Fuzzy set theory Books
MIT Press Category Theory for the Sciences The MIT Press
Book SynopsisAn introduction to category theory as a rigorous, flexible, and coherent modeling language that can be used across the sciences.Category theory was invented in the 1940s to unify and synthesize different areas in mathematics, and it has proven remarkably successful in enabling powerful communication between disparate fields and subfields within mathematics. This book shows that category theory can be useful outside of mathematics as a rigorous, flexible, and coherent modeling language throughout the sciences. Information is inherently dynamic; the same ideas can be organized and reorganized in countless ways, and the ability to translate between such organizational structures is becoming increasingly important in the sciences. Category theory offers a unifying framework for information modeling that can facilitate the translation of knowledge between disciplines.Written in an engaging and straightforward style, and assuming little background in mathematics, the book is
£49.40
The University of Chicago Press FuzzySet Social Science
Book SynopsisIn this innovative approach to the practice of social science, Charles Ragin explores the use of fuzzy sets to bridge the divide between quantitive and qualitative methods. He argues that fuzzy sets allow a far richer dialogue between ideas and evidence in social research than previously possible.
£28.50
John Wiley & Sons Inc Fuzzy Logic
Book SynopsisOffering a new perspective on a growing field, the text explores the many hardware implications of fuzzy logic based circuits. As use of AI increases, so the VLSI area of circuits is becoming a growth subject. The text surveys fuzzy set theory before moving on to cover a range of nonstandard solutions for fuzzy logic VLSI circuits. An overview of future trends is included plus practical examples from the authors'' research which will enhance the reader''s understanding of the topic. This is the first book on hardware aspects of fuzzy systems which offers a mixture of classical work and the authors'' new perspective.Table of ContentsFuzzy Sets in Approximate Reasoning: A Personal View. FUZZY LOGIC CONTROL. Fuzzy Logic Control: A systematic Design and Performance Assessment Methodology. On the Compatibility of Fuzzy Control and Conventional Control Techniques. On the Crisp-Type Fuzzy Controller: Behaviour Analysis and Improvement. FUZZY LOGIC HARDWARE IMPLEMENTATIONS. Design Considerations of Digital Fuzzy Logic Controllers. Parallel Algorithm for Fuzzy Logic Controller. Fuzzy Flip-Flop. Design Automation of Fuzzy Logic Circuits. HYBRID SYSTEMS AND APPLICATIONS. Neuro-Fuzzy Systems: Hybrid Configurations. A Fuzzy Logic Approach to Handwriting Recognition. Index.
£245.66
John Wiley & Sons Inc Fuzzy Expert System Tools D3
Book SynopsisFuzzy set theory is a mathematical structure for representing uncertainty. Modern intelligent systems must combine knowledge based on techniques for gathering and processing information with methods of approximate reasoning. This enables an intelligent system to better emulate human decision-making in uncertain environments.Table of ContentsGetting Started. Fuzzy Set Theory. Possibility/Probability Consistency Principle. Knowledge Representation. Imprecision and Fuzzy Logic. Knowledge Processing. Knowledge in FEST. Inference Engine. The Fuzzy Inference Engine. Fuzzy Inference in FEST. References. Index.
£199.76
John Wiley & Sons Inc Comparative Statistical Inference
Book SynopsisStatistical inference is the process of drawing conclusions based upon the available data on the measurement of uncertainty of a defined event. It allows one to draw a conclusion or a generalization from the available data. , i.e. if there is smoke there is a good probability there is a fire.Table of ContentsIntroduction: Statistical Inference and Decision-making. An Illustration of the Different Approaches. Probability. Utility and Decision-making. Classical Inference. Bayesian Inference. Decision Theory. Other Approaches. Perspective. References. Index.
£191.66
John Wiley & Sons Inc Modeling and Control of Uncertain Nonlinear
Book SynopsisAn original, systematic-solution approach to uncertain nonlinear systems control and modeling using fuzzy equations and fuzzy differential equations There are various numerical and analytical approaches to the modeling and control of uncertain nonlinear systems. Fuzzy logic theory is an increasingly popular method used to solve inconvenience problems in nonlinear modeling.Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations andZ-Numberpresents a structured approach to the control and modeling of uncertain nonlinear systems in industry using fuzzy equations and fuzzy differential equations. The first major work to explore methods based on neural networks and Bernstein neural networks, this innovative volume provides a framework for control and modeling of uncertain nonlinear systems with applications to industry. Readers learn how to use fuzzy techniques to solve scientific and engineering problems and understand intelligent contTable of ContentsList of Figures xi List of Tables xiii Preface xv 1 Fuzzy Equations 1 1.1 Introduction 1 1.2 Fuzzy Equations 1 1.3 Algebraic Fuzzy Equations 3 1.4 Numerical Methods for Solving Fuzzy Equations 5 1.4.1 Newton Method 5 1.4.2 Steepest Descent Method 7 1.4.3 Adomian Decomposition Method 8 1.4.4 Ranking Method 9 1.4.5 Intelligent Methods 10 1.4.5.1 Genetic Algorithm Method 10 1.4.5.2 Neural Network Method 11 1.4.5.3 Fuzzy Linear Regression Model 14 1.5 Summary 20 2 Fuzzy Differential Equations 21 2.1 Introduction 21 2.2 Predictor–Corrector Method 21 2.3 Adomian Decomposition Method 23 2.4 Euler Method 23 2.5 Taylor Method 25 2.6 Runge–Kutta Method 25 2.7 Finite Difference Method 26 2.8 Differential Transform Method 28 2.9 Neural Network Method 29 2.10 Summary 36 3 Modeling and Control Using Fuzzy Equations 39 3.1 Fuzzy Modeling with Fuzzy Equations 39 3.1.1 Fuzzy Parameter Estimation with Neural Networks 45 3.1.2 Upper Bounds of the Modeling Errors 48 3.2 Control with Fuzzy Equations 52 3.3 Simulations 59 3.4 Summary 67 4 Modeling and Control Using Fuzzy Differential Equations 69 4.1 Introduction 69 4.2 Fuzzy Modeling with Fuzzy Differential Equations 69 4.3 Existence of a Solution 72 4.4 Solution Approximation using Bernstein Neural Networks 79 4.5 Solutions Approximation using the Fuzzy Sumudu Transform 83 4.6 Simulations 85 4.7 Summary 99 5 System Modeling with Partial Differential Equations 101 5.1 Introduction 101 5.2 Solutions using Burgers–Fisher Equations 101 5.3 Solution using Wave Equations 106 5.4 Simulations 109 5.5 Summary 117 6 System Control using Z-numbers 119 6.1 Introduction 119 6.2 Modeling using Dual Fuzzy Equations and Z-numbers 119 6.3 Controllability using Dual Fuzzy Equations 124 6.4 Fuzzy Controller 128 6.5 Nonlinear System Modeling 131 6.6 Controllability using Fuzzy Differential Equations 131 6.7 Fuzzy Controller Design using Fuzzy Differential Equations and Z-number 135 6.8 Approximation using a Fuzzy Sumudu Transform and Z-numbers 138 6.9 Simulations 139 6.10 Summary 151 References 153 Index 167
£72.15
Nova Science Publishers Inc Fuzzy Control Systems
Book Synopsis
£146.24
