Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications / Edition 1

Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications / Edition 1

ISBN-10:
1852339489
ISBN-13:
9781852339487
Pub. Date:
10/18/2005
Publisher:
Springer London
ISBN-10:
1852339489
ISBN-13:
9781852339487
Pub. Date:
10/18/2005
Publisher:
Springer London
Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications / Edition 1

Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications / Edition 1

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Overview

Foresight in an engineering enterprise can make the difference between success and failure, and can be vital to the effective control of industrial systems. Applying time series analysis in the on-line milieu of most industrial plants has been problematic owing to the time and computational effort required. The advent of soft computing tools offers a solution.

The authors harness the power of intelligent technologies individually and in combination. Examples of the particular systems and processes susceptible to each technique are investigated, cultivating a comprehensive exposition of the improvements on offer in quality, model building and predictive control and the selection of appropriate tools from the plethora available.

Application-oriented engineers in process control, manufacturing, production industry and research centres will find much to interest them in this book. It is suitable for industrial training purposes, as well as serving as valuable reference material for experimental researchers.


Product Details

ISBN-13: 9781852339487
Publisher: Springer London
Publication date: 10/18/2005
Series: Advances in Industrial Control
Edition description: 2005
Pages: 372
Product dimensions: 6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

Computational Intelligence: An Introduction.- Traditional Problem Definition.- Basic Intelligent Computational Technologies.- Neural Networks Approach.- Fuzzy Logic Approach.- Evolutionary Computation.- Hybrid Computational Technologies.- Neuro-fuzzy Approach.- Transparent Fuzzy/Neuro-fuzzy Modelling.- Evolving Neural and Fuzzy Systems.- Adaptive Genetic Algorithms.- Recent Developments.- State of the Art and Development Trends.
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