Abstract
Intelligent adaptive information systems are systems which can automatically adapt their structure and behaviour in order to react better to a dynamically changing environment, and to provide knowledge which explains it. Several hybrid fuzzyneuro techniques have already proved to be very useful for this purpose, one of them being the fuzzy neural networks. Fuzzy neural networks have important and useful features, such as: adaptive learning, good generalisation, good explanation facilities in form of fuzzy rules, abilities to accommodate both data and existing knowledge about the problem, ability to act autonomously in a dynamically changing environment. In order to design and train a fuzzy neural network for a particular task in a dynamically changing environment, one need to carefully investigate the type of the dynamics, and the level of chaos in the analysed process. This chapter introduces a way of using both chaos theory and a particular fuzzy neural network, called FuNN, for building adaptive, intelligent multimodular systems. A properly designed and trained FuNN can structurally capture major characteristics of a complex process under control. The use of this methodology for building intelligent adaptive systems is illustrated through examples from control and prediction.
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Kasabov, N.K., Kozma, R. (1997). Neurofuzzy-Chaos Engineering for Building Intelligent Adaptive Information Systems. In: Ruan, D. (eds) Intelligent Hybrid Systems. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6191-0_9
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DOI: https://doi.org/10.1007/978-1-4615-6191-0_9
Publisher Name: Springer, Boston, MA
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