Foresight can be crucial in process and production control,
production-and-resources planning and in management decision making
generally. Although forecasting the future from accumulated
historical data has become a standard and reliable method in
production and financial engineering, as well as in business and
management, the use of time series analysis in the on-line milieu
of most industrial plants has been more problematic because of the
time and computational effort required.
The advent of intelligent computational technologies such as the
neural network and the genetic algorithm promotes the efficient
solution of on-line forecasting problems. Their most outstanding
successes include:
- prediction of nonlinear time series and the nonlinear
combination of forecasts using neural networks;
- prediction of chaotic time series and of output data for
second-order nonlinear plant using fuzzy logic.
The power of intelligent technologies applied individually and
in combination, has created advanced forecasting methodologies,
exemplified in Computational Intellingence in Time Series
Forecasting by particular systems and processes. The authors give 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 using such examples
as:
- forecasting of electrical load and of output data for nonlinear
plant with neuro-fuzzy networks;
- temperature prediction and correction in pyrometer reading,
tool-wear monitoring and materials property prediction using hybrid
intelligent technologies;
- evolutionary training of neuro-fuzzy networks by the use of
genetic algorithms and prediction of chaotic time series;
- isolated use of neural networks and fuzzy logic in the
nonlinear combination of traditional forecasts of temperature
series obtained from a pilot-scale chemical reactor with
temporarily disconnected controller.
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.
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