Soft Computing : Neuro-Fuzzy and Genetic Algorithms

Soft Computing Neuro-Fuzzy and Genetic Algorithms

Soft computing is a branch of computer science that deals with a family of methods that imitate human intelligence. This is done with the goal of creating tools that will contain some human-like capabilities (such as learning, reasoning and decision-making). This book covers the entire gamut of soft...

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Detalles Bibliográficos
Autor principal: Roy, Samir (-)
Otros Autores: Chakraborty, Udit
Formato: Libro electrónico
Idioma:Inglés
Publicado: Noida : Pearson India 2013.
Edición:1st ed
Materias:
Soft computing > Problems, exercises, etc.
Fuzzy logic > Problems, exercises, etc.
Genetic algorithms > Problems, exercises, etc.
Genetic programming (Computer science) > Problems, exercises, etc.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009820527306719
Tabla de Contenidos:
  • Cover
  • Contents
  • Preface
  • Acknowledgements
  • About the Authors
  • Chapter 1: Introduction
  • 1.1 What is Soft Computing?
  • 1.2 Fuzzy Systems
  • 1.3 Rough Sets
  • 1.4 Artificial Neural Networks
  • 1.5 Evolutionary Search Strategies
  • Chapter Summary
  • Test Your Knowledge
  • Answers
  • Exercises
  • Bibliography and Historical Notes
  • Chapter 2: Fuzzy Sets
  • 2.1 Crisp Sets: A Review
  • 2.1.1 Basic Concepts
  • 2.1.2 Operations on Sets
  • 2.1.3 Properties of Sets
  • 2.2 Fuzzy Sets
  • 2.2.1 Fuzziness/Vagueness/Inexactness
  • 2.2.2 Set Membership
  • 2.2.3 Fuzzy Sets
  • 2.2.4 Fuzzyness vs. Probability
  • 2.2.5 Features of Fuzzy Sets
  • 2.3 Fuzzy Membership Functions
  • 2.3.1 Some Popular Fuzzy Membership Functions
  • 2.3.2 Transformations
  • 2.3.3 Linguistic Variables
  • 2.4 Operations on Fuzzy Sets
  • 2.5 Fuzzy Relations
  • 2.5.1 Crisp Relations
  • 2.5.2 Fuzzy Relations
  • 2.5.3 Operations on Fuzzy Relations
  • 2.6 Fuzzy Extension Principle
  • 2.6.1 Preliminaries
  • 2.6.2 The Extension Principle
  • Chapter Summary
  • Solved Problems
  • Test Your Knowledge
  • Answers
  • Exercises
  • Bibliography and Historical Notes
  • Chapter 3: Fuzzy Logic
  • 3.1 Crisp Logic: A Review
  • 3.1.1 Propositional Logic
  • 3.1.2 Predicate Logic
  • 3.1.3 Rules of Inference
  • 3.2 Fuzzy Logic Basics
  • 3.2.1 Fuzzy Truth Values
  • 3.3 Fuzzy Truth in Terms of Fuzzy Sets
  • 3.4 Fuzzy Rules
  • 3.4.1 Fuzzy If-Then
  • 3.4.2 Fuzzy If-Then-Else
  • 3.5 Fuzzy Reasoning
  • 3.5.1 Fuzzy Quantifiers
  • 3.5.2 Generalized Modus Ponens
  • 3.5.3 Generalized Modus Tollens
  • Chapter Summary
  • Solved Problems
  • Test Your Knowledge
  • Answers
  • Exercises
  • Bibliography and Historical Notes
  • Chapter 4: Fuzzy Inference Systems
  • Introduction
  • 4.2 Fuzzification of the Input Variables
  • 4.3 Application of Fuzzy Operators on the Antecedent Parts of the Rules.
