Showing papers on "Neuro-fuzzy published in 1995"

Neuro-fuzzy modeling and control

Abstract: Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called adaptive-network-based fuzzy inference system (ANFIS), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed. >

Fuzzy logic systems for engineering: a tutorial

Abstract: A fuzzy logic system (FLS) is unique in that it is able to simultaneously handle numerical data and linguistic knowledge. It is a nonlinear mapping of an input data (feature) vector into a scalar output, i.e., it maps numbers into numbers. Fuzzy set theory and fuzzy logic establish the specifics of the nonlinear mapping. This tutorial paper provides a guided tour through those aspects of fuzzy sets and fuzzy logic that are necessary to synthesize an FLS. It does this by starting with crisp set theory and dual logic and demonstrating how both can be extended to their fuzzy counterparts. Because engineering systems are, for the most part, causal, we impose causality as a constraint on the development of the FLS. After synthesizing a FLS, we demonstrate that it can be expressed mathematically as a linear combination of fuzzy basis functions, and is a nonlinear universal function approximator, a property that it shares with feedforward neural networks. The fuzzy basis function expansion is very powerful because its basis functions can be derived from either numerical data or linguistic knowledge, both of which can be cast into the forms of IF-THEN rules. >

Selecting fuzzy if-then rules for classification problems using genetic algorithms

Abstract: This paper proposes a genetic-algorithm-based method for selecting a small number of significant fuzzy if-then rules to construct a compact fuzzy classification system with high classification power. The rule selection problem is formulated as a combinatorial optimization problem with two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy if-then rules. Genetic algorithms are applied to this problem. A set of fuzzy if-then rules is coded into a string and treated as an individual in genetic algorithms. The fitness of each individual is specified by the two objectives in the combinatorial optimization problem. The performance of the proposed method for training data and test data is examined by computer simulations on the iris data of Fisher. >

Combining multiple neural networks by fuzzy integral for robust classification

Abstract: In the area of artificial neural networks, the concept of combining multiple networks has been proposed as a new direction for the development of highly reliable neural network systems. The authors propose a method for multinetwork combination based on the fuzzy integral. This technique nonlinearly combines objective evidence, in the form of a fuzzy membership function, with subjective evaluation of the worth of the individual neural networks with respect to the decision. The experimental results with the recognition problem of on-line handwriting characters confirm the superiority of the presented method to the other voting techniques. >

Essentials of fuzzy modeling and control

Abstract: el->e2. The intended interpretation here is that whenever el occurs, so does e2 (or, el causes e2). However, the typicality operator can be used here to represent the fact that \"typically\" el causes e2, but not always. For example, if el=strike-thematch and e2=light-the-match, then typically, el causes e2, but not when additional information such as e3=match-is-wet is added. Thus, the preference criteria developed to deal with declarative statements can be used to retract unplausible chains of causal effects given new information. The author than develops these events into complex frames and scripts, and describes in some detail the representation of an isa hierarchy using the statements in typicality logic. It should be noted here that this effort does not differ substantially fi'om similar work on formalizing semantic networks except in the identification ofstatements in a given frame. For example, while every slot-value pair in a frame can be represented by a wff in predicate logic, a number of these formulas will be monotonic, while some will be typicality statements.

Hybrid intelligent systems

Abstract: Foreword L.A. Zadeh. Preface. 1. Overview of Intelligent Systems. 2. Research in Hybrid Intelligent Systems. 3. Expert Systems and Neural Networks.4. Industrial Experience: The Use of Hybrid Systems in the Power Industry. 5. Expert Networks. 6. Fuzzy Logic and Expert Systems. 7. Fuzzy Systems and Neural Networks. 8. Genetic Algorithms and Neural Networks. 9. Applications Using Hybrid Neural Networks with Fuzzy Logic and Genetic Algorithms. 10. Genetic Algorithms and Fuzzy Systems. 11. Adaptive Control of an Exothermic Chemical Reaction System Using Fuzzy Logic and Genetic Algorithms. 12. Genetic Algorithms and Expert Systems. 13. Hybrid Systems with Case-Based Reasoning. 14. Summary and the Future of Hybrid Intelligent Systems. References. Index.

