of these aeroelastic effects on older transport aircraft, in particular the structural components that are most affected, in severe atmospheric turbulence. Because the structural flexibility properties are mostly unknown to aircraft operators, only the trend, not the magnitude, of these effects is estimated. For this purpose, one useful concept in static aeroelastic effects for conventional aircraft structures is that under aeroelastic deformation the aerodynamic center should move aft. This concept is applied in the present paper by using the fuzzy-logic aerodynamic models. A twin-jet transport aircraft in severe atmospheric turbulence involving plunging motion is examined. It is found that the pitching moment derivatives in cruise with moderate to severe turbulence in transonic flight indicate some degree of abnormality in the stabilizer (i.e., the horizontal tail. Therefore, the horizontal tail is the most severely affected structural component of the aircraft probably caused by vibration under the dynamic loads induced by turbulence.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-01-01
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches. PMID:27472336
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-07-26
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-01-01
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches. PMID:27472336
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems
Directory of Open Access Journals (Sweden)
Chien-Hao Tseng
2016-07-01
Full Text Available This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF and fuzzy logic adaptive system (FLAS for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF, unscented Kalman filter (UKF, and CKF approaches.
Design and Development of Fuzzy Logic Controller for Liquid Flow Control
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Thae Thae Ei Aung,
2014-09-01
Full Text Available The purpose of this paper is to design a simulation system of fuzzy logic controller for Hydro-Electric Power Dam Control by using simulation package which is Fuzzy Logic Toolbox and Simulink in MATLAB software. By doing some modification of this paper, the design will be very useful for the system relates to liquid level control that widely use in industry nowadays. In this paper, we used the liquid level in tank , and use MATLAB to design a Fuzzy Control. The control of liquid level and flow between tanks is a basic problem in the process industries. Measuring the flow of liquids is a critical need in many industrial plants. Designers can realize lower development costs, superior features, and better end product performance by using fuzzy logic. Fuzzy Logic controller has better stability, small overshoot, and fast response. The paper presents Fuzzy Logic Controller (FLC method for safe reservoir control of dams through spillway gates and it presents FLC method for turbine valve to control the water flow through turbine for hydro power generation. Thus it shows overall effective control and operation of the mechanical equipments in a hydro electric power generation project with FLC and its usefulness. Dam control system takes information about water level, gate opening ratios, gate operation as parameters and controls spillway in case of flooding. In this design two input parameters: water level and flow rate and two output parameters: release valve control and drain valve control are used.
FUZZY LOGIC CONTROLLER FOR CASCADED H-BRIDGE MULTI LEVEL INVERTER
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A. CHITRA,
2011-02-01
Full Text Available This paper describes the design of a rule based Fuzzy Logic Controller (FLC for multilevel inverter. A multilevel inverter is controlled by varying the modulation index of the inverter by keepingthe DC link voltage constant. The nine level Cascaded H Bridge multilevel inverter topology is designed as the test system for the design of fuzzy logic controller after a thorough evaluation of its advantages. The conventional control methods are mainly restricted to the direct and indirect control of the inverter. The proposed fuzzy logic controller shows improved functionalities in the simulative experimental studies. The Fuzzy Associative Memory (FAM table is derived after a thorough research of the characteristics and compared with the conventional controller for harmonic disturbance, voltage profile and other system parameters.
A FUZZY-LOGIC CONTROL ALGORITHM FOR ACTIVE QUEUE MANAGEMENT IN IP NETWORKS
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Active Queue Management (AQM) is an active research area in the Internet community. Random Early Detection (RED) is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its average queue length is closely related to the load level. This paper proposes an effective fuzzy congestion control algorithm based on fuzzy logic which uses the predominance of fuzzy logic to deal with uncertain events. The main advantage of this new congestion control algorithm is that it discards the packet dropping mechanism of RED, and calculates packet loss according to a preconfigured fuzzy logic by using the queue length and the buffer usage ratio. Theoretical analysis and Network Simulator (NS) simulation results show that the proposed algorithm achieves more throughput and more stable queue length than traditional schemes. It really improves a router's ability in network congestion control in IP network.
Fuzzy logic path planning system for collision avoidance by an autonomous rover vehicle
Murphy, Michael G.
1993-01-01
The Space Exploration Initiative of the United States will make great demands upon NASA and its limited resources. One aspect of great importance will be providing for autonomous (unmanned) operation of vehicles and/or subsystems in space flight and surface exploration. An additional, complicating factor is that much of the need for autonomy of operation will take place under conditions of great uncertainty or ambiguity. Issues in developing an autonomous collision avoidance subsystem within a path planning system for application in a remote, hostile environment that does not lend itself well to remote manipulation by Earth-based telecommunications is addressed. A good focus is unmanned surface exploration of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. Four major issues addressed are (1) avoidance of a fuzzy moving obstacle; (2) backoff from a deadend in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system. Examples of the need for collision avoidance by an autonomous rover vehicle on the surface of Mars with a moving obstacle would be wind-blown debris, surface flow or anomalies due to subsurface disturbances, another vehicle, etc. The other issues of backoff, sensor fusion, and adaptive learning are important in the overall path planning system.
Fuzzy logic type 1 and type 2 based on LabVIEW FPGA
Ponce-Cruz, Pedro; MacCleery, Brian
2016-01-01
This book is a comprehensive introduction to LabVIEW FPGA™, a package allowing the programming of intelligent digital controllers in field programmable gate arrays (FPGAs) using graphical code. It shows how both potential difficulties with understanding and programming in VHDL and the consequent difficulty and slowness of implementation can be sidestepped. The text includes a clear theoretical explanation of fuzzy logic (type 1 and type 2) with case studies that implement the theory and systematically demonstrate the implementation process. It goes on to describe basic and advanced levels of programming LabVIEW FPGA and show how implementation of fuzzy-logic control in FPGAs improves system responses. A complete toolkit for implementing fuzzy controllers in LabVIEW FPGA has been developed with the book so that readers can generate new fuzzy controllers and deploy them immediately. Problems and their solutions allow readers to practice the techniques and to absorb the theoretical ideas as they arise. Fuzzy L...
Fuzzy logic multiobjective optimization for stand-alone photovoltaic plants
Energy Technology Data Exchange (ETDEWEB)
Tina, G.; Adorno, G.; Ragusa, C.
1998-07-01
objects of optimisation compete, it is applied a multiobjective optimisation technique, based on the fuzzy-logic theory. This technique requires to represent every optimisation object by a fuzzy-set which expresses the connection between the objects' value and the corresponding degree of satisfaction. In conclusion, the definition of a global fuzzy-set, which expresses the confluence between these values, allows to fix a single quality index to every project configuration. The discretion of the planner's selection has been fixed by the belonging functions to fuzzy-sets. These functions try to weigh, for every object, the judgement's classes, by themselves inaccurate, such as the concepts of satisfaction (referring to the power quality object) and of acceptable (referring to the cost object). The quality index, obtained in this way, reaches its maximum value using a deterministic scalar optimisation procedure, which leads the evolution of the project variables towards the best configuration. The optimisation method has been tested considering different kinds of site configurations with different values of the electrical loads, of the yearly power demand, of the distance from the grid and of the variable solar cells cost.
Adaptive Fuzzy Robust Control for a Class of Nonlinear Systems via Small Gain Theorem
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Xingjian Wang
2013-01-01
Full Text Available Practical nonlinear systems can usually be represented by partly linearizable models with unknown nonlinearities and external disturbances. Based on this consideration, we propose a novel adaptive fuzzy robust control (AFRC algorithm for such systems. The AFRC effectively combines techniques of adaptive control and fuzzy control, and it improves the performance by retaining the advantages of both methods. The linearizable part will be linearly parameterized with unknown but constant parameters, and the discontinuous-projection-based adaptive control law is used to compensate these parts. The Takagi-Sugeno fuzzy logic systems are used to approximate unknown nonlinearities. Robust control law ensures the robustness of closed-loop control system. A systematic design procedure of the AFRC algorithm by combining the backstepping technique and small-gain approach is presented. Then the closed-loop stability is studied by using small gain theorem, and the result indicates that the closed-loop system is semiglobally uniformly ultimately bounded.
Directory of Open Access Journals (Sweden)
Faten Baklouti
2016-01-01
Full Text Available The trajectory tracking of underactuated nonlinear system with two degrees of freedom is tackled by an adaptive fuzzy hierarchical sliding mode controller. The proposed control law solves the problem of coupling using a hierarchical structure of the sliding surfaces and chattering by adopting different reaching laws. The unknown system functions are approximated by fuzzy logic systems and free parameters can be updated online by adaptive laws based on Lyapunov theory. Two comparative studies are made in this paper. The first comparison is between three different expressions of reaching laws to compare their abilities to reduce the chattering phenomenon. The second comparison is made between the proposed adaptive fuzzy hierarchical sliding mode controller and two other control laws which keep the coupling in the underactuated system. The tracking performances of each control law are evaluated. Simulation examples including different amplitudes of external disturbances are made.
Directory of Open Access Journals (Sweden)
Hadi Sefidgar
2014-06-01
Full Text Available in this paper, a fuzzy logic control (FLC is proposed for maximum power point tracking (MPPT in wind turbine connection to Permanent Magnet Synchronous Generator (PMSG. The proposed fuzzy logic controller tracks the maximum power point (MPP by measurements the load voltage and current. This controller calculates the load power and sent through the fuzzy logic system. The main goal of this paper is design of the fuzzy logic controller in the model of DC-DC converter (boost converter. This method allows the MPPT controller output (duty cycle adjusts the voltage input to the converter to track the maximum power point of the wind generator.
Students Classification With Adaptive Neuro Fuzzy
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Mohammad Saber Iraji
2012-07-01
Full Text Available Identifying exceptional students for scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. In this article, we have tried to design an intelligent system which can separate and classify student according to learning factor and performance. a system is proposed through Lvq networks methods, anfis method to separate these student on learning factor . In our proposed system, adaptive fuzzy neural network(anfis has less error and can be used as an effective alternative system for classifying students
Development of Fuzzy Logic and Soft Computing Methodologies
Zadeh, L. A.; Yager, R.
1999-01-01
Our earlier research on computing with words (CW) has led to a new direction in fuzzy logic which points to a major enlargement of the role of natural languages in information processing, decision analysis and control. This direction is based on the methodology of computing with words and embodies a new theory which is referred to as the computational theory of perceptions (CTP). An important feature of this theory is that it can be added to any existing theory - especially to probability theory, decision analysis, and control - and enhance the ability of the theory to deal with real-world problems in which the decision-relevant information is a mixture of measurements and perceptions. The new direction is centered on an old concept - the concept of a perception - a concept which plays a central role in human cognition. The ability to reason with perceptions perceptions of time, distance, force, direction, shape, intent, likelihood, truth and other attributes of physical and mental objects - underlies the remarkable human capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Everyday examples of such tasks are parking a car, driving in city traffic, cooking a meal, playing golf and summarizing a story. Perceptions are intrinsically imprecise. Imprecision of perceptions reflects the finite ability of sensory organs and ultimately, the brain, to resolve detail and store information. More concretely, perceptions are both fuzzy and granular, or, for short, f-granular. Perceptions are f-granular in the sense that: (a) the boundaries of perceived classes are not sharply defined; and (b) the elements of classes are grouped into granules, with a granule being a clump of elements drawn together by indistinguishability, similarity. proximity or functionality. F-granularity of perceptions may be viewed as a human way of achieving data compression. In large measure, scientific progress has been, and continues to be
Fuzzy logic-based prognostic score for outcome prediction in esophageal cancer.
Wang, Chang-Yu; Lee, Tsair-Fwu; Fang, Chun-Hsiung; Chou, Jyh-Horng
2012-11-01
Given the poor prognosis of esophageal cancer and the invasiveness of combined modality treatment, improved prognostic scoring systems are needed. We developed a fuzzy logic-based system to improve the predictive performance of a risk score based on the serum concentrations of C-reactive protein (CRP) and albumin in a cohort of 271 patients with esophageal cancer before radiotherapy. Univariate and multivariate survival analyses were employed to validate the independent prognostic value of the fuzzy risk score. To further compare the predictive performance of the fuzzy risk score with other prognostic scoring systems, time-dependent receiver operating characteristic curve (ROC) analysis was used. Application of fuzzy logic to the serum values of CRP and albumin increased predictive performance for 1-year overall survival (AUC=0.773) compared with that of a single marker (AUC=0.743 and 0.700 for CRP and albumin, respectively), where the AUC denotes the area under curve. This fuzzy logic-based approach also performed consistently better than the Glasgow Prognostic Score (GPS) (AUC=0.745). Thus, application of fuzzy logic to the analysis of serum markers can more accurately predict the outcome for patients with esophageal cancer.
Methodological development of fuzzy-logic controllers from multivariable linear control.
Tso, S K; Fung, Y H
1997-01-01
It is the function of the design of a fuzzy-logic controller to determine the universes of discourse of the antecedents and the consequents, number of membership labels, distribution and shape of membership functions, rule formulation, etc. Much of the information is usually extracted from expert knowledge, operator experience, or heuristic thinking. It is hence difficult to mechanize the first-stage design of fuzzy-logic controllers using linguistic labels whose performance is no worse than that of conventional multivariable linear controllers such as state-feedback controllers, PID controllers, etc. In this paper, an original systematic seven-step linear-to-fuzzy (LIN2FUZ) algorithm is proposed for generating the labels, universes of discourse of the antecedents and the consequents, and fuzzy rules of ;basically linear' fuzzy-logic controllers, given the reference design of available conventional multivariable linear controllers. The functionally equivalent fuzzy-logic controllers can thus provide the sound basis for the further development to achieve performance beyond the capability or the conventional controllers. The validity and effectiveness of the proposed LIN2FUZ algorithm are demonstrated by a four-input one-output inverted pendulum system. PMID:18255897
Uzoka, Faith-Michael Emeka; Obot, Okure; Barker, Ken; Osuji, J
2011-07-01
The task of medical diagnosis is a complex one, considering the level vagueness and uncertainty management, especially when the disease has multiple symptoms. A number of researchers have utilized the fuzzy-analytic hierarchy process (fuzzy-AHP) methodology in handling imprecise data in medical diagnosis and therapy. The fuzzy logic is able to handle vagueness and unstructuredness in decision making, while the AHP has the ability to carry out pairwise comparison of decision elements in order to determine their importance in the decision process. This study attempts to do a case comparison of the fuzzy and AHP methods in the development of medical diagnosis system, which involves basic symptoms elicitation and analysis. The results of the study indicate a non-statistically significant relative superiority of the fuzzy technology over the AHP technology. Data collected from 30 malaria patients were used to diagnose using AHP and fuzzy logic independent of one another. The results were compared and found to covary strongly. It was also discovered from the results of fuzzy logic diagnosis covary a little bit more strongly to the conventional diagnosis results than that of AHP.
Construction of a fuzzy and all Boolean logic gates based on DNA
DEFF Research Database (Denmark)
M. Zadegan, Reza; Jepsen, Mette D E; Hildebrandt, Lasse;
2015-01-01
DNA locks on one DNA origami box structure enabled fuzzy logical operation that allows biosensing of complex molecular signals. Integrating logic gates with DNA origami systems opens a vast avenue to applications in the fields of nanomedicine for diagnostics and therapeutics.......Logic gates are devices that can perform logical operations by transforming a set of inputs into a predictable single detectable output. The hybridization properties, structure, and function of nucleic acids can be used to make DNA-based logic gates. These devices are important modules in molecular...... computing and biosensing. The ideal logic gate system should provide a wide selection of logical operations, and be integrable in multiple copies into more complex structures. Here we show the successful construction of a small DNA-based logic gate complex that produces fluorescent outputs corresponding...
