Chapter by chapter, Computational Intelligence in Time Series Forecasting harnesses 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; these include:
. forecasting electrical load, chemical reactor behaviour and high-speed-network congestion using fuzzy logic;
. prediction of airline passenger patterns and of output data for nonlinear plant with combination neuro-fuzzy networks;
. evolutionary modelling and anticipation of stock performance by the use of genetic algorithms.
Application-oriented engineers in process control, manufacturing, the production industries and research centres will find much to interest them in Computational Intelligence in Time Series Forecasting and the book is suitable for industrial training purposes. It will also serve as valuable reference material for experimental researchers.
Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. Theseries offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
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