Computational Intelligence in Time Series Forecasting

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.

Teaches the practising reader to apply previously impractical statistical methods to the prediction of a wide range of variables in industrial plant increasing efficiency and saving money The academic reader will learn of the latest research in the design of intelligent computing systems and their application to control mechanisms opening up new areas of predictive control and automation Includes supplementary material: sn.pub/extras

Inhalt
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.