Directory of Open Access Journals (Sweden)
Baghdad BELABES
2008-12-01
Full Text Available In this paper a hybrid controller combining a linear model following controller (LMFC and fuzzy logic control (FLC for speed vector controlled permanent magnet synchronous motor (PMSM is described on this study. The FLC is introduced at the adaptive mechanism level. First, an LMFC system is designed to allow the plant states to be controlled to follow the states produced by a reference model. In the nominal conditions, the model following is perfect and the adaptive mechanism based on the fuzzy logic is idle. Secondly, when parameter variations or external disturbances occur, an augmented signal will be generated by FLC mechanism to preserve the desired model following control performance. The effectiveness and robustness of the proposed controller is demonstrated by some simulation results.
Some Improvements on Active Queue Management Mechanism Based on Adaptive Fuzzy Control
Directory of Open Access Journals (Sweden)
Nguyen Kim Quoc
2015-11-01
Full Text Available Active queue management operates at network nodes to control the number of packets in the queue of nodes, by actively receiving packets when the queue is not full, removing packets when the queue is full or notifying bottlenecks even in the embryonic period of the bottlenecks due to to-be-full queue. In recent years, scientists have used fuzzy logic to improve queue management mechanisms. Overall, these improvements have used Mamdani fuzzy system with a fixed structure with triangular functions for input and output variables, so they do not adapt to the changing state of the network. We propose a adaptive fuzzy control (AFC model to improve the effectiveness of active queue management mechanisms.
Exploration of the Adaptive Neuro - Fuzzy Inference System Architecture and its Applications
Directory of Open Access Journals (Sweden)
Okereke Eze Aru
2016-09-01
Full Text Available In this paper we exhibited an architecture and essential learning process basic in fuzzy inference system and adaptive neuro fuzzy inference system which is a hybrid network implemented in framework of adaptive network. In genuine figuring environment, soft computing techniques including neural network, fuzzy logic algorithms have been generally used to infer a real choice utilizing given input or output information traits, ANFIS can build mapping taking into account both human learning and hybrid algorithms. This study includes investigation of ANFIS methodology. ANFIS procedure is utilized to display nonlinear functions, to control a standout amongst the most essential parameters of the impelling machine and anticipate a turbulent time arrangement, all yielding more viable, quicker result.
Modelling defeasible reasoning by means of adaptive logic games
P. Verdée
2011-01-01
In this article, I present a dynamic logic game for defeasible reasoning. I argue that, as far as defeasible reasoning is concerned, one should distinguish between practical and ideal rationality. Starting from the adaptive logic framework, I formalize both rationality notions by means of logic game
A fuzzy logic approach to modeling the underground economy in Taiwan
Yu, Tiffany Hui-Kuang; Wang, David Han-Min; Chen, Su-Jane
2006-04-01
The size of the ‘underground economy’ (UE) is valuable information in the formulation of macroeconomic and fiscal policy. This study applies fuzzy set theory and fuzzy logic to model Taiwan's UE over the period from 1960 to 2003. Two major factors affecting the size of the UE, the effective tax rate and the degree of government regulation, are used. The size of Taiwan's UE is scaled and compared with those of other models. Although our approach yields different estimates, similar patterns and leading are exhibited throughout the period. The advantage of applying fuzzy logic is twofold. First, it can avoid the complex calculations in conventional econometric models. Second, fuzzy rules with linguistic terms are easy for human to understand.
Modelling of the automatic stabilization system of the aircraft course by a fuzzy logic method
Mamonova, T.; Syryamkin, V.; Vasilyeva, T.
2016-04-01
The problem of the present paper concerns the development of a fuzzy model of the system of an aircraft course stabilization. In this work modelling of the aircraft course stabilization system with the application of fuzzy logic is specified. Thus the authors have used the data taken for an ordinary passenger plane. As a result of the study the stabilization system models were realised in the environment of Matlab package Simulink on the basis of the PID-regulator and fuzzy logic. The authors of the paper have shown that the use of the method of artificial intelligence allows reducing the time of regulation to 1, which is 50 times faster than the time when standard receptions of the management theory are used. This fact demonstrates a positive influence of the use of fuzzy regulation.
Fuzzy logic based anaesthesia monitoring systems for the detection of absolute hypovolaemia.
Mansoor Baig, Mirza; Gholamhosseini, Hamid; Harrison, Michael J
2013-07-01
Anaesthesia monitoring involves critical diagnostic tasks carried out amongst lots of distractions. Computers are capable of handling large amounts of data at high speed and therefore decision support systems and expert systems are now capable of processing many signals simultaneously in real time. We have developed two fuzzy logic based anaesthesia monitoring systems; a real time smart anaesthesia alarm system (RT-SAAM) and fuzzy logic monitoring system-2 (FLMS-2), an updated version of FLMS for the detection of absolute hypovolaemia. This paper presents the design aspects of these two systems which employ fuzzy logic techniques to detect absolute hypovolaemia, and compares their performances in terms of usability and acceptability. The interpretation of these two systems of absolute hypovolaemia was compared with clinicians' assessments using Kappa analysis, RT-SAAM K=0.62, FLMS-2 K=0.75; an improvement in performance by FLMS-2.
A novel fuzzy logic inference system for decision support in weaning from mechanical ventilation.
Kilic, Yusuf Alper; Kilic, Ilke
2010-12-01
Weaning from mechanical ventilation represents one of the most challenging issues in management of critically ill patients. Currently used weaning predictors ignore many important dimensions of weaning outcome and have not been uniformly successful. A fuzzy logic inference system that uses nine variables, and five rule blocks within two layers, has been designed and implemented over mathematical simulations and random clinical scenarios, to compare its behavior and performance in predicting expert opinion with those for rapid shallow breathing index (RSBI), pressure time index and Jabour' weaning index. RSBI has failed to predict expert opinion in 52% of scenarios. Fuzzy logic inference system has shown the best discriminative power (ROC: 0.9288), and RSBI the worst (ROC: 0.6556) in predicting expert opinion. Fuzzy logic provides an approach which can handle multi-attribute decision making, and is a very powerful tool to overcome the weaknesses of currently used weaning predictors.
Fuzzy Logic-based expert system for evaluating cake quality of freeze-dried formulations
DEFF Research Database (Denmark)
Trnka, Hjalte; Wu, Jian-Xiong; van de Weert, Marco;
2013-01-01
are needed. The aim of this study was to develop a fuzzy logic system based on image analysis (IA) for analyzing cake quality. Freeze-dried samples with different visual quality attributes were prepared in well plates. Imaging solutions together with image analytical routines were developed for extracting...... critical visual features such as the degree of cake collapse, glassiness, and color uniformity. On the basis of the IA outputs, a fuzzy logic system for analysis of these freeze-dried cakes was constructed. After this development phase, the system was tested with a new screening well plate. The developed...... fuzzy logic-based system was found to give comparable quality scores with visual evaluation, making high-throughput classification of cake quality possible....
Analyses and Simulation of Fuzzy Logic Control for Suspension System of a Track Vehicle
Institute of Scientific and Technical Information of China (English)
YU Yang; WEI Xue-xia; ZHANG Yong-fa
2008-01-01
The vibration caused by terrible road excitation affects the ride quality and safety of track vehicles. The vibration control of suspension systems is a very important factor for modern track vehicles. A fuzzy logic control for suspension system of a track vehicle is presented. A mechanical model and a system of differential equations of motion taking account of the mass of loading wheel are established. Then the fuzzy logic control is applied to control the vibration of suspension system of track vehicles for sine signal and random road surfaces. Numerical simulation shows that the maximum acceleration of suspension system can be reduced to 44% of the original value for sine signal road surface, and the mean square root of acceleration of suspension system can be reduced to 21% for random road surface. Therefore, the proposed fuzzy logic control is an efficient method for the suspension systems of track vehicles.
A real time fuzzy logic power management strategy for a fuel cell vehicle
International Nuclear Information System (INIS)
Highlights: • We present a real time fuzzy logic power management strategy. • This strategy is applied to hybrid electric vehicle dynamic model. • Three configurations evaluated during a drive cycle. • The hydrogen consumption is analysed for the three configurations. - Abstract: This paper presents real time fuzzy logic controller (FLC) approach used to design a power management strategy for a hybrid electric vehicle and to protect the battery from overcharging during the repetitive braking energy accumulation. The fuel cell (FC) and battery (B)/supercapacitor (SC) are the primary and secondary power sources, respectively. This paper analyzes and evaluates the performance of the three configurations, FC/B, FC/SC and FC/B/SC during real time driving conditions and unknown driving cycle. The MATLAB/Simulink and SimPowerSystems software packages are used to model the electrical and mechanical elements of hybrid vehicles and implement a fuzzy logic strategy
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
Tahmasebi, Pejman; Hezarkhani, Ardeshir
2012-05-01
The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.
Design of a fuzzy logic based controller for neutron power regulation
International Nuclear Information System (INIS)
This work presents a fuzzy logic controller design for neutron power control, from its source to its full power level, applied to a nuclear reactor model. First, we present the basic definitions on fuzzy sets as generalized definitions of the crisp (non fuzzy) set theory. Likewise, we define the basic operations on fuzzy sets (complement, union, and intersection), and the operations on fuzzy relations such as projection and cylindrical extension operations. Furthermore, some concepts of the fuzzy control theory, such as the main modules of the typical fuzzy controller structure and its internal variables, are defined. After the knowledge base is obtained by simulation of the reactor behavior, where the controlled system is modeled by a simple nonlinear reactor model, this model is used to infer a set of fuzzy rules for the reactor response to different insertions of reactivity. The reduction of the response time, using fuzzy rule based controllers on this reactor, is possible by adjusting the output membership functions, by selecting fuzzy rule sets, or by increasing the number of crisp inputs to the fuzzy controller. System characteristics, such as number of rules, response times, and safety parameter values, were considered in the evaluation of each controller merits. Different fuzzy controllers are designed to attain the desired power level, to maintain a constant level for long periods of time, and to keep the reactor away from a shutdown condition. The basic differences among the controllers are the number of crisp inputs and the novel implementation of a crisp power level-based selection of different sets of output membership functions. Simulation results highlight, mainly: (1) A decrease of the response variations at low power level, and (2) a decrease in the time required to attain the desired neutron power. Finally, we present a comparative study of different fuzzy control algorithms applied to a nuclear model. (Author)
Using Fuzzy Logic in Hybrid Multihop Wireless Networks
Directory of Open Access Journals (Sweden)
A.J. Yuste
2010-08-01
Full Text Available In order to extend the use of Mobile ad hoc networks (MANET to commercial applications, it isnecessary to provide a mechanism to integrate this kind of networks into the Internet. In this way,MANETs could be utilized in visiting theme parks, commercial centers and military scenarios. Theinterconnection of MANETs and the Internet is supported by a Gateway. The gateway is responsible forinforming about some configuration parameters as well as for enabling the creation of the routes to theInternet in the MANET nodes. For these tasks, several control messages are generated. The method inwhich these messages are originated defines the existing Integration Supports for MANETs. In particular,under the hybrid Global Connectivity support, the Gateway generates periodic Modified RouterAdvertisements (MRA which are broadcast in an area close to the Gateway. The optimum values todefine the periodicity of these messages and the diameter (number of hops of the area in which they arepropagated depend on the network conditions. Therefore, an automatic and dynamic algorithm isrecommended to be implemented in the Gateway to adjust these two parameters. In this sense, this paperpresents a technique by which the interval of emission of the MRA messages is controlled by a fuzzysystem. The fuzzy system captures several network conditions such as the link stability or the number ofsources. The simulation results show that the proposed scheme outperforms other adaptive approachesfor the gateway discovery in MANETs.
Fuzzy Logic: Toward Measuring Gottfredson's Concept of Occupational Social Space.
Hesketh, Beryl; And Others
1989-01-01
Investigated the application of fuzzy graphic rating scale to measurement of preferences for occupational sex type, prestige, and interests using Gottfredson's concept of occupational social space. Reported reliability and validity data with illustrative examples of respondents' interpretations of their own fuzzy ratings. Outlined counseling and…
Traffic Forecasting Model Based on Takagi-Sugeno Fuzzy Logical System
Institute of Scientific and Technical Information of China (English)
WANG Wei-gong; LI Zheng; CHENG Mei-ling
2005-01-01
The local multiple regression fuzzy(LMRF)model based on Takagi-Sugeno fuzzy logical system and its application in traffic forecasting is proposed. Besides its prediction accuracy is testified and the model is proved much better than conventional forecasting methods. According to the regional traffic system, the model perfectly states the complex non-linear relation of the traffic and the local social economy. The model also efficiently deals with the system lack of enough data.
Application of Fuzzy Logic Controller to Level Control of Twin-Roll Strip Casting
Institute of Scientific and Technical Information of China (English)
QI Chun-yu; DI Hong-shuang; ZHANG Xiao-ming; GAO De-fu
2003-01-01
An intelligent fuzzy-PID controller consisting of fuzzy logic controller and PID controller was developed to control the molten steel level of twin-roll strip caster. Additionally, a feedforward differential PID controller was used for stopper position control in order to avoid differential kick. It is proved by simulation that the proposed intelligent controller is able to obtain zero steady state error asymptotically and the control system is robust due to its fuggy behavior of the controller.
A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables
Directory of Open Access Journals (Sweden)
Jaw-Kuen Shiau
2015-04-01
Full Text Available Maximum power point tracking (MPPT is one of the key functions of the solar power management system in solar energy deployment. This paper investigates the design of fuzzy-logic-based solar power MPPT algorithms using different fuzzy input variables. Six fuzzy MPPT algorithms, based on different input variables, were considered in this study, namely (i slope (of solar power-versus-solar voltage and changes of the slope; (ii slope and variation of the power; (iii variation of power and variation of voltage; (iv variation of power and variation of current; (v sum of conductance and increment of the conductance; and (vi sum of angles of arctangent of the conductance and arctangent of increment of the conductance. Algorithms (i–(iv have two input variables each while algorithms (v and (vi use a single input variable. The fuzzy logic MPPT function is deployed using a buck-boost power converter. This paper presents the details of the determinations, considerations of the fuzzy rules, as well as advantages and disadvantages of each MPPT algorithm based upon photovoltaic (PV cell properties. The range of the input variable of Algorithm (vi is finite and the maximum power point condition is well defined in steady condition and, therefore, it can be used for multipurpose controller design. Computer simulations are conducted to verify the design.
Planning by Case-Based Reasoning Based on Fuzzy Logic
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Atmani Baghdad
2013-05-01
Full Text Available The treatment of complex systems often requires the manipulation of vague, imprecise and uncertain information. Indeed, the human being is c ompetent in handling of such systems in a natural way. Instead of thinking in mathematical te rms, humans describes the behavior of the system by language proposals. In order to represent this type of information, Zadeh proposed to model the mechanism of human thought by approximate reasoning based on linguistic variables. He introduced the theory of fuzzy sets i n 1965, which provides an interface between language and digital worlds. In this paper, we prop ose a Boolean modeling of the fuzzy reasoning that we baptized Fuzzy-BML and uses the c haracteristics of induction graph classification. Fuzzy-BML is the process by which t he retrieval phase of a CBR is modelled not in the conventional form of mathematical equations, but in the form of a database with membership functions of fuzzy rules.
A formalization of commonsense reasoning based on fuzzy logic
Zadeh, L. A.
1985-01-01
The basic idea underlying the approach outlined in this paper is that commonsense knowledge may be regarded as a collection of dispositions, that is, propositions which are preponderantly, but not necessarily always, true. Technically, a disposition may be interpreted as a proposition with implicit fuzzy quantifiers, e.g., most, almost all, usually, often, etc. For example, a disposition such as Swedes are blond may be interpreted as most Swedes are blond. For purposes of inference from commonsense knowledge, the conversion of a disposition into a proposition with explicit fuzzy quantifiers sets the stage for an application of syllogistic reasoning in which the premises are allowed to be of the form Q A's are B's, where A and B are fuzzy predicates and Q is a fuzzy quantifier. In general, the conclusion yielded by such reasoning is a proposition which may be converted into a disposition through the suppression of fuzzy quantifiers.