  • 4.4 Evaluation of the Fuzzy Rules
  • 4.5 Aggregation of Output Fuzzy Sets Across the Rules
  • 4.6 Defuzzification of the Resultant Aggregate Fuzzy Set
  • 4.6.1 Centroid Method
  • 4.6.2 Centre-of-Sums (CoS) Method
  • 4.6.3 Mean-of-Maxima (MoM) Method
  • 4.7 Fuzzy Controllers
  • 4.7.1 Fuzzy Air Conditioner Controller
  • 4.7.2 Fuzzy Cruise Controller
  • Chapter Summary
  • Solved Problems
  • Test Your Knowledge
  • Answers
  • Exercises
  • Bibliography and Historical Notes
  • Chapter 5: Rough Sets
  • 5.1 Information Systems and Decision Systems
  • 5.2 Indiscernibility
  • 5.3 Set Approximations
  • 5.4 Properties of Rough Sets
  • 5.5 Rough Membership
  • 5.6 Reducts
  • Application
  • Chapter Summary
  • Solved Problems
  • Test Your Knowledge
  • Answers
  • Exercises
  • Bibliography and Historical Notes
  • Chapter 6: Artificial Neural Networks:Basic Concepts
  • 6.1 Introduction
  • 6.1.1 The Biological Neuron
  • 6.1.2 The Artificial Neuron
  • 6.1.3 Characteristics of the Brain
  • 6.2 Computation in Terms of Patterns
  • 6.2.1 Pattern Classification
  • 6.2.2 Pattern Association
  • 6.3 The McCulloch-Pitts Neural Model
  • 6.4 The Perceptron
  • 6.4.1 The Structure
  • 6.4.2 Linear Separability
  • 6.4.3 The XOR Problem
  • 6.5 Neural Network Architectures
  • 6.5.1 Single Layer Feed Forward ANNs
  • 6.5.2 Multilayer Feed Forward ANNs
  • 6.5.3 Competitive Network
  • 6.5.4 Recurrent Networks
  • 6.6 Activation Functions
  • 6.6.1 Identity Function
  • 6.6.2 Step Function
  • 6.6.3 The Sigmoid Function
  • 6.6.4 Hyperbolic Tangent Function
  • 6.7 Learning by Neural Nets
  • 6.7.1 Supervised Learning
  • 6.7.2 Unsupervised Learning
  • Chapter Summary
  • Solved Problems
  • Test Your Knowledge
  • Answers
  • Exercises
  • Bibliography and Historical Notes
  • Chapter 7: Pattern Classifiers
  • 7.1 Hebb Nets
  • 7.2 Perceptrons
  • 7.3 Adaline
  • 7.4 Madaline
  • Chapter Summary.
  • Solved Problems
  • Test Your Knowledge
  • Answers
  • Exercises
  • Bibliography and Historical Notes
  • Chapter 8: Pattern Associators
  • 8.1 Auto-associative Nets
  • 8.1.1 Training
  • 8.1.2 Application
  • 8.1.3 Elimination of Self-connection
  • 8.1.4 Recognition of Noisy Patterns
  • 8.1.5 Storage of Multiple Patterns in an Auto-associative Net
  • 8.2 Hetero-associative Nets
  • 8.2.1 Training
  • 8.2.2 Application
  • 8.3 Hopfield Networks
  • 8.3.1 Architecture
  • 8.3.2 Training
  • 8.4 Bidirectional Associative Memory
  • 8.4.1 Architecture
  • 8.4.2 Training
  • 8.4.3 Application
  • Chapter Summary
  • Solved Problems
  • Test Your Knowledge
  • Answers
  • Exercises
  • Bibliography and Historical Notes
  • Chapter 9: Competitive Neural Nets
  • 9.1 The Maxnet
  • 9.1.1 Training a MAXNET
  • 9.1.2 Application of Maxnet
  • 9.2 Kohonen's Self-organizing Map (SOM)
  • 9.2.1 SOM Architecture
  • 9.2.2 Learning by Kohonen's SOM
  • 9.2.3 Application
