Swarm Intelligence and the Taguchi Method for Identification of Fuzzy Models

Swarm Intelligence and the Taguchi Method for Identification of Fuzzy Models

Arun Khosla (National Institute of Technology, Jalandhar, India), Shakti Kumar (Haryana Engineering College, Jalandhar, India) and K. K. Aggarwal (GGS Indraprastha University, Delhi, India)
Copyright: © 2006 |Pages: 23
DOI: 10.4018/978-1-59140-827-7.ch010
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Abstract

Nature is a wonderful source of inspiration for building models and techniques for solving difficult problems in design, optimisation, and control. More specifically, the study of evolution, the human immune system, and the collective behaviour of insects/birds have guided the origin of evolutionary algorithms, artificial immune systems, and optimisation techniques based on swarm intelligence, respectively. In this chapter, we present the use of particle swarm optimisation (PSO) and the Taguchi method for the identification of optimised fuzzy models from the available data. PSO is a member of the broad category of swarm intelligence (SI) techniques based on the metaphor of social interaction. It has been used for finding promising solutions in complex search spaces through the interaction of particles in a swarm, and is especially useful when dealingwith a high number of dimensions and situations where problem-specific information is not available. However, caution needs to be exercised in selecting PSO, as the performance of PSO largely depends on their values. In this chapter, a systematic reasoning approach based on the Taguchi method is also presented to quickly identify PSO parameters. The Taguchi method is a robust design approach that helps in optimisation, and which requires relatively few experiments. Although we focus here on the use of PSO and the Taguchi method for fuzzy model identification, these techniques have much broader use and application. In order to validate our approach, data from the rapid Nickel-Cadmium (Ni-Cd) battery charger developed by the authors were used. The results are based on real data and illustrate the viability and efficiency of the approach.

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Dedication
Table of Contents
Preface
Colin Fyfe
Chapter 1
Cong Tran, Ajith Abraham, Lakhmi Jain
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Chapter 2
Ah Chung Tsoi, Phuong Kim To, Markus Hagenbuchner
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Application of Text Mining Methodologies to Health Insurance Schedules
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Chapter 3
Quan Bai, Minjie Zhang
An intelligent agent is a reactive, proactive, autonomous, and social entity. The social ability of an agent is exercised in a multi-agent system... Sample PDF
Coordinating Agent Interactions Under Open Environments
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Chapter 4
Russell Gluck, John Fulcher
The chapter commences with an overview of automatic speech recognition (ASR), which covers not only the de facto standard approach of hidden Markov... Sample PDF
Literacy by Way of Automatic Speech Recognition
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Chapter 5
Lars Petersson, Luke Fletcher, Nick Barnes, Alexander Zelinsky
This chapter gives an overview of driver assistance systems (DAS) in general and the Smart Cars project in particular. In the Driver Assistance... Sample PDF
Smart Cars: The Next Frontier
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Chapter 6
Amanda J.C. Sharkey, Noel Sharkey
This chapter considers the application of swarm intelligence principles to collective robotics. Our aim is to identify the reasons for the growing... Sample PDF
The Application of Swarm Intelligence to Collective Robots
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Chapter 7
Mikhail Prokopenko, Geoff Poulton, Don Price
An approach to the structural health management (SHM) of future aerospace vehicles is presented. Such systems will need to operate robustly and... Sample PDF
Self-Organising Impact Sensing Networks in Robust Aerospace Vehicles
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Chapter 8
Russell Beale, Andy Pryke
This chapter argues that a knowledge discovery system should be interactive, should utilise the best in artificial intelligence (AI), evolutionary... Sample PDF
Knowledge Through Evolution
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Chapter 9
Brijesh Verma, Rinku Panchal
This chapter presents neural network-based techniques for the classification of micro-calcification patterns in digital mammograms. Artificial... Sample PDF
Neural Networks for the Classification of Benign and Malignant Patters in Digital Mammograms
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Chapter 10
Arun Khosla, Shakti Kumar, K. K. Aggarwal
Nature is a wonderful source of inspiration for building models and techniques for solving difficult problems in design, optimisation, and control.... Sample PDF
Swarm Intelligence and the Taguchi Method for Identification of Fuzzy Models
$37.50
About the Authors