A method for fuzzy rules extraction directly from numerical data and its application to pattern classification

Abstract: In this paper, we discuss a new method for extracting fuzzy rules directly from numerical input-output data for pattern classification Fuzzy rules with variable fuzzy regions are defined by activation hyperboxes which show the existence region of data for a class and inhibition hyperboxes which inhibit the existence of data for that class These rules are extracted from numerical data by recursively resolving overlaps between two classes Then, optimal input variables for the rules are determined using the number of extracted rules as a criterion The method is compared with neural networks using the Fisher iris data and a license plate recognition system for various examples >

A sensor-based navigation for a mobile robot using fuzzy logic and reinforcement learning

Abstract: The proposed navigator consists of an avoidance behavior and goal-seeking behavior. Two behaviors are independently designed at the design stage and then combined them by a behavior selector at the running stage. A behavior selector using a bistable switching function chooses a behavior at each action step so that the mobile robot can go for the goal position without colliding with obstacles. Fuzzy logic maps the input fuzzy sets representing the mobile robot's state space determined by sensor readings to the output fuzzy sets representing the mobile robot's action space. Fuzzy rule bases are built through the reinforcement learning which requires simple evaluation data rather than thousands of input-output training data. Since the fuzzy rules for each behavior are learned through a reinforcement learning method, the fuzzy rule bases can be easily constructed for more complex environments. In order to find the mobile robot's present state, ultrasonic sensors mounted at the mobile robot are used. The effectiveness of the proposed method is verified by a series of simulations. >

Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence

Abstract: From the Publisher: Presents a theory of uncertainty, consistent with and combining the theories of probability and fuzzy sets. Extends the logic programming form of knowledge representation and method of inference to permit the inclusion of uncertainties such as probabilistic knowledge and fuzzy incompleteness. Describes the application to general areas of knowledge engineering including expert and decision-support systems, evidential and case-based reasoning, fuzzy control and databases. An accompanying disk for Macintosh and one for the IBM PC enables readers to implement the examples while following the text.

Intelligent Control: Fuzzy Logic Applications

Abstract: CONVENTIONAL AND INTELLIGENT CONTROL Introduction Conventional Control Techniques Summary Problems References KNOWLEDGE REPRESENTATION AND PROCESSING Introduction Knowledge and Intelligence Logic Semantic Networks Frames Production Systems Summary Problems References FUNDAMENTALS OF FUZZY LOGIC Introduction Fuzzy Sets Fuzzy Logic Operations Some Definitions Fuzzy Relations Composition and Inference Membership Function Estimation Summary Problems References FUZZY LOGIC CONTROL Introduction Basics of Fuzzy Control Decision Making with Crisp Measurements Defuzzification Architectures of Fuzzy Control Summary Problems References KNOWLEDGE-BASED TUNING Introduction Theoretical Background Analytical Framework Computational Efficiency Dynamic Switching of Fuzzy Resolution Illustrative Example Summary Problems References KNOWLEDGE-BASED CONTROL OF ROBOTS Introduction Robotic Control System Application to Robots In-Loop Direct Control High-Level Fuzzy Control Control Hierarchy System Development Servo Expert Development Summary Problems References SERVO MOTOR TUNING Introduction System Development Results Theory of Rule Base Decoupling Experimental Illustration Summary Problems References HIERARCHICAL FUZZY CONTROL Introduction General Concepts Hierarchical Model Effect of Information Processing Application in Process Control Summary Problems References INTELLIGENT RESTRUCTURING OF PRODUCTION SYSTEMS Introduction Theoretical Framework Implementation Using a Blackboard Architecture Case Study Summary Problems References FUTURE APPLICATIONS Introduction Intelligence in Automation Intelligent Multiagent Control Reconfigurable Autonomous Manipulators Intelligent Fusion of Sensors and Actuators Mechatronics Era Conclusion Problems References APPENDIX A: Further Topics on Fuzzy Logic APPENDIX B: Software Tools for Fuzzy Logic Applications Index

Fuzzy sets engineering

Abstract: From the Publisher: The phenomenon of fuzzy sets amplified by their numerousapplications has triggered a significant amount of interest among research communities as well as practitioners in many areas including engineering, ecology, economics, administration, and business. What is the role of fuzzy sets? When should they be used? Why do they work? What are good design practices? This book presents the genuine essence of engineering of fuzzy sets. It includes sound theory, a general methodological framework, efficient algorithms, and detailed validation schemes. Fuzzy Sets Engineering presents discussions in a top-down fashion, with general methodology followed by specific domains which rely strongly on the methodological foundations. Based on this methodological framework, the book then provides a careful, in-depth exposure to very diversified areas. Numerous application-driven examples are offered. Text discusses general modelling methodology of fuzzy sets then describes useful ideas of neurocomputations. Self-contained chapters allow readers to customize their reading by selecting any of these essential design topics: fuzzy controller, fuzzy control, or information processing with recurrent systems such as fuzzy flip-flops or fuzzy Petri nets. Topics can be investigated in a variety of orders. This versatile format makes this an ideal textbook or reference source for both novices and experienced individuals.