Traffic Signals Control with Adaptive Fuzzy Controller in Urban Road Network
Institute of Scientific and Technical Information of China (English)
LI Yan; FAN Xiao-ping
2008-01-01
An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network.The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level.The control level decides the signal tunings in an intersection with a fuzzy logic controller.The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one.Consequently the system performances are improved.A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections.So the AFC combined with the WCC can be applied in a road network for signal timings.Simulations of the AFC on a real traffic scenario have been conducted.Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.
Robust Fault Detection Using Robust Z1 Estimation and Fuzzy Logic
Curry, Tramone; Collins, Emmanuel G., Jr.; Selekwa, Majura; Guo, Ten-Huei (Technical Monitor)
2001-01-01
This research considers the application of robust Z(sub 1), estimation in conjunction with fuzzy logic to robust fault detection for an aircraft fight control system. It begins with the development of robust Z(sub 1) estimators based on multiplier theory and then develops a fixed threshold approach to fault detection (FD). It then considers the use of fuzzy logic for robust residual evaluation and FD. Due to modeling errors and unmeasurable disturbances, it is difficult to distinguish between the effects of an actual fault and those caused by uncertainty and disturbance. Hence, it is the aim of a robust FD system to be sensitive to faults while remaining insensitive to uncertainty and disturbances. While fixed thresholds only allow a decision on whether a fault has or has not occurred, it is more valuable to have the residual evaluation lead to a conclusion related to the degree of, or probability of, a fault. Fuzzy logic is a viable means of determining the degree of a fault and allows the introduction of human observations that may not be incorporated in the rigorous threshold theory. Hence, fuzzy logic can provide a more reliable and informative fault detection process. Using an aircraft flight control system, the results of FD using robust Z(sub 1) estimation with a fixed threshold are demonstrated. FD that combines robust Z(sub 1) estimation and fuzzy logic is also demonstrated. It is seen that combining the robust estimator with fuzzy logic proves to be advantageous in increasing the sensitivity to smaller faults while remaining insensitive to uncertainty and disturbances.
Directory of Open Access Journals (Sweden)
Angelo A. Beltran Jr.
2014-03-01
Full Text Available —The control and operation of electronic systems relies and depends on the availability of the power supply. Rechargeable batteries have been more pervasively used as the energy storage and power source for various electrical and electronic systems and devices, such as communication systems, electronic devices, renewable power systems, electric vehicles, etc. However, the rechargeable batteries are subjected to the availability of the external power source when it is drained out. Because of the concern of battery life, environmental pollution and a possible energy crisis, the renewable solar energy has received an increasing attention in recent years. A fuzzy logic control based grid tied uninterruptible power supply integrating renewable solar energy can be used for electrical and electronic systems to produce power generation. This paper presents the design and implementation of fuzzy logic control based grid tied uninterruptible power supply integrating the renewable solar power energy system. The uninterruptible power supply (UPS system is characterized by the rechargeable battery that is connected with the Photovoltaic Panel through the DC/DC converter, the utility AC through the AC/DC converter and the load is connected through the DC/AC converter. The whole operation is controlled by the fuzzy logic algorithm. A complete hardware prototype system model of the fuzzy logic control based on the grid tied uninterruptible power supply integrating with the renewable solar energy is designed and implemented. The operation and effectiveness of the proposed system is then demonstrated by the actual and real time implementation of the fuzzy logic control grid tied operation uninterruptible power supply integrating renewable solar energy connected to the rechargeable battery bank and a PIC microcontroller platform for fuzzy logic control and operation
Detection of Stator Winding Fault in Induction Motor Using Fuzzy Logic with Optimal Rules
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Hamid Fekri Azgomi
2013-04-01
Full Text Available Induction motors are critical components in many industrial processes. Therefore, swift, precise and reliable monitoring and fault detection systems are required to prevent any further damages. The online monitoring of induction motors has been becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose traction motor faults. This paper presents a simple method for the detection of stator winding faults (which make up 38% of induction motor failures based on monitoring the line/terminal current amplitudes. In this method, fuzzy logic is used to make decisions about the stator motor condition. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the fuzzy inference. Simulation results are presented to verify the accuracy of motor’s fault detection and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis.
FUZZY LOGIC CONTROLLER AS MODELING TOOL FOR THE BURNING PROCESS OF A CEMENT PRODUCTION PLANT
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P.B. Osofisan
2012-01-01
Full Text Available
ENGLISH ABSTRACT: A comprehensive optimisation of the cement production process presents a problem since the input variables as well as the output variables are non-linear, interdependent and contain uncertainties. To arrive at a solution, a Fuzzy Logic controller has been designed to achieve a well-defined relationship between the main and vital variables through the instrumentality of a Fuzzy Model. The Fuzzy Logic controller has been simulated on a digital computer using MATLAB 5.0 Fuzzy Logic Tool Box, using data from a local cement production plant.
AFRIKAANSE OPSOMMING: Die omvattende optimisering van 'n proses wat sement vervaardig, word beskryf deur nie-linieêre inset- en uitsetveranderlikes wat onderling afhanklik is, en ook van onsekere aard is. Om 'n optimum oplossing te verkry, word 'n Wasigheidsmodel gebruik. Die model word getoets deur gebruik te maak van die MATLAB 5.0 Fuzzy Logic Tool Box en data vanaf 'n lokale sementvervaardigingsaanleg.
PC based speed control of dc motor using fuzzy logic controller
Energy Technology Data Exchange (ETDEWEB)
Mandal, S.K.; Kanphade, R.D.; Lavekar, K.P.
1998-07-01
The dc motor is extensively used as constant speed drive in textile mills, paper mills, printing press, etc.. If the load and supply voltage are time varying, the speed will be changed. Since last few decades the conventional PID controllers are used to maintain the constant speed by controlling the duty ratio of Chopper. Generally, four quadrant chopper is used for regenerative braking and reverse motoring operation. Fuzzy Logic is newly introduced in control system. Fuzzy Control is based on Fuzzy Logic, a logical system which is too much closer in spirit to human thinking and natural language. The Fuzzy Logic Controller (FLC) provides a linguistic control strategy based on knowledge base of the system. Firstly, the machine is started very smoothly from zero to reference speed in the proposed scheme by increasing the duty ratio. Then change and rate of change of speed (dN, dN/dt), change and rate of change input voltage (dV, dV/dt) and load current are input to FLC. The new value of duty ratio is determined from the Fuzzy rule base and defuzzification method. The chopper will be 'ON' according to new duty ratio to maintain the constant speed. The dynamic and steady state performance of the proposed system is better than conventional control system. In this paper mathematical simulation and experimental implementation are carried out to investigate the drive performance.
Fuzzy logic and information fusion to commemorate the 70th birthday of Professor Gaspar Mayor
Sastre, Joan
2016-01-01
This book offers a timely report on key theories and applications of soft-computing. Written in honour of Professor Gaspar Mayor on his 70th birthday, it primarily focuses on areas related to his research, including fuzzy binary operators, aggregation functions, multi-distances, and fuzzy consensus/decision models. It also discusses a number of interesting applications such as the implementation of fuzzy mathematical morphology based on Mayor-Torrens t-norms. Importantly, the different chapters, authored by leading experts, present novel results and offer new perspectives on different aspects of Mayor’s research. The book also includes an overview of evolutionary fuzzy systems, a topic that is not one of Mayor’s main areas of interest, and a final chapter written by the Spanish pioneer in fuzzy logic, Professor E. Trillas. Computer and decision scientists, knowledge engineers and mathematicians alike will find here an authoritative overview of key soft-computing concepts and techniques.
FUZZY LOGIC APPLICATION FOR ECONOMICAL-FINANCIAL PERFORMANCE EVALUATION OF PRODUCTION COOPERATIVES.
Directory of Open Access Journals (Sweden)
Jaciara Treter
2010-04-01
Full Text Available In this work a methodology based on Fuzzy Logic was developed for classification and evaluation of Economical-financial Performance of production cooperatives. Continuous scales were introduced and Fuzzy Logic based sub-models, were created to evaluate Own Capital Return, Payment Capacity and Capital Structure of a production cooperative. By applying the developed model, the Performance dynamics of two production cooperatives (Coopermil and Cotricampo was analyzed in a 5 year’s period.. Evaluations carried out through numeric simulation in order to analyse the influence of several indicators on a production cooperative performance and expert evaluations showed good correlation.
Nonlinear Aerodynamic Modeling From Flight Data Using Advanced Piloted Maneuvers and Fuzzy Logic
Brandon, Jay M.; Morelli, Eugene A.
2012-01-01
Results of the Aeronautics Research Mission Directorate Seedling Project Phase I research project entitled "Nonlinear Aerodynamics Modeling using Fuzzy Logic" are presented. Efficient and rapid flight test capabilities were developed for estimating highly nonlinear models of airplane aerodynamics over a large flight envelope. Results showed that the flight maneuvers developed, used in conjunction with the fuzzy-logic system identification algorithms, produced very good model fits of the data, with no model structure inputs required, for flight conditions ranging from cruise to departure and spin conditions.
Fuzzy-logic-based safety verification framework for nuclear power plants.
Rastogi, Achint; Gabbar, Hossam A
2013-06-01
This article presents a practical implementation of a safety verification framework for nuclear power plants (NPPs) based on fuzzy logic where hazard scenarios are identified in view of safety and control limits in different plant process values. Risk is estimated quantitatively and compared with safety limits in real time so that safety verification can be achieved. Fuzzy logic is used to define safety rules that map hazard condition with required safety protection in view of risk estimate. Case studies are analyzed from NPP to realize the proposed real-time safety verification framework. An automated system is developed to demonstrate the safety limit for different hazard scenarios.
Energy Technology Data Exchange (ETDEWEB)
Larbes, C.; Ait Cheikh, S.M.; Obeidi, T.; Zerguerras, A. [Laboratoire des Dispositifs de Communication et de Conversion Photovoltaique, Departement d' Electronique, Ecole Nationale Polytechnique, 10, Avenue Hassen Badi, El Harrach, Alger 16200 (Algeria)
2009-10-15
This paper presents an intelligent control method for the maximum power point tracking (MPPT) of a photovoltaic system under variable temperature and irradiance conditions. First, for the purpose of comparison and because of its proven and good performances, the perturbation and observation (P and O) technique is briefly introduced. A fuzzy logic controller based MPPT (FLC) is then proposed which has shown better performances compared to the P and O MPPT based approach. The proposed FLC has been also improved using genetic algorithms (GA) for optimisation. Different development stages are presented and the optimized fuzzy logic MPPT controller (OFLC) is then simulated and evaluated, which has shown better performances. (author)
Genetic Fuzzy Logic Control Technique for a Mobile Robot Tracking a Moving Target
Directory of Open Access Journals (Sweden)
Karim Benbouabdallah
2013-01-01
Full Text Available Target tracking is a crucial function for an autonomous mobile robot navigating in unknown environments. This paper presents a mobile robot target tracking approach based on artificial intelligence techniques. The proposed controller calculates both the mobile robot linear and angular velocities from the distance and angle that separate it to the moving target. The controller was designed using fuzzy logics theory and then, a genetic algorithm was applied to optimize the scaling factors of the fuzzy logic controller for better accuracy and smoothness of the robot trajectory. Simulation results illustrate that the proposed controller leads to good performances in terms of computational time and tracking errors convergence.
Intelligent learning technique based-on fuzzy logic for multi-robot path planning
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Soccer robot system is a tremendously challenging intelligent system developed to mimic human soc cer competition based on the multi-discipline research: robotics, intelligent control, computer vision, etc. robot path planning strategy is a very important subject concerning to the performance and intelligence degree of the multi-robot system. Therefore, this paper studies the path planning strategy of soccer system by using fuzzy log ic. After setting up two fuzziers and two sorts of fuzzy rules for soccer system, fuzzy logic is applied to work space partition and path revision. The experiment results show that this technique can well enhance the perform ance and intelligence degree of the system.
Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms
Siddique, Nazmul
2014-01-01
Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller. The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...
Marwan Rosyadi; Muyeen, S.M.; Rion Takahashi; Junji Tamura
2012-01-01
In this paper, a design fuzzy logic controller for a variable speed permanent magnet wind generator connected to a grid system through a LC-filter is proposed. A new current control method of grid side conversion is developed by integrating the fuzzy controller, in which both active and reactive power, delivered to a power grid system, is controlled effectively. The fuzzy logic controller is designed to adjust the gain parameters of the PI controllers under any operating conditions, so that t...
Directory of Open Access Journals (Sweden)
Dr. Shailja Shukla
2010-03-01
coordinate to tackle the parameter uncertainties of the system, and to obtain good WMR dynamic response.[1] Here we Apply 3D line following mythology. It transforms from 3D to 2D and also maps the image coordinates and vice versa, leading to the improved accuracy of the WMR position. The fuzzy logic Controller may give a good command signal; moreover we can find a highly accurate plant model to design the controller taking into account The unknown factors like friction and dynamic environment.This design, in its modularity, creates a portable autonomous fuzzy logic controller applicable to any mobile vehicle with only minor adaptations.
Fuzzy Logic Expert System-A Prescriptive Approach
Wahid palash; Md. Fuzlul Karim; Sumaiya Sultana Rika; Md. Faruque Islam
2015-01-01
A membership value of a fuzzy set has been defined as the degree to which an element belongs to this fuzzy set. It is possible to give other interpretations to the membership degree like a certainty factor, a degree of truth, a degree of satisfaction and a degree of possibility. In 1978 Zadeh extended the fuzzy set theory to a possibility theory where the membership values are considered as degrees of possibility. Zadeh justies the possibility theory by the fact that the imprecision that i...
Using fuzzy logic analysis for siting decisions of infiltration trenches for highway runoff control.
Ki, Seo Jin; Ray, Chittaranjan
2014-09-15
Determining optimal locations for best management practices (BMPs), including their field considerations and limitations, plays an important role for effective stormwater management. However, these issues have been often overlooked in modeling studies that focused on downstream water quality benefits. This study illustrates the methodology of locating infiltration trenches at suitable locations from spatial overlay analyses which combine multiple layers that address different aspects of field application into a composite map. Using seven thematic layers for each analysis, fuzzy logic was employed to develop a site suitability map for infiltration trenches, whereas the DRASTIC method was used to produce a groundwater vulnerability map on the island of Oahu, Hawaii, USA. In addition, the analytic hierarchy process (AHP), one of the most popular overlay analyses, was used for comparison to fuzzy logic. The results showed that the AHP and fuzzy logic methods developed significantly different index maps in terms of best locations and suitability scores. Specifically, the AHP method provided a maximum level of site suitability due to its inherent aggregation approach of all input layers in a linear equation. The most eligible areas in locating infiltration trenches were determined from the superposition of the site suitability and groundwater vulnerability maps using the fuzzy AND operator. The resulting map successfully balanced qualification criteria for a low risk of groundwater contamination and the best BMP site selection. The results of the sensitivity analysis showed that the suitability scores were strongly affected by the algorithms embedded in fuzzy logic; therefore, caution is recommended with their use in overlay analysis. Accordingly, this study demonstrates that the fuzzy logic analysis can not only be used to improve spatial decision quality along with other overlay approaches, but also is combined with general water quality models for initial and refined
Indian Academy of Sciences (India)
Rohan Kumar; R Anbalagan
2015-03-01
A comprehensive study for the identification of landslide susceptible zones using landslide frequency ratio and fuzzy logic in GIS environment is presented for Tehri reservoir rim region (Uttarakhand, India). Temporal remote sensing data was used to prepare important landslide causative factor layers and landslide inventory. Primary and secondary topographic attributes namely slope, aspect, relative relief, profile curvature, topographic wetness index, and stream power index, were derived from digital elevation model. Landslide frequency ratio technique was adopted to correlate factors with landslides. Further, fuzzy logic method was applied for the integration of factors (causative factor) to map landslide susceptible zones. Normalized landslide frequency ratio value was used for the fuzzy membership function and different fuzzy operators were considered for the preparation of landslide susceptibility/hazard index map. The factors considered in this study were found to be carrying a wide range of information. Accordingly, a methodology was evolved to integrate the factors using combined fuzzy gamma and fuzzy OR operation. Fuzzy gamma integration was performed for six different gamma values (range: 0–1). Gamma value of 0.95 was selected for the preparation of final susceptibility map. Landslide susceptibility index map was divided into the following five hazard zones – very low, low, moderate, high, and very high – on the basis of natural break classification. Validation of the model was performed by using cumulative percentage curve technique. Area under curve value of cumulative percentage curve of proposed landslide susceptibility map (gamma = 0.95) was found to be 0.834 and it can be said that 83.4% accuracy was achieved by applying combined fuzzy logic and landslide frequency ratio method.
