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Evolving intelligent systems : methodology and applications.

Research output: Book/Report/ProceedingsBook

Published

Publication date04/2010
Place of publicationNew York
PublisherWiley-Blackwell
Number of pages444
ISBN (Print)978-0470287194
Original languageEnglish

Publication series

NameIEEE Press Series on Computational Intelligence
PublisherWiley-Blackwell

Abstract

The newly established concept of evolving intelligent systems is a result of the synergy between conventional systems, neural networks and fuzzy systems as structures for information representation and the real time methods for machine learning. It targets non-stationary processes by developing novel on-line learning methods and computationally efficient algorithms for real-time applications. Fuzzy systems have demonstrated the ability to formalize in a computationally efficient manner the approximate reasoning typical of humans. Neural Networks present a convenient framework for synthesis and analysis of complex non-linear systems. Neuro-fuzzy systems combine the advantages of both areas and often use established machine learning methods for design. One of the important research challenges today is to further develop the intelligent systems theory towards the design of truly intelligent systems with a higher level of flexibility and autonomy that can develop their understanding of the environment and ultimately their intelligence. To address the problems of modelling, control, prediction, classification and data processing in such environments a system must be able to fully adapt its structure rather than adjust its parameters based on a pre-trained and fixed structure. That is, the system must be able to evolve, to self-develop, to self-organize. Evolving intelligent systems are inspired by the idea of system model evolution. They focus on the evolution of an open family of system models representing the system in different situations and operating conditions. In this sense they differ from the traditional evolutionary algorithms. They use inheritance and gradual change with the aim of life-long learning and adaptation, self-organization (including system structure evolution) in order to adapt to the (unknown and unpredictable) environment. Embedded soft computing diagnostics and prognostics algorithms, intelligent agents and controllers are the natural implementation area of evolving systems as a realistic and practical tool for design of real time intelligent systems.

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