  • 9.3 Learning Vector Quantization (LVQ)
  • 9.3.1 LVQ Learning
  • 9.3.2 Application
  • 9.4 Adaptive Resonance Theory (ART)
  • 9.4.1 The Stability-Plasticity Dilemma
  • 9.4.2 Features of ART Nets
  • 9.4.3 Art 1
  • Chapter Summary
  • Solved Problems
  • Test Your Knowledge
  • Answers
  • Exercises
  • Bibliography and Historical Notes
  • Chapter 10: Backpropagation
  • 10.1 Multi-layer Feedforward Net
  • 10.1.1 Architecture
  • 10.1.2 Notational Convention
  • 10.1.3 Activation Functions
  • 10.2 The Generalized Delta Rule
  • 10.3 The Backpropagation Algorithm
  • 10.3.1 Choice of Parameters
  • 10.3.2 Application
  • Chapter Summary
  • Solved Problems
  • Test Your Knowledge
  • Answers
  • Exercises
  • Bibliography and Historical Notes
  • Chapter 11: Elementary Search Techniques
  • 11.1 State Spaces
  • 11.2 State Space Search
  • 11.2.1 Basic Graph Search Algorithm
  • 11.2.2 Informed and Uninformed Search
  • 11.3 Exhaustive Search.
  • 11.3.1 Breadth-first Search (BFS)
  • 11.3.2 Depth-first Search (DFS)
  • 11.3.3 Comparison Between BFS and DFS
  • 11.3.4 Depth-first Iterative Deepening
  • 11.3.5 Bidirectional Search
  • 11.3.6 Comparison of Basic Uninformed Search Strategies
  • 11.4 Heuristic Search
  • 11.4.1 Best-first Search
  • 11.4.2 Generalized State Space Search
  • 11.4.3 Hill Climbing
  • 11.4.4 The A/A* Algorithms
  • 11.4.5 Problem Reduction
  • 11.4.6 Means-ends Analysis
  • 11.4.7 Mini-Max Search
  • 11.4.8 Constraint Satisfaction
  • 11.4.9 Measures of Search
  • 11.5 Production Systems
  • Chapter Summary
  • Solved Problems
  • Test Your Knowledge
  • Answers
  • Exercises
  • Bibliography and Historical Notes
  • Chapter 12: Advanced Search Strategies
  • 12.1 Natural Evolution: A Brief Review
  • 12.1.1 Chromosomes
  • 12.1.2 Natural Selection
  • 12.1.3 Crossover
  • 12.1.4 Mutation
  • 12.2 Genetic Algorithms (GAs)
  • 12.2.1 Chromosomes
  • 12.2.2 Fitness Function
  • 12.2.3 Population
  • 12.2.4 GA Operators
  • 12.2.5 Elitism
  • 12.2.6 GA Parameters
  • 12.2.7 Convergence
  • 12.3 Multi-objective Genetic Algorithms
  • 12.3.1 MOO Problem Formulation
  • 12.3.2 The Pareto-optimal Front
  • 12.3.3 Pareto-optimal Ranking
  • 12.3.4 Multi-objective Fitness
  • 12.3.5 Multi-objective GA Process
  • 12.4 Simulated Annealing
  • Chapter Summary
  • Solved Problems
  • Test Your Knowledge
  • Answers
  • Exercise
  • Bibliography and Historical Notes
  • Chapter 13: Hybrid Systems
  • 13.1 Neuro-genetic Systems
  • 13.1.1 GA-based Weight Determination of Multi-layerFeed-forward Net
  • 13.1.2 Neuro-evolution of Augmenting Topologies (NEAT)
  • 13.2 Fuzzy-Neural Systems
  • 13.2.1 Fuzzy Neurons
  • 13.2.2 Adaptive Neuro-fuzzy Inference System (ANFIS)
  • 13.3 Fuzzy-genetic Systems
  • Chapter Summary
  • Test Your Knowledge
  • Answers
  • Bibliography and Historical Notes
  • Index.

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