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Computational Intelligence in Time Series Forecasting

Name: Computational Intelligence in Time Series Forecasting
Brand: Springer London Ltd
SKU: 9781849969703
Price: 3426 CZK
Availability: InStock

Theory and Engineering Applications

Autor: Ajoy K. Palit, Dobrivoje Popovic

Jazyk:

Vazba: Brožovaná

Vydavatel: Springer London Ltd

Dostupnost: Skladem u dodavatele

Odesíláme za 5-8 dnů

3 426 Kč

Foresight can be crucial in process and production control, production-and-resources planning and in...

Informace o knize

Autor

Ajoy K. Palit, Dobrivoje Popovic

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2010

Stránek

372

EAN

9781849969703

ISBN

1849969701

Enbook ID

01434848

Vydavatel

Springer London Ltd

Hmotnost

605

Rozměry

155 x 21 x 22

Kompletní popis

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.Foresight in an engineering business can make the difference between success and failure, and can be vital to the effective control of industrial systems. The authors of this book harness the power of intelligent technologies individually and in combination.Foresight in an engineering enterprise can make the difference between success and failure and can be vital to the effective control of industrial systems. Forecasting the future from accumulated historical data is a tried and tested method in areas such as engineering finance. Applying 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 soft computing tools such as the neural network and the genetic algorithm offers a solution.§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. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.