Computational Intelligence in Time Series Forecasting [electronic resource] : Theory and Engineering Applications / by Ajoy K. Palit, Dobrivoje Popovic.

By: Palit, Ajoy K [author.]Contributor(s): Popovic, Dobrivoje [author.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Advances in Industrial Control: Publisher: London : Springer London, 2005Description: XXII, 372 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781846281846Subject(s): Engineering | Artificial intelligence | Computer simulation | Statistics | System safety | Engineering | Control Engineering | Artificial Intelligence (incl. Robotics) | Simulation and Modeling | Automation and Robotics | Quality Control, Reliability, Safety and Risk | Statistics for Engineering, Physics, Computer Science, Chemistry & GeosciencesAdditional physical formats: Printed edition:: No titleOnline resources: Click here to access online
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.
In: Springer eBooksSummary: 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.
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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.

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.

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