Fuzzy, crisp, and human logic in e-commerce marketing data mining
Hearn, Kelda L.; Zhang, Yanqing
2001-03-01
In today's business world there is an abundance of available data and a great need to make good use of it. Many businesses would benefit from examining customer habits and trends and making marketing and product decisions based on that analysis. However, the process of manually examining data and making sound decisions based on that data is time consuming and often impractical. Intelligent systems that can make judgments similar to human judgments are sorely needed. Thus, systems based on fuzzy logic present themselves as an option to be seriously considered. The work described in this paper attempts to make an initial comparison between fuzzy logic and more traditional hard or crisp logic to see which would make a better substitute for human intervention. In this particular case study, customers are classified into categories that indicate how desirable the customer would be as a prospect for marketing. This classification is based on a small set of customer data. The results from these investigations make it clear that fuzzy logic is more able to think for itself and make decisions that more closely match human decision and is therefore significantly closer to human logic than crisp logic.
Land cover classification of Landsat 8 satellite data based on Fuzzy Logic approach
Taufik, Afirah; Sakinah Syed Ahmad, Sharifah
2016-06-01
The aim of this paper is to propose a method to classify the land covers of a satellite image based on fuzzy rule-based system approach. The study uses bands in Landsat 8 and other indices, such as Normalized Difference Water Index (NDWI), Normalized difference built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) as input for the fuzzy inference system. The selected three indices represent our main three classes called water, built- up land, and vegetation. The combination of the original multispectral bands and selected indices provide more information about the image. The parameter selection of fuzzy membership is performed by using a supervised method known as ANFIS (Adaptive neuro fuzzy inference system) training. The fuzzy system is tested for the classification on the land cover image that covers Klang Valley area. The results showed that the fuzzy system approach is effective and can be explored and implemented for other areas of Landsat data.
Studies on Fuzzy Logic and Dispositions for Medical Diagnosis
Prof. Sripati Mukhopadhyay; Jyotirmoy Ghosh
2011-01-01
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, ...
Enhancement in Edge Detection by Threshold Value in Sobel Operator by using Fuzzy Logic
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Prof. (Dr. S. K. Singh
2014-07-01
Full Text Available The key uncertainty of the edge detection algorithm is Threshold decision in image processing. Fuzzy logic for threshold and generated threshold values are used for edge detection, the results of this method is compared to many well known edge detector. However, the value of the input parameters providing the appreciable results in the proposed detector is more stable than classical operator.
A "fuzzy"-logic language for encoding multiple physical traits in biomolecules.
Warszawski, Shira; Netzer, Ravit; Tawfik, Dan S; Fleishman, Sarel J
2014-12-12
To carry out their activities, biological macromolecules balance different physical traits, such as stability, interaction affinity, and selectivity. How such often opposing traits are encoded in a macromolecular system is critical to our understanding of evolutionary processes and ability to design new molecules with desired functions. We present a framework for constraining design simulations to balance different physical characteristics. Each trait is represented by the equilibrium fractional occupancy of the desired state relative to its alternatives, ranging from none to full occupancy, and the different traits are combined using Boolean operators to effect a "fuzzy"-logic language for encoding any combination of traits. In another paper, we presented a new combinatorial backbone design algorithm AbDesign where the fuzzy-logic framework was used to optimize protein backbones and sequences for both stability and binding affinity in antibody-design simulation. We now extend this framework and find that fuzzy-logic design simulations reproduce sequence and structure design principles seen in nature to underlie exquisite specificity on the one hand and multispecificity on the other hand. The fuzzy-logic language is broadly applicable and could help define the space of tolerated and beneficial mutations in natural biomolecular systems and design artificial molecules that encode complex characteristics. PMID:25311857
A fuzzy-logic based MPPT method for stand-alone wind turbine system
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Huynh Quang Minh
2014-09-01
Full Text Available In this paper, a fuzzy-logic based maximum power point tracking (MPPT method for a standalone wind turbine system is proposed. Hill climb searching (HCS method is usedto achieve the MPPT of thepermanent magnet synchronous generator (PMSG driven wind turbine system. Simulation results will show the effectiveness of the proposed method in various operating conditions.
ENERGY EFFICIENT FLOW AND LEVEL CONTROL IN A HYDRO POWER PLANT USING FUZZY LOGIC
Directory of Open Access Journals (Sweden)
A. Selwin Mich Priyadharson
2014-01-01
Full Text Available The main objective and an innovative design of this work is to improve the energy efficiency by controlling the variables flow and level in a hydroelectric power plant using Programmable Logic Control (PLC-Human Machine Interface (HMI and fuzzy logic approach. This project will focus on design and development of flow and level controller for small scale hydro generating units by implementing gate control based on PLC-HMI and Fuzzy Logic Control (FLC. So far there is no other better performing control scheme, with uncomplicated approach, in order to match and satisfy the dynamic changes in load demand. In this project, FLC will be applied to flow and level control for small scale hydro generating units is proposed. A lab scale experimental setup is made-up as prototype model for flow and level control and simulation outputs were achieved, using PLC-HMI based fuzzy controller scheme. The hardware set up is designed with 5 stages in the tank 1 and 2 stages in the tank 2. Based on the outputs of the level sensors from tanks 1 and 2, the ladder logic will perform. B&R Industrial Automation PLC inbuilt with 24 digital inputs and provides 16 potential free outputs is used to perform control action. Finally, the performance of the proposed scheme is evaluated by simulation results by comparing with conventional controllers output using the data collected from the hydroelectric power plant. The merits of the proposed Fuzzy scheme over the conventional method are spotlighted.
Controlling the power output of a nuclear reactor with fuzzy logic
Ruan, D.; Wal, A.J. van der
1998-01-01
The application of fuzzy logic control (FLC) in the domain of nuclear industry presents a tremendous challenge. The main reason for this is the public awareness of the risks of nuclear reactors and the very strict safety regulations in force for nuclear power plants. The very same regulations preven
Process optimization of citric acid production from aspergillus niger using fuzzy logic design
International Nuclear Information System (INIS)
The inherent non-linearity of citric acid fermentation from Aspergillus niger renders its control difficult, so there is a need to fine-tune the bioreactor performance for maximum production of citric acid in batch culture. For this, fuzzy logic is becoming a popular tool to handle non-linearity of a batch process. The present manuscript deals with fuzzy logic control of citric acid accretion by A. niger in a stirred tank reactor using blackstrap sugarcane molasses as a basal fermentation medium. The customary batches were termed as control while those under fuzzy logic were experimental. The performance of fuzzy logic control of stirred tank reactor was found to be very encouraging for enhanced production of citric acid. The comparison of kinetic parameters showed improved citrate synthase ability of experimental culture (Yp/x = 7.042 g/g). When the culture grown on 150 g/l carbohydrates was monitored for Qp, Qs and Yp/s, there was significant enhancement in these variables over the control. Specific productivity of culture (qp = 0.070 g/g cells/h) was several fold increased. The enthalpy (HD = 70.5 kJ/mol) and entropy of activation (S = -144 J/mol/K) of enzyme for citric acid biosynthesis, free energies for transition state formation and substrate binding for sucrose hydrolysis of experimental were substantially improved. (author)
Process optimization of citric acid production from aspergillus niger using fuzzy logic design
International Nuclear Information System (INIS)
The inherent non-linearity of citric acid fermentation from Aspergillus niger renders its control difficult, so there is a need to fine-tune the bioreactor performance for maximum production of citric acid in batch culture. For this, fuzzy logic is becoming a popular tool to handle non-linearity of a batch process. The present manuscript deals with fuzzy logic control of citric acid accretion by A. niger in a stirred tank reactor using blackstrap sugarcane molasses as a basal fermentation medium. The customary batches were termed as 'control' while those under fuzzy logic were 'experimental'. The performance of fuzzy logic control of stirred tank reactor was found to be very encouraging for enhanced production of citric acid. The comparison of kinetic parameters showed improved citrate synthase ability of experimental culture (Yp/x = 7.042 g/g). When the culture grown on 150 g/l carbohydrates was monitored for Qp, Qs and Yp/s, there was significant enhancement in these variables over the control. Specific productivity of culture (qp = 0.070 g/g cells/h) was several fold increased. The enthalpy (HD = 70.5 kJ/mol) and entropy of activation (S = -144 J/mol/K) of enzyme for citric acid biosynthesis, free energies for transition state formation and substrate binding for sucrose hydrolysis of experimental were substantially improved. (author)
FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM
The paper describes a variable-speed wind generation system where fuzzy logic principles are used to optimize efficiency and enhance performance control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which either pump...
DEFF Research Database (Denmark)
Abootorabi Zarchi, Hossein; Jónsson, Ragnar Ingi; Blanke, Mogens
2009-01-01
detection and hypothesis testing applied on activity sensor data. This paper enhances earlier method by employing fuzzy logic technique to classify oestrus alerts from a model-based detection method utilising the cyclic nature of oestrus. Based on the distribution of the trait period since last detected...
Q-V droop control using fuzzy logic and reciprocal characteristic
DEFF Research Database (Denmark)
Wanga, Lu; Hu, Yanting; Chen, Zhe
2014-01-01
electric power at distributed voltage level, which not only is an autonomous system, but also can be connected to the main grid. To improve the stability and controllability of the power grid, this paper presents an improved Q-V droop control strategy using fuzzy logic controller and reciprocal...
Fuzzy Logic Based MPPT Controller for a Small Wind Turbine System
DEFF Research Database (Denmark)
Petrila, Diana; Blaabjerg, Frede; Muntean, Nicolae;
2012-01-01
This paper describes the design of a maximum power point tracking (MPPT) strategy for a variable speed, small scale, wind turbine systems based on a fuzzy logic controller (FLC). The FLC has as input variables the change in mechanical power (ΔPm), the change in rotor speed (Δω), and the sign of ΔPm...
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei; Wang, Chengshan;
2015-01-01
Microgrid is an efficient solution to integraterenewable energy sources (RES) into power systems. Inorder to deal with the intermittent characteristics of therenewable energy based distributed generation (DG) units,a fuzzy-logic based coordinated control strategy of thebattery energy storage system...
Fuzzy Logic Controlled Solar Module for Driving Three- Phase Induction Motor
Afiqah Zainal, Nurul; Sooi Tat, Chan; Ajisman
2016-02-01
Renewable energy produced by solar module gives advantages for generated three- phase induction motor in remote area. But, solar module's ou tput is uncertain and complex. Fuzzy logic controller is one of controllers that can handle non-linear system and maximum power of solar module. Fuzzy logic controller used for Maximum Power Point Tracking (MPPT) technique to control Pulse-Width Modulation (PWM) for switching power electronics circuit. DC-DC boost converter used to boost up photovoltaic voltage to desired output and supply voltage source inverter which controlled by three-phase PWM generated by microcontroller. IGBT switched Voltage source inverter (VSI) produced alternating current (AC) voltage from direct current (DC) source to control speed of three-phase induction motor from boost converter output. Results showed that, the output power of solar module is optimized and controlled by using fuzzy logic controller. Besides that, the three-phase induction motor can be drive and control using VSI switching by the PWM signal generated by the fuzzy logic controller. This concluded that the non-linear system can be controlled and used in driving three-phase induction motor.
Fuzzy Logic QoS Dynamic Source Routing for Mobile Ad Hoc Networks
Institute of Scientific and Technical Information of China (English)
ZHANG Xu; CHENG Sheng; FENG Mei-yu; DING Wei
2004-01-01
Considering the characters of dynamic topology and the imprecise state information in mobile ad hoc network,we propose a Fuzzy Logic QoS Dynamic Source Routing (FLQDSR) algorithm based on Dynamic Source Routing (DSR)protocol while adopting fuzzy logic to select the appropriate QoS routing in multiple paths which are searched in parallel.This scheme considers not only the bandwidth and end-to-end delay of routing, but also the cost of the path. On the other hand the merit of using fuzzy logic is that it can be implemented by hardware. This makes the realization of the scheme easier and faster. However our algorithm is based on DSR, the maximal hop count should be less than 10, i.e., the scale of mobile ad hoc network should not be very large. Simulation results show that FLQDSR can tolerate a high degree of information imprecision by adding the fuzzy logic module which integrates the QoS requirements of application and the routing QoS parameters to determine the most qualified one in every node.
A "fuzzy"-logic language for encoding multiple physical traits in biomolecules.
Warszawski, Shira; Netzer, Ravit; Tawfik, Dan S; Fleishman, Sarel J
2014-12-12
To carry out their activities, biological macromolecules balance different physical traits, such as stability, interaction affinity, and selectivity. How such often opposing traits are encoded in a macromolecular system is critical to our understanding of evolutionary processes and ability to design new molecules with desired functions. We present a framework for constraining design simulations to balance different physical characteristics. Each trait is represented by the equilibrium fractional occupancy of the desired state relative to its alternatives, ranging from none to full occupancy, and the different traits are combined using Boolean operators to effect a "fuzzy"-logic language for encoding any combination of traits. In another paper, we presented a new combinatorial backbone design algorithm AbDesign where the fuzzy-logic framework was used to optimize protein backbones and sequences for both stability and binding affinity in antibody-design simulation. We now extend this framework and find that fuzzy-logic design simulations reproduce sequence and structure design principles seen in nature to underlie exquisite specificity on the one hand and multispecificity on the other hand. The fuzzy-logic language is broadly applicable and could help define the space of tolerated and beneficial mutations in natural biomolecular systems and design artificial molecules that encode complex characteristics.
Self-tuning fuzzy logic control of a switched reluctance generator for wind energy applications
DEFF Research Database (Denmark)
Park, Kiwoo; Chen, Zhe
2012-01-01
This paper presents a new self-tuning fuzzy logic control (FLC) based speed controller of a switched reluctance generator (SRG) for wind power applications. Due to its doubly salient structure and magnetic saturation, the SRG possesses an inherent characteristic of strong nonlinearity. In addition...
DEFF Research Database (Denmark)
Mauricio Iglesias, Miguel; Vangsgaard, Anna Katrine; Gernaey, Krist;
2013-01-01
This contribution explores the use of diagnosis and control modules based on fuzzy set theory and logic for bioreactor monitoring and control. With this aim, two independent modules were used jointly to carry out first the diagnosis of the state of the system and then use transfer this information...