Industrial Applications of Fuzzy Logic and Intelligent Systems

Abstract: Downloading the book in this website lists can give you more advantages. It will show you the best book collections and completed collections. So many books can be found in this website. So, this is not only this industrial applications of fuzzy logic and intelligent systems. However, this book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts. This is simple, read the soft file of the book and you get it.

Fuzzy logic and NeuroFuzzy applications explained

Abstract: 1. Fuzzy Logic in Minutes. 2. Fuzzy Logic Primer. 3. Development Tools for Fuzzy Systems. 4. NeuroFuzzy Technologies. 5. Case Studies of Industrial Applications. 6. Fuzzy Design Cookbook. 7. Using the Software. 8. Comparing Fuzzy vs. Conventional Control. References. Index.

NEFCLASSmdash;a neuro-fuzzy approach for the classification of data

Abstract: In this paper we present NEFCLASS, a neuro-fuzzy system for the classification of data. This approach is based on our generic model of a fuzzy perceptron which can be used to derive fuzzy neural networks or neural fuzzy systems for specific domains. The presented model derives fuzzy rules from data to classify patterns into a number of (crisp) classes. NEFCLASS uses a supervised learning algorithm based on fuzzy error backpropagation that is used in other derivations of the fuzzy perceptron. Introduction Combinations of neural networks and fuzzy systems are very popular (for an overview see [4, 6]), but most of the approaches are not easy to compare because they use very different architectures, activation functions, propagation and learning algorithms, etc. In [s] we presented a fuzzy perceptron as a generic model of multilayer fuzzy neural networks. It can be used as a common base for neuro-fuzzy architectures in order to ease the comparision of different approaches. By applying additional constraints to the definition of the fuzzy perceptron one can e.g. obtain a structure that can be interpreted as a usual fuzzy controller, and easily create a neuro-fuzzy controller this way [3, 8, 91. In thii paper we present an approach to neuro-fuzzy data analysis. The goal is to derive fuzzy rules from a set of data that can be separated in different crisp classes, i.e. at this moment we do not consider data where the patterns belong to overlapping or fuzzy categories. The fuzziness involved is due to an imperfect or incomplete measurement of features thus rendering it difficult to assign a pattern to the correct category. “Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commerical advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission.” @ 19% ACM O-89791-658-1 95 COO2 3.50 The fuzzy rules describing the data are of the form: if ~~ is g1 and zz is ~2 and . . . end zn is p, then the pattern (21,22, . . . ,z,) belongs to class i, where~l,... , pn are fuzzy sets. The task of the NEFCLASS model is to discover these rules and to learn the shape of the membership functions. We will first briefly present the fuzzy perceptron model in section II, and in section III we show how the NEFCLASS model is derived from it. We also present the supervised learning algorithm. In the fourth section we discuss the learning results we got by applying NEFCLASS to the SRIS data set, and we compare the results to other approaches. The Fuzzy Perceptron A fuzzy perceptron has the architecture of an usual multilayer perceptron, but the weights are modelled as fuzzy sets and the activation, output, and propagation functions are changed accordingly. The intention of this model is to be interpretable in form of linguistic rules and to be able to use prior rule based knowledge, so the learning has not to start from scratch. In [s] we suggested a generic model for fuzzy neural networks based on a 3-layer fuzzy perceptron. By using it to derive neural fuzzy systems for special domains, it would be possible to evaluate these diierent neuro-fuzzy approaches by means of the same underlying model. The fuzzy perceptron was used to derive the NEFCON model [3, 8, 91 for neuro-fuzzy controll applications, and it is now used to define the NEFCLASS model discussed in this paper. We will therefore shortly present the definition of the generic fuzzy perceptron. Definition 1 A %-layer fuzzy perceptron is D S-layer feedforward neural network (U, W, NET, A, 0, ex) with the following specifications: (i) I! = U U; is a non-empty set of units (neurons) igM andM={1,2,3} is theindezsetofu. Foralli,jE M, U; # B and U; n Uj = t? with i # j holds. U1 is called input layer, Us rule layer (hidden layer), and Ii3 output layer. (ii) The structure of the network (connections) is defined as W : U x U --+ T(R), such that there are only connections W(u, w) with u E Cr,, u E U,+I : E all fuzzy subsets of L). {1,2)) (T(R) is the set of

