Soft Computing in Engineering Design and Manufacturing

1. Front Matter

   Pages I-XIX

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2. Evolutionary Computing

   1. Front Matter

      Pages 1-1

      PDF

   2. Artificial Neural Networks Optimization by means of Evolutionary Algorithms

      • I. De Falco, A. Della Cioppa, P. Natale, E. Tarantino

      Pages 3-12

   3. Fitness Causes Bloat

      • W. B. Langdon, R. Poli

      Pages 13-22

   4. Triune-Brain Inspired Unifying View of Intelligent Computation

      • Raju Surampudi Bapi

      Pages 23-29

   5. An Overview of Evolutionary Computing for Multimodal Function Optimisation

      • Rajkumar Roy, Ian C. Parmee

      Pages 30-39

   6. Evolution of Cellular-automaton-based Associative Memories

      • Marcin Chady, Riccardo Poli

      Pages 40-47

   7. Simple Implementation of Genetic Programming by Column Tables

      • Vladimír Kvasnièka, Jiøí Pospíchal

      Pages 48-56

3. Neural Networks

   1. Front Matter

      Pages 57-57

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   2. XCS Classifier System Reliably Evolves Accurate, Complete, and Minimal Representations for Boolean Functions

      • Tim Kovacs

      Pages 59-68

   3. Designing Neural Networks using a Genetic Rule-based System

      • Kwok Ching Tsui, Mark Plumbley

      Pages 69-78

   4. A Neural Network for Real-World Postal Address Recognition

      • Michael Blumenstein, Brijesh Verma

      Pages 79-83

   5. Neural Processing in Semi-Rigid Connections of Steel Structures

      • G. E. Stavroulakis, A. V. Avdelas, P. D. Panagiotopoulos, K. M. Abdalla

      Pages 84-92

   6. Evolutionary Design of Neural Trees for Heart Rate Prediction

      • Byoung-Tak Zhang, Je-Gun Joung

      Pages 93-101

4. Fuzzy Logic

   1. Front Matter

      Pages 103-103

      PDF

   2. Hypertrapezoidal Fuzzy Membership Functions for Decision Aiding

      • Wallace E. Kelly, John H. Painter

      Pages 105-113

   3. An Iterative Two-Phase Approach to Fuzzy System Design

      • F. Abbattista, G. Castellano, A. M. Fanelli

      Pages 114-121

   4. A Fuzzy Control Course on the Internet

      • Jan Jantzen, Mariagrazia Dotoli

      Pages 122-130

   5. FUZOS — Fuzzy Operating System Support For Information Technology

      • A. B. Patki, G. V. Raghunathan, Azar Khurshid

      Pages 131-140

   6. Industrial Application of Chaos Engineering

      • Tadashi Iokibe

      Pages 141-150

Soft Computing has emerged as an important approach towards achieving intelligent computational paradigms where key elements are learning from experience in the presence of uncertainties, fuzzy belief functioos, and ·evolutioo of the computing strategies of the learning agent itself. Fuzzy, neural and evolutionary computing are the three major themes of soft computing. The book presents original research papers dealing with the theory of soft computing and its applicatioos in engineering design and manufacturing. The methodologies have been applied to a large variety of real life problems. Applicatioo of soft computing has provided the opportunity to integrate human like 'vagueness' and real life 'uncertainty' to an otherwise 'hard' computer programme. Now, a computer programme can learn, adapt, and evolve using soft computing. The book identifies the strengths and Iimitatioos of soft cOOlputing techniques, particularly with reference to their engineering applications. The applications range fran design optimisatioo to scheduling and image analysis. Goal optimisatioo with incomplete infmnatioo and under uncertainty is the key to solving real-life problems in design and manufacturing. Soft computing techniques presented in this book address these issues. Computatiooal complexity and efficient implementatioo of these techniques are also major concerns for realising useful industrial applications of soft computing. The different parts in the book also address these issues. The book cootains 9 parts, 8 of which are based 00 papers fran the '2nd On-line World Conference 00 Soft Computing in Engineering Design and Manufacture (WSC2),.

• Scheduling
• artificial neural network
• cognition
• evolution
• evolutionary algorithm
• genetic programming
• information
• mutation
• neural network
• operating system
• optimization
• pattern recognition
• robot
• robotics
• simulation
• complexity

• School of Mechanical Engineering, University of Bath, Bath, UK

  P. K. Chawdhry

• The CIM Institute, Cranfield University, Cranfield, UK

  R. Roy

• Department of Aerospace Engineering, Indian Institute of Technology, Powai, India

  R. K. Pant

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