A fuzzy logic controlled superconducting magnetic energy storage, SMES frequency stabilizer
Energy Technology Data Exchange (ETDEWEB)
Hemeida, Ashraf Mohamed [E.E. Dept, Higher Institute of Energy, South Valley University, Aswan (Egypt)
2010-06-15
This paper presents application of fuzzy logic controlled superconducting magnetic energy storage device, SMES to damp the frequency oscillations of interconnected two-area power systems due to load excursions. The system frequency oscillations appear due to load disturbance. To stabilize the system frequency oscillations, the active power can be controlled via superconducting magnetic energy storage device, SMES. The error in the area control and its rate of change is used as controller input signals to the proposed fuzzy logic controller. In order to judge the effect of the proposed fuzzy logic controlled SMES, a comparative study is made between its effect and the effect of the conventional proportional plus integral (PI) controlled SMES. The studied system consists of two-area (thermal-thermal) power system each one equipped with SMES unit. The time simulation results indicate the superiority of the proposed fuzzy logic controlled SMES over the conventional PI SMES in damping the system oscillations and reach quickly to zero frequency deviation. The system is modeled and solved by using MATLAB software. (author)
Process Monitoring by combining several signal-analysis results using fuzzy logic
International Nuclear Information System (INIS)
In order to improve reliability in detecting anomalies in nuclear power plant performance, a method is presented which is based on acquiring various characteristics of signal data using autoregressive, wavelet and fractal-analysis techniques. These characteristics are combined using a decision making approach based on fuzzy logic. This approach is able to detect and distinguish several system states
Energy Technology Data Exchange (ETDEWEB)
Huang Xiaohua; Xiao Liye [Graduate School of the Chinese Academy of Sciences, Key Lab. of Applied Superconductivity, Inst. of Electrical Engineering, BJ (China)
2008-07-01
The mathematic models for wind power generator and superconducting magnetic energy storage (SMES) are introduced, and the function of SMES in improving the operation performance of wind farms is studied. The simulation results show that the proposed fuzzy logic-controlled SMES is a very effective device for improving the transient stability of wind power system after short circuit of the grid. (orig.)
Handling Uncertain and Ambiguous Spatial Expressions in Text Using Fuzzy Logic
Directory of Open Access Journals (Sweden)
V. R. KANAGAVALLI
2013-08-01
Full Text Available The knowledge era, as this era is called, poses the challenge of churning knowledge from the pool of information available from various sources. Much of the text documents acting as an information source and the query posed by the user implicitly have a geographic or spatial reference component present in it. This logically leads to the conclusion by the previous studies that more than 80% of the searches are pertaining to geographic locations. Text documents imply the usage of natural language and as such it yields to explicit vague fuzzy descriptions involving linguistic terms such as near to, far from, to the east of, very close and also implicit vague spatial references. Any text document which contains physical location specifications such as place names, geographic coordinates, landmarks, country names etc., are supposed to contain the spatial information. Fuzzy logic is an extension to the Boolean crisp logic toaccommodate for the fuzziness of an element belonging to a set. The understanding and extraction of spatial components is a primal area of study not only in the field of information retrieval but also invarious other fields such as Robotics, Psychology, Geosciences, Geography, Political Sciences, Geographic Economy, Environmental, Mining and Petroleum Engineering, Natural Resources, Epidemiology,Demography etc., Given a query involving events, the aim of this ongoing research work is to extract both the explicit and implicit spatial references from the text documents using fuzzy logic techniques.
Fuzzy logic techniques for rendezvous and docking of two geostationary satellites
Ortega, Guillermo
1995-01-01
Large assemblings in space require the ability to manage rendezvous and docking operations. In future these techniques will be required for the gradual build up of big telecommunication platforms in the geostationary orbit. The paper discusses the use of fuzzy logic to model and implement a control system for the docking/berthing of two satellites in geostationary orbit. The system mounted in a chaser vehicle determines the actual state of both satellites and generates torques to execute maneuvers to establish the structural latching. The paper describes the proximity operations to collocate the two satellites in the same orbital window, the fuzzy guidance and navigation of the chaser approaching the target and the final Fuzzy berthing. The fuzzy logic system represents a knowledge based controller that realizes the close loop operations autonomously replacing the conventional control algorithms. The goal is to produce smooth control actions in the proximity of the target and during the docking to avoid disturbance torques in the final assembly orbit. The knowledge of the fuzzy controller consists of a data base of rules and the definitions of the fuzzy sets. The knowledge of an experienced spacecraft controller is captured into a set of rules forming the Rules Data Base.
Directory of Open Access Journals (Sweden)
Ammar Hussein Mutlag
2014-01-01
Full Text Available This paper presents an adaptive fuzzy logic controller (FLC design technique for photovoltaic (PV inverters using differential search algorithm (DSA. This technique avoids the exhaustive traditional trial and error procedure in obtaining membership functions (MFs used in conventional FLCs. This technique is implemented during the inverter design phase by generating adaptive MFs based on the evaluation results of the objective function formulated by the DSA. In this work, the mean square error (MSE of the inverter output voltage is used as an objective function. The DSA optimizes the MFs such that the inverter provides the lowest MSE for output voltage and improves the performance of the PV inverter output in terms of amplitude and frequency. The design procedure and accuracy of the optimum FLC are illustrated and investigated using simulations conducted for a 3 kW three-phase inverter in a MATLAB/Simulink environment. Results show that the proposed controller can successfully obtain the desired output when different linear and nonlinear loads are connected to the system. Furthermore, the inverter has reasonably low steady state error and fast response to reference variation.
Fuzzy logic augmentation of nature-inspired optimization metaheuristics theory and applications
Melin, Patricia
2015-01-01
This book describes recent advances on fuzzy logic augmentation of nature-inspired optimization metaheuristics and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in two main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic augmentation of nature-inspired optimization metaheuristics, which basically consists of papers that propose new optimization algorithms enhanced using fuzzy systems. The second part contains papers with the main theme of application of optimization algorithms, which are basically papers using nature-inspired techniques to achieve optimization of complex optimization problems in diverse areas of application.
Comparison between Conventional and Fuzzy Logic PID Controllers for Controlling DC Motors
Directory of Open Access Journals (Sweden)
Essam Natsheh
2010-09-01
Full Text Available Fuzzy logic and proportional-integral-derivative (PID controllers are compared for use in direct current (DC motors positioning system. A simulation study of the PID position controller for the armature-controlled with fixed field and field controlled with fixed armature current DC motors is performed. Fuzzy rules and the inferencing mechanism of the fuzzy logic controller (FLC are evaluated by using conventional rule-lookup tables that encode the control knowledge in a rules form. The performance assessment of the studied position controllers is based on transient response and error integral criteria. The results obtained from the FLC are not only superior in the rise time, speed fluctuations, and percent overshoot but also much better in the controller output signal structure, which is much remarkable in terms of the hardware implementation.
Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization
Castillo, Oscar; Kacprzyk, Janusz
2015-01-01
This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...
Exploring the use of fuzzy logic models to describe the relation between SBP and RR values.
Gouveia, Sónia; Brás, Susana
2012-01-01
In this work, fuzzy logic based models are used to describe the relation between systolic blood pressure (SBP) and tachogram (RR) values as a function of the SBP level. The applicability of these methods is tested using real data in Lying (L) and Standing (S) conditions and generated surrogate data. The results indicate that fuzzy models exhibit a similar performance in both conditions, and their performance is significantly higher with real data than with surrogate data. These results point out the potential of a fuzzy logic approach to model properly the relation between SBP and RR values. As a future work, it remains to assess the clinical impact of these findings and inherent repercussion on the estimation of time domain baroreflex sensitivity indices.
Institute of Scientific and Technical Information of China (English)
HuangMiao-hua; JinGuo-dong
2003-01-01
The Hierarchical Structure Fuzzy Logic Control(HSFLC) strategies of torque distribute for Parallel Hybrid Electric Vehicle (PHEV) in the mocle of operation of the vehicle i. e. , acceleration, cruise, deceleration etc. have been studied. Using secondly developed the hybrid vehicle simulation tool ADVISOR, the dynamic model of PHEV has been set up by MATLAB/SIMULINK. The engine, motor as well as the battery characteristics have been studied. Simulation results show that the proposed hierarchical structured fuzzy logic control strategy is effective over the entire operating range of the vehicle in terms of fuel economy. Based on the analyses of the simulation results and driver's experiences, a fuzzy controller is designed and developed to control the torque distribution. The controller is evaluated via hardware-in-the-loop simulator (HILS). The results show that controller verify its value.
A novel vehicle navigation map matching algorithm based on fuzzy logic and its application
Institute of Scientific and Technical Information of China (English)
TONG Xiao-hua; WU Song-chun; WU Shu-qing; LIU Da-jie
2005-01-01
A new real-time map matching algorithm based on fuzzy logic is proposed. 3 main factors affecting the reliability of map matching, including the distance between the vehicle location and the matching road segment, the angle between the vehicle direction and the road segment direction and the road connectivity are discussed. Fuzzy rules for the distance, angle and connectivity are presented to calculate the matching reliability. 2 indicators for estimating the matching reliability are then derived, one is the lower limit of the reliability, and the other is the limit error of the difference between the maximal value and the second-maximal value of the reliability. A real-time map-matching system based on fuzzy logic is therefore developed. Using the real data of global positioning system(GIS) based navigation and geographic information system(GPS) based road map, the method is verified and the results prove the effectiveness of the proposed method.
Design and Implementation of a Water Level Controller using Fuzzy Logic
Directory of Open Access Journals (Sweden)
Namrata Dey
2013-06-01
Full Text Available This paper analyzes the effectiveness of water level control using fuzzy logic. The water level in the tank is sensed using transistor switching principle. The level sensed is fed to the PIC16 microcontroller. The user provides the set point to the microcontroller through serial communication using the COM development port software, Terminal. It computes the error as the difference between the set point and the process variable. The fuzzy logic programmed in the microcontroller is applied which controls the water level in the tank using the drain and the feed pumps. Once the set point has been reached, the message along with the present level is sent back through serial communication to the user interface on a PC. Thus, the water level in the tank is controlled according to the set point given by the user. The implementation of a fuzzy level controller has many applications such as boiler drum level control, reverse osmosis plant, demineralisation plant etc.
VANET Broadcast Protocol Based on Fuzzy Logic and Lightweight Retransmission Mechanism
Wu, Celimuge; Ohzahata, Satoshi; Kato, Toshihiko
Vehicular ad hoc networks have been attracting the interest of both academic and industrial communities on account of their potential role in Intelligent Transportation Systems (ITS). However, due to vehicle movement and fading in wireless communications, providing a reliable and efficient multi-hop broadcast service in vehicular ad hoc networks is still an open research topic. In this paper, we propose FUZZBR (FUZZy BRoadcast), a fuzzy logic based multi-hop broadcast protocol for information dissemination in vehicular ad hoc networks. FUZZBR has low message overhead since it uses only a subset of neighbor nodes to relay data messages. In the relay node selection, FUZZBR jointly considers multiple metrics of inter-vehicle distance, node mobility and signal strength by employing the fuzzy logic. FUZZBR also uses a lightweight retransmission mechanism to retransmit a packet when a relay fails. We use computer simulations to evaluate the performance of FUZZBR.
Enhancement of transient stability by fuzzy logic-controlled SMES considering communication delay
Energy Technology Data Exchange (ETDEWEB)
Ali, Mohd Hasan; Wu, Bin [Dept. of Electrical and Computer Engineering, Ryerson University, 245 Church Street, Toronto, Ontario (Canada); Park, Minwon; Yu, In-Keun [Dept. of Electrical Engineering, Changwon National University, Changwon, Gyeongnam 641-773 (Korea); Murata, Toshiaki; Tamura, Junji [Dept. of Electrical and Electronic Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami, Hokkaido 090-8507 (Japan)
2009-09-15
This paper presents a fuzzy logic-controlled superconducting magnetic energy storage (SMES) for the enhancement of transient stability in a multi-machine power system. The control scheme of SMES is based on a pulse width modulation (PWM) voltage source converter (VSC) and a two-quadrant DC-DC chopper using gate-turn-off (GTO) thyristor. Total kinetic energy deviation (TKED) of the synchronous generators is used as the fuzzy input for SMES control. Communication delays introduced in online calculation of the TKED are considered for the actual analysis of transient stability. Global positioning system (GPS) is proposed for the practical implementation of the calculation of the TKED. Simulation results of balanced fault at different points in a multi-machine power system show that the proposed fuzzy logic-controlled SMES is an effective device for transient stability enhancement of multi-machine power system. Moreover, the transient stability performance is effected by the communication delay. (author)
PSO type-reduction method for geometric interval type-2 fuzzy logic systems
Institute of Scientific and Technical Information of China (English)
ZHAO Xian-zhang; GAO Yi-bo; ZENG Jun-fang; YANG Yi-ping
2008-01-01
In a special case of type-2 fuzzy logic systems (FLS), i.e. geometric interval type-2 fuzzy logic sys-tems (GIT-2FLS), the crisp output is obtained by computing the geometric center of footprint of uncertainty (FOU) without type-reduction, but the defuzzifying method acts against the corner concepts of type-2 fuzzy sets in some cases. In this paper, a PSO type-reduction method for GIT-2FLS based on the particle swarm optimiza-tion (PSO) algorithm is presented. With the PSO type-reduction, the inference principle of geometric interval FLS operating on the continuous domain is consistent with that of traditional interval type-2 FLS operating on the discrete domain. With comparative experiments, it is proved that the PSO type-reduction exhibits good perform-ance, and is a satisfactory complement for the theory of GIT-2FLS.
International Nuclear Information System (INIS)
As part of the special track on 'Lessons learned from computational intelligence in nuclear applications' at the forthcoming FLINS 2004 conference on Applied Computational Intelligence (Blankenberge, Belgium, September 1-3, 2004), research experiences on fuzzy logic techniques in applications of nuclear reactor control operation are critically reviewed in this presentation. Assessment of four real fuzzy control applications at the MIT research reactor in the US, the FUGEN heavy water reactor in Japan, the BR1 research reactor in Belgium, and a TRIGA Mark III reactor in Mexico will be examined thought a SWOT analysis (strengths, weakness, opportunities, and threats). Special attention will be paid to the current cooperation between the Belgian Nuclear Research Centre (SCK-CEN) and the Mexican Nuclear Centre (ININ) on the fuzzy logic control for nuclear reactor control project under the partial support of the National Council for Science and Technology of Mexico (CONACYT). (Author)
Adaptive Fuzzy-Lyapunov Controller Using Biologically Inspired Swarm Intelligence
Alejandro Carrasco Elizalde; Peter Goldsmith
2008-01-01
The collective behaviour of swarms produces smarter actions than those achieved by a single individual. Colonies of ants, flocks of birds and fish schools are examples of swarms interacting with their environment to achieve a common goal. This cooperative biological intelligence is the inspiration for an adaptive fuzzy controller developed in this paper. Swarm intelligence is used to adjust the parameters of the membership functions used in the adaptive fuzzy controller. The rules of the cont...
Apalit, Nathan
2010-01-01
The world of musculoskeletal disorders (MSDs) is complicated and fuzzy. Fuzzy logic provides a precise framework for complex problems characterized by uncertainty, vagueness and imprecision. Although fuzzy logic would appear to be an ideal modeling language to help address the complexity of MSDs, little research has been done in this regard. The Work Ratio is a novel mathematical model that uses fuzzy logic to provide a numerical and linguistic valuation of the likelihood of return to work and remaining at work. It can be used for a worker with any MSD at any point in time. Basic mathematical concepts from set theory and fuzzy logic are reviewed. A case study is then used to illustrate the use of the Work Ratio. Its potential strengths and limitations are discussed. Further research of its use with a variety of MSDs, settings and multidisciplinary teams is needed to confirm its universal value.