Fuzzy sets, fuzzy logic, fuzzy methods with applications

Abstract: Fuzzy Sets: Basic Notions Examples of Fuzzy Sets Operations with Fuzzy Sets t-norm-Based Operations Fuzzy Numbers and Their Arithmetic Fuzzifified Relationships: Fuzzy Relations Properties of Fuzzy Relations Fuzzy Relationships between Variables Fuzzy Programming Linguistic Variables: The Notion of a Linguistic Variable Fuzzy Control Relational Equations and Fuzzy Control Approximate Reasoning Examples for Applications of Fuzzy Controllers Measure Theory and Fuzzy Sets: Fuzzy Measures for Crisp Sets Fuzzy Measures for Fuzzy Sets Fuzziness and Probability Some Applications Fuzziness Measures Fuzzy Data Analysis: Data and Their Analysis Qualitative Data Analysis Quantitative Data Analysis Evaluation of Methods.

Short term load forecasting using fuzzy neural networks

Abstract: This paper presents the development of a fuzzy system for short term load forecasting. The fuzzy system has the network structure and the training procedure of a neural network and is called a fuzzy neural network (FNN). An FNN initially creates a rule base from existing historical load data. The parameters of the rule base are then tuned through a training process, so that the output of the FNN adequately matches the available historical load data. Once trained, the FNN can be used to forecast future loads. Test results show that the FNN can forecast future loads with an accuracy comparable to that of neural networks, while its training is much faster than that of neural networks. >

Design and analysis of fuzzy identifiers of nonlinear dynamic systems

Abstract: Uses fuzzy systems as identifiers for nonlinear dynamic systems. The author provides a theoretical justification for the fuzzy identifiers by proving that they are capable of following the output of a general nonlinear dynamic system to arbitrary accuracy in any finite time interval. The fuzzy identifiers are constructed from a set of adaptable fuzzy IF-THEN rules and can combine both numerical information (in the form of input-output pairs obtained by exciting the system with an input signal and measuring the corresponding outputs) and linguistic information (in the form of IF-THEN rules about the behavior of the system in terms of vague and fuzzy words) into their designs in a uniform fashion. The author develops two fuzzy identifiers. The first one is designed through the following four steps: 1) define some fuzzy sets in the state space U/spl sub/R/sup n/ of the system; these fuzzy sets do not change; 2) construct fuzzy rule bases of the fuzzy identifier which comprise rules whose IF parts constitute all the possible combinations of the fuzzy sets defined in 1); 3) design the fuzzy systems in the fuzzy identifier based on the fuzzy rule bases of 2); and 4) develop an adaptive law for the free parameters in the fuzzy identifier. The second fuzzy identifier is designed in a similar way as the first one except that: a) the parameters characterizing the fuzzy sets in the state space change during the adaptation procedure; and b) the fuzzy systems and the adaptive law are different. The author proves that: 1) both fuzzy identifiers are globally stable in the sense that all variables in the fuzzy identifiers are uniformly bounded, and 2) under some conditions the identification errors of both fuzzy identifiers converge to zero asymptotically. Finally, the author simulates the fuzzy identifiers for identifying the chaotic glycolytic oscillator, and the results show that: 1) the fuzzy identifiers can approximate the chaotic system at a reasonable speed and accuracy without using any linguistic information, and 2) by incorporating some fuzzy linguistic IF-THEN rules about the behavior of the system into the fuzzy identifiers, the speed and accuracy of the fuzzy identifiers are greatly improved. >

Multiple network fusion using fuzzy logic

Abstract: Multiplayer feedforward networks trained by minimizing the mean squared error and by using a one of c teaching function yield network outputs that estimate posterior class probabilities. This provides a sound basis for combining the results from multiple networks to get more accurate classification. This paper presents a method for combining multiple networks based on fuzzy logic, especially the fuzzy integral. This method non-linearly combines objective evidence, in the form of a network output, with subjective evaluation of the importance of the individual neural networks. The experimental results with the recognition problem of on-line handwriting characters show that the performance of individual networks could be improved significantly. >

Genetic algorithms for fuzzy control.1. Offline system development and application

Abstract: Although fuzzy logic controllers and expert systems have been successfully applied in many complex industrial processes, they experience a deficiency in knowledge acquisition and rely to a great extent on empirical and heuristic knowledge which, in many cases, cannot be objectively elicited. Among the problems to be resolved in fuzzy controller design are the determination of the linguistic state space, definition of the membership functions of each linguistic term and the derivation of the control rules. Some of these problems can be solved by application of machine learning. First, it is desirable to simplify and automate the specification of linguistic rules. Secondly, it is also desirable that modification of control rules is possible in order to cope with previously unknown or changes in process dynamics. Machine learning methods have, in recent years, emerged from the use of learning algorithms modelled on natural and biological systems. These methods attempt to abstract the advanced mechanisms of learning exhibited by such systems, which can consequently be applied to intelligent control. One of these new algorithms is the genetic algorithm which is modelled on the processes of natural evolution. The paper develops the application of genetic algorithm techniques for fuzzy controller design. Genetic algorithms are used to automate and introduce objective criteria in defining fuzzy controller parameters.