French speaking meeting on fuzzy logics and its applications
International Nuclear Information System (INIS)
The LFA meeting is a opportunity for university searchers and industrialists to meet each others and to present their most recent results on the theory of fuzzy sets and/or on its applications. The domain of applications ranges from the fuzzy control of processes to classifying, pattern recognition, data analysis, decision making, reasoning, image processing and interpretation, data fusion, artificial intelligence or data management systems. This issue of the LFA meeting inaugurates some new theories of uncertainty such as the Dempster-Shafer theory of belief functions or the qualitative approaches. From the 40 communications published in this book, two fall into the Inis scope (radioactive waste management and 3-D NMR imaging of brain tissues) and one into the Etde scope (fuzzy control of an electric-powered vehicle). (J.S.)
The Fuzzy Logic of MicroRNA Regulation: A Key to Control Cell Complexity.
Ripoli, Andrea; Rainaldi, Giuseppe; Rizzo, Milena; Mercatanti, Alberto; Pitto, Letizia
2010-08-01
Genomic and clinical evidence suggest a major role of microRNAs (miRNAs) in the regulatory mechanisms of gene expression, with a clear impact on development and physiology; miRNAs are a class of endogenous 22-25 nt single-stranded RNA molecules, that negatively regulate gene expression post-transcriptionally, by imperfect base pairing with the 3' UTR of the corresponding mRNA target. Because of this imperfection, each miRNA can bind multiple targets, and multiple miRNAs can bind the same mRNA target; although digital, the miRNAs control mechanism is characterized by an imprecise action, naturally understandable in the theoretical framework of fuzzy logic.A major practical application of fuzzy logic is represented by the design and the realization of efficient and robust control systems, even when the processes to be controlled show chaotic, deterministic as well unpredictable, behaviours. The vagueness of miRNA action, when considered together with the controlled and chaotic gene expression, is a hint of a cellular fuzzy control system. As a demonstration of the possibility and the effectiveness of miRNA based fuzzy mechanism, a fuzzy cognitive map -a mathematical formalism combining neural network and fuzzy logic- has been developed to study the apoptosis/proliferation control performed by the miRNA-17-92 cluster/E2F1/cMYC circuitry.When experimentally demonstrated, the concept of fuzzy control could modify the way we analyse and model gene expression, with a possible impact on the way we imagine and design therapeutic intervention based on miRNA silencing.
Prediction total specific pore volume of geopolymers produced from waste ashes by fuzzy logic
Directory of Open Access Journals (Sweden)
Ali Nazari
2012-04-01
Full Text Available In the present work, total specific pore volume of inorganic polymers (geopolymers made from seeded fly ash and rice husk bark ash has been predicted by fuzzy logic. Different specimens, made from a mixture of fly ash and rice husk bark ash in fine and coarse form together with alkali activator made of water glass and NaOH solution, were subjected to porosimetry tests at 7 and 28 days of curing. The curing regime was different: one set of the specimens were cured at room temperature until reaching to 7 and 28 days and the other sets were oven cured for 36 hours at the range of 40-90 °C and then cured at room temperature until 7 and 28 days. A model based on fuzzy logic for predicting the total specific pore volume of the specimens has been presented. To build the model, training and testing using experimental results from 120 specimens were conducted. The used data as the inputs of fuzzy logic models are arranged in a format of six parameters that cover the percentage of fine fly ash in the ashes mixture, the percentage of coarse fly ash in the ashes mixture, the percentage of fine rice husk bark ash in the ashes mixture, the percentage of coarse rice husk bark ash in the ashes mixture, the temperature of curing and the time of water curing. According to the input parameters, in the fuzzy logic model, the pore volume of each specimen was predicted. The training and testing results in the fuzzy logic model have shown a strong potential for predicting the total specific pore volume of the geopolymer specimens in the considered range.
Mzenda, Bongile; Gegov, Alexander; Brown, David J; Petrov, Nedyalko
2012-01-01
This study investigates the feasibility of using Artificial Neural Network (ANN) and fuzzy logic based techniques to select treatment margins for dynamically moving targets in the radiotherapy treatment of prostate cancer. The use of data from 15 patients relating error effects to the Tumour Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) radiobiological indices was contrasted against the use of data based on the prostate volume receiving 99% of the prescribed dose (V99%) and the rectum volume receiving more than 60Gy (V60). For the same input data, the results of the ANN were compared to results obtained using a fuzzy system, a fuzzy network and current clinically used statistical techniques. Compared to fuzzy and statistical methods, the ANN derived margins were found to be up to 2 mm larger at small and high input errors and up to 3.5 mm larger at medium input error magnitudes.
Hybrid neural network and fuzzy logic approaches for rendezvous and capture in space
Berenji, Hamid R.; Castellano, Timothy
1991-01-01
The nonlinear behavior of many practical systems and unavailability of quantitative data regarding the input-output relations makes the analytical modeling of these systems very difficult. On the other hand, approximate reasoning-based controllers which do not require analytical models have demonstrated a number of successful applications such as the subway system in the city of Sendai. These applications have mainly concentrated on emulating the performance of a skilled human operator in the form of linguistic rules. However, the process of learning and tuning the control rules to achieve the desired performance remains a difficult task. Fuzzy Logic Control is based on fuzzy set theory. A fuzzy set is an extension of a crisp set. Crisp sets only allow full membership or no membership at all, whereas fuzzy sets allow partial membership. In other words, an element may partially belong to a set.
Construction of a fuzzy and Boolean logic gates based on DNA.
Zadegan, Reza M; Jepsen, Mette D E; Hildebrandt, Lasse L; Birkedal, Victoria; Kjems, Jørgen
2015-04-17
Logic gates are devices that can perform logical operations by transforming a set of inputs into a predictable single detectable output. The hybridization properties, structure, and function of nucleic acids can be used to make DNA-based logic gates. These devices are important modules in molecular computing and biosensing. The ideal logic gate system should provide a wide selection of logical operations, and be integrable in multiple copies into more complex structures. Here we show the successful construction of a small DNA-based logic gate complex that produces fluorescent outputs corresponding to the operation of the six Boolean logic gates AND, NAND, OR, NOR, XOR, and XNOR. The logic gate complex is shown to work also when implemented in a three-dimensional DNA origami box structure, where it controlled the position of the lid in a closed or open position. Implementation of multiple microRNA sensitive DNA locks on one DNA origami box structure enabled fuzzy logical operation that allows biosensing of complex molecular signals. Integrating logic gates with DNA origami systems opens a vast avenue to applications in the fields of nanomedicine for diagnostics and therapeutics.
Elbouz, Marwa; Alfalou, Ayman; Brosseau, Christian
2011-06-01
Home automation is being implemented into more and more domiciles of the elderly and disabled in order to maintain their independence and safety. For that purpose, we propose and validate a surveillance video system, which detects various posture-based events. One of the novel points of this system is to use adapted Vander-Lugt correlator (VLC) and joint-transfer correlator (JTC) techniques to make decisions on the identity of a patient and his three-dimensional (3-D) positions in order to overcome the problem of crowd environment. We propose a fuzzy logic technique to get decisions on the subject's behavior. Our system is focused on the goals of accuracy, convenience, and cost, which in addition does not require any devices attached to the subject. The system permits one to study and model subject responses to behavioral change intervention because several levels of alarm can be incorporated according different situations considered. Our algorithm performs a fast 3-D recovery of the subject's head position by locating eyes within the face image and involves a model-based prediction and optical correlation techniques to guide the tracking procedure. The object detection is based on (hue, saturation, value) color space. The system also involves an adapted fuzzy logic control algorithm to make a decision based on information given to the system. Furthermore, the principles described here are applicable to a very wide range of situations and robust enough to be implementable in ongoing experiments.
Energy Technology Data Exchange (ETDEWEB)
Santos, Francisco Carlos B. dos; Carneiro, Alvaro Luiz Guimaraes [Instituto de Pesquisas Energeticas e Nucleares (IPEN-CNEN/SP), Sao Paulo - SP (Brazil)], E-mails: fcarlos@usp.br, carneiro@ipen.br
2010-11-15
Aggregation tools database and subsequent interpretation are the most challenge in the area of sustainability This task becomes very complex due to correlation of topics that comprise the dimensions that form the basis of the concept of sustainable development. The technique known as Fuzzy Logic or Fuzzy Logic is a powerful tool to capture information on vacancies, which is often the only information available in the area of sustainability. (author)
Adaptive Fuzzy-Lyapunov Controller Using Biologically Inspired Swarm Intelligence
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Alejandro Carrasco Elizalde
2008-01-01
Full Text Available The collective behaviour of swarms produces smarter actions than those achieved by a single individual. Colonies of ants, flocks of birds and fish schools are examples of swarms interacting with their environment to achieve a common goal. This cooperative biological intelligence is the inspiration for an adaptive fuzzy controller developed in this paper. Swarm intelligence is used to adjust the parameters of the membership functions used in the adaptive fuzzy controller. The rules of the controller are designed using a computing-with-words approach called Fuzzy-Lyapunov synthesis to improve the stability and robustness of an adaptive fuzzy controller. Computing-with-words provides a powerful tool to manipulate numbers and symbols, like words in a natural language.
Adaptive neuro-fuzzy controller of switched reluctance motor
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Tahour Ahmed
2007-01-01
Full Text Available This paper presents an application of adaptive neuro-fuzzy (ANFIS control for switched reluctance motor (SRM speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI.
Robust adaptive fuzzy control scheme for nonlinear system with uncertainty
Institute of Scientific and Technical Information of China (English)
Mingjun ZHANG; Huaguang ZHANG
2006-01-01
In this paper, a robust adaptive fuzzy control scheme for a class of nonlinear system with uncertainty is proposed. First, using prior knowledge about the plant we obtain a fuzzy model, which is called the generalized fuzzy hyperbolic model (GFHM). Secondly, for the case that the states of the system are not available an observer is designed and a robust adaptive fuzzy output feedback control scheme is developed. The overall control system guarantees that the tracking error converges to a small neighborhood of origin and that all signals involved are uniformly bounded. The main advantages of the proposed control scheme are that the human knowledge about the plant under control can be used to design the controller and only one parameter in the adaptive mechanism needs to be on-line adjusted.
Building Better Discipline Strategies for Schools by Fuzzy Logics
Chang, Dian-Fu; Juan, Ya-Yun; Chou, Wen-Ching
2014-01-01
This study aims to realize better discipline strategies for applying in high schools. We invited 400 teachers to participate the survey and collected their perceptions on the discipline strategies in terms of the acceptance of strategies and their effectiveness in schools. Based on the idea of fuzzy statistics, this study transformed the fuzzy…
A New Neuro-Fuzzy Adaptive Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
ZHU Lili; ZHANG Huanchun; JING Yazhi
2003-01-01
Novel neuro-fuzzy techniques are used to dynamically control parameter settings of genetic algorithms (GAs). The benchmark routine is an adaptive genetic algorithm (AGA) that uses a fuzzy knowledge-based system to control GA parameters. The self-learning ability of the cerebellar model ariculation controller(CMAC) neural network makes it possible for on-line learning the knowledge on GAs throughout the run. Automatically designing and tuning the fuzzy knowledge-base system, neurofuzzy techniques based on CMAC can find the optimized fuzzy system for AGA by the renhanced learning method. The Results from initial experiments show a Dynamic Parametric AGA system designed by the proposed automatic method and indicate the general applicability of the neuro-fuzzy AGA to a wide range of combinatorial optimization.
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Márcio Mendonça
2015-10-01
Full Text Available In this work, it is analyzed a multivariate system control of an alcoholic fermentation process with no minimum phase. The control is made with PID classic controllers associated with a supervisory system based on Fuzzy Systems. The Fuzzy system, a priori, send set-points to PID controllers, but also adds protection functions, such as if the biomass valued is at zero or very close. The Fuzzy controller changes the campaign to prevent or mitigate the paralyzation of the process. Three control architectures based on Fuzzy Control Systems are presented and compared in performance with classic control in different campaigns. The third architecture, in particular, adds an adaptive function. A brief summary of Fuzzy theory and correlated works will be presented. And, finally simulations results, conclusions and future works end the article.
Amador-Angulo, Leticia; Mendoza, Olivia; Castro, Juan R.; Rodríguez-Díaz, Antonio; Melin, Patricia; Castillo, Oscar
2016-01-01
A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot. We add two types of perturbations in the model for the Generalized Type-2 Fuzzy Logic System to better analyze its behavior under uncertainty and this shows better results when compared to the original BCO. We implemented various performance indices; ITAE, IAE, ISE, ITSE, RMSE and MSE to measure the performance of the controller. The experimental results show better performances using GT2FLS then by IT2FLS and T1FLS in the dynamic adaptation the parameters for the BCO algorithm. PMID:27618062
Amador-Angulo, Leticia; Mendoza, Olivia; Castro, Juan R; Rodríguez-Díaz, Antonio; Melin, Patricia; Castillo, Oscar
2016-01-01
A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot. We add two types of perturbations in the model for the Generalized Type-2 Fuzzy Logic System to better analyze its behavior under uncertainty and this shows better results when compared to the original BCO. We implemented various performance indices; ITAE, IAE, ISE, ITSE, RMSE and MSE to measure the performance of the controller. The experimental results show better performances using GT2FLS then by IT2FLS and T1FLS in the dynamic adaptation the parameters for the BCO algorithm. PMID:27618062
Santiago Girola Schneider, Rafael
2015-08-01
The fuzzy logic is a branch of the artificial intelligence founded on the concept that 'everything is a matter of degree.' It intends to create mathematical approximations on the resolution of certain types of problems. In addition, it aims to produce exact results obtained from imprecise data, for which it is particularly useful for electronic and computer applications. This enables it to handle vague or unspecific information when certain parts of a system are unknown or ambiguous and, therefore, they cannot be measured in a reliable manner. Also, when the variation of a variable can produce an alteration on the others.The main focus of this paper is to prove the importance of these techniques formulated from a theoretical analysis on its application on ambiguous situations in the field of the rich clusters of galaxies. The purpose is to show its applicability in the several classification systems proposed for the rich clusters, which are based on criteria such as the level of richness of the cluster, the distribution of the brightest galaxies, whether there are signs of type-cD galaxies or not or the existence of sub-clusters.Fuzzy logic enables the researcher to work with “imprecise” information implementing fuzzy sets and combining rules to define actions. The control systems based on fuzzy logic join input variables that are defined in terms of fuzzy sets through rule groups that produce one or several output values of the system under study. From this context, the application of the fuzzy logic’s techniques approximates the solution of the mathematical models in abstractions about the rich galaxy cluster classification of physical properties in order to solve the obscurities that must be confronted by an investigation group in order to make a decision.
Improved Gravitational Search Algorithm (GSA Using Fuzzy Logic
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Omid Mokhlesi
2013-04-01
Full Text Available Researchers tendency to use different collective intelligence as the search methods to optimize complex engineering problems has increased because of the high performance of this algorithms. Gravitational search algorithm (GSA is among these algorithms. This algorithm is inspired by Newton's laws of physics and gravitational attraction. Random masses are agents who have searched for the space. This paper presents a new Fuzzy Population GSA model called FPGSA. The proposed method is a combination of parametric fuzzy controller and gravitational search algorithm. The space being searched using this combined reasonable and accurate method. In the collective intelligence algorithms, population size influences the final answer so that for a large population, a better response is obtained but the algorithm execution time is longer. To overcome this problem, a new parameter called the dispersion coefficient is added to the algorithm. Implementation results show that by controlling this factor, system performance can be improved.
Using Fuzzy Logic for Performance Evaluation in Reinforcement Learning
Berenji, Hamid R.; Khedkar, Pratap S.
1992-01-01
Current reinforcement learning algorithms require long training periods which generally limit their applicability to small size problems. A new architecture is described which uses fuzzy rules to initialize its two neural networks: a neural network for performance evaluation and another for action selection. This architecture is applied to control of dynamic systems and it is demonstrated that it is possible to start with an approximate prior knowledge and learn to refine it through experiments using reinforcement learning.
Fuzzy Logic Based Controller for Maintaining Human Comfort within Intelligent Building System
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Nasrodin .T. Mustapha, Momoh J. E. Salami, Nazim and M. Nasiri
2012-10-01
proper control of AHU dampers and fans is an effective and practical means to satisfy human comfort with minimum energy consumption.Keywords: Human comfort, Intelligent control, Air handling unit, Adaptive fuzzy logic control
Feasibility of using adaptive logic networks to predict compressor unit failure
Energy Technology Data Exchange (ETDEWEB)
Armstrong, W.W.; Chungying Chu; Thomas, M.M. [Dendronic Decisions Limited, Edmonton (Canada)] [and others
1995-12-31
In this feasibility study, an adaptive logic network (ALN) was trained to predict failures of turbine-driven compressor units using a large database of measurements. No expert knowledge about compressor systems was involved. The predictions used only the statistical properties of the measurements and the indications of failure types. A fuzzy set was used to model measurements typical of normal operation. It was constrained by a requirement imposed during ALN training, that it should have a shape similar to a Gaussian density, more precisely, that its logarithm should be convex-up. Initial results obtained using this approach to knowledge discovery in the database were encouraging.
Computationally Efficient Adaptive Type-2 Fuzzy Control of Flexible-Joint Manipulators
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Hicham Chaoui
2013-05-01
Full Text Available In this paper, we introduce an adaptive type-2 fuzzy logic controller (FLC for flexible-joint manipulators with structured and unstructured dynamical uncertainties. Simplified interval fuzzy sets are used for real-time efficiency, and internal stability is enhanced by adopting a trade-off strategy between the manipulator’s and the actuators’ velocities. Furthermore, the control scheme is independent of the computationally expensive noisy torque and acceleration signals. The controller is validated through a set of numerical simulations and by comparing it against its type-1 counterpart. The ability of the adaptive type-2 FLC in coping with large magnitudes of uncertainties yields an improved performance. The stability of the proposed control system is guaranteed using Lyapunov stability theory.
IMPLEMENTATION OF FUZZY LOGIC MAXIMUM POWER POINT TRACKING CONTROLLER FOR PHOTOVOLTAIC SYSTEM
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Rasoul Rahmani
2013-01-01
Full Text Available In this study, simulation and hardware implementation of Fuzzy Logic (FL Maximum Power Point Tracking (MPPT used in photovoltaic system with a direct control method are presented. In this control system, no proportional or integral control loop exists and an adaptive FL controller generates the control signals. The designed and integrated system is a contribution of different aspects which includes simulation, design and programming and experimental setup. The resultant system is capable and satisfactory in terms of fastness and dynamic performance. The results also indicate that the control system works without steady-state error and has the ability of tracking MPPs rapid and accurate which is useful for the sudden changes in the atmospheric condition. MATLAB/Simulink software is utilized for simulation and also programming the TMS320F2812 Digital Signal Processor (DSP. The whole system designed and implemented to hardware was tested successfully on a laboratory PV array. The obtained experimental results show the functionality and feasibility of the proposed controller.
K.V. Santhosh
2015-01-01
This paper proposes a technique for measurement of liquid flow using venturi and ultrasonic flow meter(UFM) to have following objectives a) to design a multi-sensor data fusion (MSDF) architecture for using both the sensors, b) improve sensitivity and linearity of venturi and ultrasonic flow meter, and c) detect and diagnosis of faults in sensor if any. Fuzzy logic algorithm is used to fuse outputs of both the sensor and train the fuzzy block to produces output which has an improved character...
Adaptive Fuzzy Systems in Computational Intelligence
Berenji, Hamid R.
1996-01-01
In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.
A fuzzy logic system for seizure onset detection in intracranial EEG.
Rabbi, Ahmed Fazle; Fazel-Rezai, Reza
2012-01-01
We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved.
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Hodeiseh Gordan
2012-11-01
Full Text Available This paper proposes a novel inference mechanism for an interval type-2 Takagi-Sugeno-Kang fuzzy logic controlsystem (IT2 TSK FLCS. This paper focuses on control applications for case both plant and controller use A2-C0 TSK models. The defuzzified output of the T2FLS is then obtained by averaging the defuzzified outputs of the resultant four embedded T1FLSs in order to reduce the computational burden of T2 TSK FS. A simplified T2 TSK FS based on a hybrid structure of four type-1 fuzzy systems (T1 TSK FS. A simulation example is presented to show the eectiveness of this method.
Olivero, Jesús; Márquez, Ana L; Real, Raimundo
2013-01-01
This study uses the amphibian species of the Mediterranean basin to develop a consistent procedure based on fuzzy sets with which biogeographic regions and biotic transition zones can be objectively detected and reliably mapped. Biogeographical regionalizations are abstractions of the geographical organization of life on Earth that provide frameworks for cataloguing species and ecosystems, for answering basic questions in biogeography, evolutionary biology, and systematics, and for assessing priorities for conservation. On the other hand, limits between regions may form sharply defined boundaries along some parts of their borders, whereas elsewhere they may consist of broad transition zones. The fuzzy set approach provides a heuristic way to analyse the complexity of the biota within an area; significantly different regions are detected whose mutual limits are sometimes fuzzy, sometimes clearly crisp. Most of the regionalizations described in the literature for the Mediterranean biogeographical area present a certain degree of convergence when they are compared within the context of fuzzy interpretation, as many of the differences found between regionalizations are located in transition zones, according to our case study. Compared with other classification procedures based on fuzzy sets, the novelty of our method is that both fuzzy logic and statistics are used together in a synergy in order to avoid arbitrary decisions in the definition of biogeographic regions and transition zones.
Ramesh, Tejavathu; Kumar Panda, Anup; Shiva Kumar, S
2015-07-01
In this research study, a model reference adaptive system (MRAS) speed estimator for speed sensorless direct torque and flux control (DTFC) of an induction motor drive (IMD) using two adaptation mechanism schemes are proposed to replace the conventional proportional integral controller (PIC). The first adaptation mechanism scheme is based on Type-1 fuzzy logic controller (T1FLC), which is used to achieve high performance sensorless drive in both transient as well as steady state conditions. However, the Type-1 fuzzy sets are certain and unable to work effectively when higher degree of uncertainties presents in the system which can be caused by sudden change in speed or different load disturbances, process noise etc. Therefore, a new Type-2 fuzzy logic controller (T2FLC) based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties and improves the performance and also robust to various load torque and sudden change in speed conditions, respectively. The detailed performances of various adaptation mechanism schemes are carried out in a MATLAB/Simulink environment with a speed sensor and speed sensorless modes of operation when an IMD is operating under different operating conditions, such as, no-load, load and sudden change in speed, respectively. To validate the different control approaches, the system also implemented on real-time system and adequate results are reported for its validation.
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Yuanchun Li
2015-01-01
Full Text Available For the trajectory control of the probe soft landing on the asteroids with weak gravitational field, this paper presents a combined integral sliding mode control with an adaptive fuzzy logic system, named adaptive fuzzy sliding mode control (AFSMC scheme. Considering the uncertainty of the orbit dynamics model in the small body fixed coordinate system, and the polyhedron modeling uncertainty in the gravitational potential, a fuzzy logic system is adopted to approximate the upper bound of the uncertainties. In addition, a robust control item is introduced to compensate for the approximation error of fuzzy logic system. The designed adaptive law and robust item make the closed-loop control stable and the tracking errors are convergent to zero. The controller not only guarantees the rapidity and accuracy of the desired trajectory tracking, but also enhances the robustness of the control system, improving the dynamic tracking performance for the probe soft landing on asteroids. Finally, the contrastive simulation results are presented to show the feasibility and effectiveness of the proposed control scheme.
Development of an adaptive online fuzzy arbitrator for forecasting short-term natural gas usage
Lukas, Richard James, Jr.
2001-07-01
The focus of the work is on the development and utilization of a self-assembling Fuzzy logic controller for the purpose of improving short term natural gas load forecasts generated by artificial neural networks (ANN) and linear regression (LR) models. The approach is to form a matrix of dynamic post processors (DPP), composed of ARMAX models, which use load estimates generated by ANNs and LRs as inputs. The problem is to then determine the performance of each DPP under different operating conditions, and to generate a final load estimate using a Fuzzy logic controller. The contributions of this research are as follows. First, as part of a residuals analysis, prefiltering and nonlinear transforms are explored for the purpose of increasing the correlation of environmental input factors with gas load, while decreasing multicollinearity. This has the effect of reducing the covariance of model parameters and increasing forecast confidence. The result of this analysis will be used to develop ARMAX models to postfilter the ANN and LR forecast model estimates. The gas operating regions will be characterized by an adaptive clustering algorithm that will partition operating conditions into distinct patterns with unique consumption characteristics. Finally, an adaptive online Fuzzy controller identifies the characteristics of each DPP under different operating conditions, and generates a weighted average of the DPP estimators to produce the final gas load estimate.
Model-Free Adaptive Fuzzy Sliding Mode Controller Optimized by Particle Swarm for Robot Manipulator
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Amin Jalali
2013-05-01
Full Text Available The main purpose of this paper is to design a suitable control scheme that confronts the uncertainties in a robot. Sliding mode controller (SMC is one of the most important and powerful nonlinear robust controllers which has been applied to many non-linear systems. However, this controller has some intrinsic drawbacks, namely, the chattering phenomenon, equivalent dynamic formulation, and sensitivity to the noise. This paper focuses on applying artificial intelligence integrated with the sliding mode control theory. Proposed adaptive fuzzy sliding mode controller optimized by Particle swarm algorithm (AFSMC-PSO is a Mamdani’s error based fuzzy logic controller (FLS with 7 rules integrated with sliding mode framework to provide the adaptation in order to eliminate the high frequency oscillation (chattering and adjust the linear sliding surface slope in presence of many different disturbances and the best coefficients for the sliding surface were found by offline tuning Particle Swarm Optimization (PSO. Utilizing another fuzzy logic controller as an impressive manner to replace it with the equivalent dynamic part is the main goal to make the model free controller which compensate the unknown system dynamics parameters and obtain the desired control performance without exact information about the mathematical formulation of model.
First-Order ARMA Type Fuzzy Time Series Method Based on Fuzzy Logic Relation Tables
Cem Kocak
2013-01-01
Fuzzy time series approaches have an important deficiency according to classical time series approaches. This deficiency comes from the fact that all of the fuzzy time series models developed in the literature use autoregressive (AR) variables, without any studies that also make use of moving averages (MAs) variables with the exception of only one study (Egrioglu et al. (2013)). In order to eliminate this deficiency, it is necessary to have many of daily life time series be expressed with Aut...
F. S. Marzano; Scaranari, D.; Celano, M.; Alberoni, P. P.; Vulpiani, G.; Montopoli, M.
2006-01-01
International audience A model-based fuzzy classification method for C-band polarimetric radar data, named Fuzzy Radar Algorithm for Hydrometeor Classification at C-band (FRAHCC), is presented. Membership functions are designed for best fitting simulation data at C-band, and they are derived for ten different hydrometeor classes by means of a scattering model, based on T-Matrix numerical method. The fuzzy logic classification technique uses a reduced set of polarimetric observables, i.e. c...
Sandra Aparecida Santos; Helano Póvoa Lima; Humberto Perotto Baldivieso; Luíz Orcirio Oliveira; Walfrido Moraes Tomás
2015-01-01
The regional feasibility of ranching (RFR) index was obtained in order to evaluate the productive potential of farms in the Pantanal. Five indicators were selected by expert and employed for the developing of the index. One of the five indicators corresponded to the natural potential for livestock ranching (NPLR) index which was generated by GIS-fuzzy logic. Fuzzy inference process, involving definitions of membership functions, fuzzy set operations and inference rules was implemented and val...
A Fuzzy Logic Based Controller for the Automated Alignment of a Laser-beam-smoothing Spatial Filter
Krasowski, M. J.; Dickens, D. E.
1992-01-01
A fuzzy logic based controller for a laser-beam-smoothing spatial filter is described. It is demonstrated that a human operator's alignment actions can easily be described by a system of fuzzy rules of inference. The final configuration uses inexpensive, off-the-shelf hardware and allows for a compact, readily implemented embedded control system.
Transparent fuzzy logic based methods for some human resource problems
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Canós-Darós, L.
2012-01-01
Full Text Available Personnel selection and reallocation are human resources policies that should be planned andimplemented accurately because of their importance to the future of the company. When a person is hired, he or sherepresents an investment in human capital. This is the reason because managers have to use different tools in order tomake good decisions. In this paper we present some fuzzy tools for personnel selection and reallocation processes andthe advantages and disadvantages of their applying. Moreover, we show some examples, including the use of a specific software.
Fuzzy Logic Control of Single Phase Matrix Converter Fed Induction Heating System
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P. Umasankar
2014-07-01
Full Text Available This article represents the modeling and simulation of a Single Phase Matrix Converter (SPMC fed Induction Heating (IH system. The working principle and the control system using Fuzzy Logic Controller (FLC are elucidated in detail. The performance of the system and their harmonic content analysis of Single Phase Matrix Converter are carried out in MATLAB/Simulink environment. Pulse Width Modulation (PWM switching strategy by varying the duty cycle based on Fuzzy Logic Control is employed to obtain better performance for a constant voltage, constant frequency input supply for various output frequencies. The proposed control strategy results achieve low Total Harmonic Distortion (THD for various operating frequencies without large reactive storage elements.
DSA Image Fusion Based on Dynamic Fuzzy Logic and Curvelet Entropy
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Guangming Zhang
2009-06-01
Full Text Available The curvelet transform as a multiscale transform has directional parameters occurs at all scales, locations, and orientations. It is superior to wavelet transform in image processing domain. This paper analyzes the characters of DSA medical image, and proposes a novel approach for DSA medical image fusion, which is using curvelet information entropy and dynamic fuzzy logic. Firstly, the image was decomposed by curvelet transform to obtain the different level information. Then the entropy from different level of DSA medical image was calculated, and a membership function based on dynamic fuzzy logic was constructed to adjust the weight for image subbands coefficients via entropy. At last an inverse curvelet transform was applied to reconstruct the image to synthesize one DSA medical image which could contain more integrated accurate detail information of blood vessels than any one of the individual source images. By compare, the efficiency of our method is better than weighted average, laplacian pyramid and traditional wavelet transform method.
Fuzzy Logic Navigation and Obstacle Avoidance by a Mobile Robot in an Unknown Dynamic Environment
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Mohammed Faisal
2013-01-01
Full Text Available Mobile robot navigation has remained an open problem over the last two decades. Mobile robots are required to navigate in unknown and dynamic environments, and in recent years the use of mobile robots in material handling has considerably increased. Usually workers push carts around warehouses and manually handle orders which is not very cost‐effective. To this end, a potential method to control a swarm of mobile robots in a warehouse with static and dynamic obstacles is to use the wireless control approach. Further, to be able to control different types of mobile robots in the warehouse, the fuzzy logic control approach has been chosen. Therefore, in this paper, an on‐line navigation technique for a wheeled mobile robot (WMR in an unknown dynamic environment using fuzzy logic techniques is investigated. In this paper, we aim to use the robot in application in a warehouse. Experimental results show the effectiveness of the proposed algorithm.
A tunable fuzzy logic controller for the vehicle semi-active suspension system
Institute of Scientific and Technical Information of China (English)
方子帆; DENG; Zhaoxiang; 等
2002-01-01
On the basis of analyzing the system constitution of vehicle semi-active suspension,a 4-DOF(degree of freedom)dynamic model is established.A tunable fuzzy logic controller is designed by using without quantification method and taking into account the uncertainty,nonlinearity and complexity of parameters for a vehicle suspension system.Simulation to test the performance of this controller is performed under random excitations and definite disturbances of a C grade road,and the effects of time delay and changes of system parameters on the vehicle suspension system are researched.The numerical simulation shows that the performance of the designed tunable fuzzy logic controller is effective,stable and reliable.