Neural Networks for Chemical Engineers

Abstract: Part 1 Theoretical issues: an introduction to artificial neural networks, D.T. Pham unsupervised neural learning, E. Oja back-propagation and its variations, D. Tsaptsinos the general approximation problem for feed-forward neural networks, A.B. Bulsari connectionism vs symbolism - an overview, A.B. Bujosa et al introduction to connectionist computer vision systems, D.W. Moolman et al. Part 2 Applications: lime kiln simulation and control by neural networks, B. Ribeiro and A. Dourado Correia concentration estimation using neural networks and partial conventional models, B. Schenker and M. Agarwal data rectification for dynamic processes using artificial neural networks, T.W. Karjala and D.M. Himmelblau applications of neural networks in process dynamics, A.B. Bulsari process modelling for fault detection using neural networks, T. Fujiwara local modelling as a tool for semi-empirical or semi-mechanistic process modelling, B.A. Foss and T.A. Johansen estimation of measurement error variances and process data reconciliation, C. Aldrich and J.S.J. van Deventer process monitoring and visualization using self-organizing maps, O. Simula and J. Kangas an overview of dynamic system control using neural networks, P. Zufiria nonlinear system identification using neural networks - dynamics and instabilities, R. Rico-Martinez et al pattern-based interpretation of on-line process data, J.F. Davis and C.-M. Wang modelling ill-defined behaviour of reacting systems using neural networks, C. Aldrich and J.S.J. van Deventer global vs local networks in identification and control -a case study of neutralization, M.N. Karim and B. Eikens modelling chemical processes using multiresolution representation neural networks, K. Yoda and T. Furuya the videographic characterisation of flotation froths using neural networks, D.W. Moolman et al fuzzy modelling using two connectionist architectures, J. Zhang and A.J. Morris system identification using elman and Jordan networks, D.T. Pham et al time-series prediction with on-line correction of kalman gain - a connectionist approach, A. Dobnikar et al neural networks based control strategies for a continuous polymerisation reactor, N. Watanabe statistical and neural methods in classification and modelling, E.B. Martin et al clustering and statistical techniques in neural networks, V. Venkatasubramanian and R. Rengaswamy.

Modeling and control of carbon monoxide concentration using a neuro-fuzzy technique

Abstract: Modeling and control of carbon monoxide (CO) concentration using a neuro-fuzzy technique are discussed. A self-organizing fuzzy identification algorithm (SOFIA) for identifying complex systems such as CO concentration is proposed. The main purpose of SOFIA is to reduce the computational requirement for identifying a fuzzy model. In particular, the authors simplify a procedure for finding the optimal structure of fuzzy partition. The /spl delta/ rule, which is a basic learning method in neural networks, is used for parameter identification of a fuzzy model. SOFIA consists of four stages which effectively realize structure identification and parameter identification. The procedure of SOFIA is concretely demonstrated by a simple example which has been used in some modeling exercises. The identification result shows effectiveness of SOFIA. Next, the authors apply SOFIA to a prediction problem for CO concentration in the air at the busiest traffic intersection in a large city of Japan. Prediction results show that the fuzzy model is much better than a linear model. Furthermore, the authors simulate a control system for keeping CO concentration at a constant level by using the identified fuzzy model. A self-learning method for adaptively modifying controller parameters by /spl delta/ rule is introduced because the dynamics of real CO concentration system changes gradually over a long period of time. Two self-learning controllers are designed in this simulation. One is a self-learning linear PI controller. The other is a self-learning fuzzy PI controller. The authors investigate robustness and adaptability of this control system for disturbance and parameter perturbation of the CO concentration model. Simulation results show that the self-learning fuzzy controller is more robust and adaptive. >

Fuzzy Logic for Business and Industry

Abstract: We may not be able to make you love reading, but fuzzy logic for business and industry will lead you to love reading starting from now. Book is the window to open the new world. The world that you want is in the better stage and level. World will always guide you to even the prestige stage of the life. You know, this is some of how reading will give you the kindness. In this case, more books you read more knowledge you know, but it can mean also the bore is full.