Brandão, Euzeli da Silva; dos Santos, Iraci; Lanzillotti, Regina Serrão; Moreira, Augusto Júnior
2013-08-01
The objective was to propose the use of Fuzzy Logic for recognition of comfort patterns in people undergoing a technology of nursing care because of pemphigus vulgaris, a rare mucocutaneous disease that affects mainly adults. The proposal applied experimental methods, with subjects undergoing a qualitative-quantitative comparison (taxonomy/relevance) of the comfort patterns before and after the intervention. A record of a chromatic scale corresponding to the intensity of each attribute was required: pain, mobility and impaired self-image. The Fuzzy rules established by an inference engine set the standard for comfort in maximum, median and minimum discomfort, reflecting the effectiveness of nursing care. Although rarely used in the area of nursing, this logic enabled viable research without a priori scaling of the number of subjects depending on the estimation of population parameters. It is expected to evaluate the pattern of comfort in the client with pemphigus, before the applied technology, in a personalized way, leading to a comprehensive evaluation.
Fuzzy logic merger of spectral and ecological information for improved montane forest mapping.
White, Joseph D.; Running, Steven W.; Ryan, Kevin C.; Key, Carl H.
2002-01-01
Environmental data are often utilized to guide interpretation of spectral information based on context, however, these are also important in deriving vegetation maps themselves, especially where ecological information can be mapped spatially. A vegetation classification procedure is presented which combines a classification of spectral data from Landsat‐5 Thematic Mapper (TM) and environmental data based on topography and fire history. These data were combined utilizing fuzzy logic where assignment of each pixel to a single vegetation category was derived comparing the partial membership of each vegetation category within spectral and environmental classes. Partial membership was assigned from canopy cover for forest types measured from field sampling. Initial classification of spectral and ecological data produced map accuracies of less than 50% due to overlap between spectrally similar vegetation and limited spatial precision for predicting local vegetation types solely from the ecological information. Combination of environmental data through fuzzy logic increased overall mapping accuracy (70%) in coniferous forest communities of northwestern Montana, USA.
Yuan, Kebin; Parri, Andrea; Yan, Tingfang; Wang, Long; Munih, Marko; Vitiello, Nicola; Wang, Qining
2015-01-01
In this paper, we present a fuzzy-logic-based hybrid locomotion mode classification method for an active pelvis orthosis. Locomotion information measured by the onboard hip joint angle sensors and the pressure insoles is used to classify five locomotion modes, including two static modes (sitting, standing still), and three dynamic modes (level-ground walking, ascending stairs, and descending stairs). The proposed method classifies these two kinds of modes first by monitoring the variation of the relative hip joint angle between the two legs within a specific period. Static states are then classified by the time-based absolute hip joint angle. As for dynamic modes, a fuzzy-logic based method is proposed for the classification. Preliminary experimental results with three able-bodied subjects achieve an off-line classification accuracy higher than 99.49%.
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Fatih Korkmaz
2013-11-01
Full Text Available The induction motors are indispensable motor types for industrial applications due to its wellknown advantages. Therefore, many kind of control scheme are proposed for induction motors over the past years and direct torque control has gained great importance inside of them due to fast dynamic torque response behavior and simple control structure. This paper suggests a new approach on the direct torque controlled induction motors, Fuzzy logic based space vector modulation, to overcome disadvantages of conventional direct torque control like high torque ripple. In the proposed approach, optimum switching states are calculated by fuzzy logic controller and applied by space vector pulse width modulator to voltage source inverter. In order to test and compare the proposed DTC scheme with conventional DTC scheme simulations, in Matlab/Simulink, have been carried out in different speed and load conditions. The simulation results showed that a significant improvement in the dynamic torque and speed responses when compared to the conventional DTC scheme.
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Phaneendra Venkat Gatta
2014-05-01
Full Text Available The wind energy conversion system Wind Farms (WF are employ squirrel cage induction generator (SCIG. The injection of wind power into an electric grid causes the power quality problems such as variation of voltage, flicker, harmonics etc, and these are measured according to national/international guidelines. To solve these problems Custom Power Devices (CUPS are used. This paper has proposed a compensation strategy based on a particular cups device for Unified Power Quality Compensator (UPQC with an application of PI and Fuzzy Logic Controllers. The proposed strategy controls both active and reactive power in the converters of UPQC. This paper presents the comparison between without UPQC, UPQC with PI controller and UPQC with Fuzzy Logic Controller. By using MATLAB/SIMULINK software the control strategies are designed. The simulation results are shown for comparison of different control strategies and by performing FFT analysis Total Harmonic Distortions (THD are calculated.
Fuzzy logic control strategy for submerged arc automatic welding of digital controlling
Institute of Scientific and Technical Information of China (English)
He Kuanfang; Huang Shisheng; Zhou Yiqing; Wang Zhenmin
2008-01-01
A microcomputer control system based on 80C320 and a switching regulation of wire feeder were designed. A correction factor based double model fuzzy logic controller (FLC) was introduced to achieve welding digital and intellectualized control by means of wire feeding speed feedback. The controller has many functions such as keyboard input, light emitting diode (LED) display and real-time intellectualized control of welding process etc. The controlling performance influenced by the coefficient of correction function was discussed. It was concluded by the experiments the relation between the coefftcient of correction function and welding quality, when the coefficient of correction function is great, the dynamic character of controller is better, when the coefficient of correction function is small, the sensitivity character of controller is better. Experimental results also show that digital and fuzzy logic control method enable the improvement of appearance of weld and stability of welding process to be achieved in submerged arc automatic welding.
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Nirban Chakraborty
2014-04-01
Full Text Available This paper presents the speed control scheme of direct vector control of Induction Motor drive (IM drive. The Fuzzy logic controller is (FLC used as the controller part here for the direct vector control of Induction Motor using Sinusoidal PWM Inverter (SPWM. Fuzzy logic controller has become a very popular controlling scheme in the field of Industrial application. The entire module of this IM is divided into several parts such as IM body module, Inverter module, coordinate transformation module and Sinusoidal pulse width modulation (SPWM production module and so on. With the help of this module we can analyze a variety of different simulation waveforms, which provide an effective means for the analysis and design of the IM control system using FLC technique.
Elimination & Mitigation of Sag & Swell Using a New UPQC-S Methodology & Fuzzy Logic Controller
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Kanaka Raju Kalla,
2014-05-01
Full Text Available This paper presents the enhancement of voltage sags, harmonic distortion and low power factor using Unified Power Quality Conditioner (UPQC with Fuzzy Logic Controller in distribution system, The series inverter of UPQC is controlled to perform simultaneous 1 voltage sag/swell compensation and 2 load reactive power sharing with the shunt inverter. Since the series inverter simultaneously delivers active and reactive powers, this concept is named as UPQC-S (S for complex power in this paper; a detailed mathematical formulation of PAC for UPQC-S is carried out. In this paper details are carried out on both series inverter & shunt inverter, and fuzzy logic controller is applied to shunt inverter in order to dc fluctuations and to compensate reactive power. The feasibility and effectiveness of the proposed UPQC-S approach are validated by simulation in using MATLAB software.
Optimization of the Fermentation Process in a Brewery with a Fuzzy Logic Controller
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Philip B. OSOFISAN
2007-08-01
Full Text Available In this research work, the fermentation process in a Brewery will be optimized, with the application of Fuzzy Logic Controller (FLC. Fermentation is controlled by regulating the temperature, the oxygen content and the pitch rate; but the temperature plays a dominant role in the optimization of the fermentation process. For our case study (Guinness Nigeria Plc the optimal fermentation temperature is 16ºC, so the FLC has been designed to maintain this temperature. The designed FLC can also be applied to maintain any other optimal fermentation temperature e.g. 20ºC. These two cases have been investigated. The FLC has been stimulated on a digital computer using MATLAB 5.0 Fuzzy Logic Tool Box.
Yuan, Kebin; Parri, Andrea; Yan, Tingfang; Wang, Long; Munih, Marko; Vitiello, Nicola; Wang, Qining
2015-08-01
In this paper, we present a fuzzy-logic-based hybrid locomotion mode classification method for an active pelvis orthosis. Locomotion information measured by the onboard hip joint angle sensors and the pressure insoles is used to classify five locomotion modes, including two static modes (sitting, standing still), and three dynamic modes (level-ground walking, ascending stairs, and descending stairs). The proposed method classifies these two kinds of modes first by monitoring the variation of the relative hip joint angle between the two legs within a specific period. Static states are then classified by the time-based absolute hip joint angle. As for dynamic modes, a fuzzy-logic based method is proposed for the classification. Preliminary experimental results with three able-bodied subjects achieve an off-line classification accuracy higher than 99.49%. PMID:26737144
Methodology of analysis sustainable development of Ukraine by using the theory fuzzy logic
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Methodology of analysis sustainable development of Ukraine by using the theory fuzzy logic
2016-02-01
Full Text Available Article objective is analysis of the theoretical and methodological aspects for the assessment of sustainable development in times of crisis. The methodical approach to the analysis of sustainable development territory taking into account the assessment of the level of economic security has been proposed. A necessity of development of the complex methodical approach to the accounting of the indeterminacy properties and multicriterial in the tasks to provide economic safety on the basis of using the fuzzy logic theory (or the fuzzy sets theory was proved. The results of using the method of fuzzy sets of during the 2002-2012 years the dynamics of changes dynamics of sustainable development in Ukraine were presented.
The stock-flow model of spatial data infrastructure development refined by fuzzy logic.
Abdolmajidi, Ehsan; Harrie, Lars; Mansourian, Ali
2016-01-01
The system dynamics technique has been demonstrated to be a proper method by which to model and simulate the development of spatial data infrastructures (SDI). An SDI is a collaborative effort to manage and share spatial data at different political and administrative levels. It is comprised of various dynamically interacting quantitative and qualitative (linguistic) variables. To incorporate linguistic variables and their joint effects in an SDI-development model more effectively, we suggest employing fuzzy logic. Not all fuzzy models are able to model the dynamic behavior of SDIs properly. Therefore, this paper aims to investigate different fuzzy models and their suitability for modeling SDIs. To that end, two inference and two defuzzification methods were used for the fuzzification of the joint effect of two variables in an existing SDI model. The results show that the Average-Average inference and Center of Area defuzzification can better model the dynamics of SDI development.
Power System Stabilizer Driven by an Adaptive Fuzzy Set for Better Dynamic Performance
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H. F. Soliman
2006-01-01
Full Text Available This paper presents a novel application of a fuzzy logic controller (FLC driven by an adaptive fuzzy set (AFS for a power system stabilizer (PSS.The proposed FLC, driven by AFS, is compared with a classical FLC, driven by a fixed fuzzy set (FFS. Both FLC algorithms use the speed error and its rate of change as input vectors. A single generator equipped with FLC-PSS and connected to an infinite bus bar through double transmission lines is considered. Both FLCs, using AFS and FFS, are simulated and tested when the system is subjected to different step changes in the reference value. The simulation results of the proposed FLC, using the adaptive fuzzy set, give a better dynamic response of the overall system by improving the damping coefficient and decreasing the rise time and settling time compared with classical FLC using FFS. The proposed FLC using AFS also reduces the computational time of the FLC as the number of rules is reduced.
Ensemble of ground subsidence hazard maps using fuzzy logic
Park, Inhye; Lee, Jiyeong; Saro, Lee
2014-06-01
Hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok, Korea, were constructed using fuzzy ensemble techniques and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, groundwater, and ground subsidence maps. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 70/30 for training and validation of the models. The relationships between the detected ground-subsidence area and the factors were identified and quantified by frequency ratio (FR), logistic regression (LR) and artificial neural network (ANN) models. The relationships were used as factor ratings in the overlay analysis to create ground-subsidence hazard indexes and maps. The three GSH maps were then used as new input factors and integrated using fuzzy-ensemble methods to make better hazard maps. All of the hazard maps were validated by comparison with known subsidence areas that were not used directly in the analysis. As the result, the ensemble model was found to be more effective in terms of prediction accuracy than the individual model.
Methods of development fuzzy logic driven decision-support models in copper alloys processing
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S. Kluska-Nawarecka
2010-01-01
Full Text Available Development of a diagnostic decision support system using different then divalent logical formalism, in particular fuzzy logic, allows the inference from the facts presented not as explicit numbers, but described by linguistic variables such as the "high level", "low temperature", "too much content", etc. Thanks to this, process of inference resembles human manner in actual conditions of decision-making processes. Knowledge of experts allows him to discover the functions describing the relationship between the classification of a set of objects and their characteristics, on the basis of which it is possible to create a decision-making rules for classifying new objects of unknown classification so far. This process can be automated. Experimental studies conducted on copper alloys provide large amounts of data. Processing of these data can be greatly accelerated by the classification trees algorithms which provides classes that can be used in fuzzy inference model. Fuzzy logic also provides the flexibility of allocating to classes on the basis of membership functions (which is similar to events in real-world conditions. Decision-making in foundry operations often requires reliance on knowledge incomplete and ambiguous, hence that the conclusions from the data and facts may be "to some extent" true, and the technologist has to determine what level of confidence is acceptable, although the degree of accuracy for specific criteria is defined by membership function, which takes values from interval . This paper describes the methodology and the process of developing fuzzy logic-based models of decision making based on preprocessed data with classification trees, where the needs of the diverse characteristics of copper alloys processing are the scope. Algorithms for automatic classification of the materials research work of copper alloys are clearly the nature of the innovative and promising hope for practical applications in this area.
Traffic Engineering and Quality of Experience in MPLS Network by Fuzzy Logic characterization
Satya Prakash Rout; Palash Ghosal
2015-01-01
This paper proposes a load balancing algorithm using fuzzy logic so that maximum Quality of Experience can be achieved. Avoidance of congestion is one of the major performance objectives of traffic engineering in MPLS networks. Load balancing can prevent the congestion caused due to inefficient allocation of network resources. Another aspect of the network performance is Quality of Experience (QoE). QoE in telecommunications terminology, it is a measurement used to determine how w...
Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks
M. Bazazzadeh; Badihi, H.; A Shahriari
2011-01-01
This paper presents a successful approach in designing a Fuzzy Logic Controller (FLC) for a specific Jet Engine. At first, a suitable mathematical model for the jet engine is presented by the aid of SIMULINK. Then by applying different reasonable fuel flow functions via the engine model, some important engine-transient operation parameters (such as thrust, compressor surge margin, turbine inlet temperature, etc.) are obtained. These parameters provide a precious database, which train a neural...
Fuzzy logic for large mining bucket wheel reclaimer motion control——from an engineer's perspective
Institute of Scientific and Technical Information of China (English)
LU Tienfu
2011-01-01
The bucket wheel reclaimer(BWR)is a key piece of equipment which has been widely used for stacking and reclaiming bulk materials(i. e. iron ore and coal)in places such as ports, iron-steel plants, coal storage areas, and power stations from stockpiles. BWRs are very large in size, heavy in weight, expensive in price, and slow in motion. There are many challenges in attempting to automatically control their motion to accurately follow the required trajectories involving uncertain parameters from factors such as friction, turbulent wind, its own dynamics, and encoder limitations. As BWRs are always heavily engaged in production and cannot be spared very long for motion control studies and associated developments, a BWR model and simulation environment closely resembling real life conditions would be beneficial. The following research focused mainly on the implementation of fuzzy logic to a BWR motion control from an engineer's perspective. First, the modeling of a BWR including partially known parameters such as friction force and turbulence to the system was presented. This was then followed by the design of a fuzzy logic-based control built on a model-based control loop. The investigation provides engineers with an example of applying fuzzy logic in a model based approach to properly control the motion of a large BWR following defined trajectories, as well as to show possible ways of further improving the controller performance. The result indicates that fuzzy logic can be applied easily by engineers to overcome most motion control issues involving a large BWR.