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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Adaptation of Fuzzy Inference System Using Neural Learning
verfasst von : A. Abraham
Erschienen in: Fuzzy Systems Engineering
Verlag: Springer Berlin Heidelberg
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The integration of neural networks and fuzzy inference systems could be formulated into three main categories: cooperative, concurrent and integrated neuro-fuzzy models. We present three different types of cooperative neurofuzzy models namely fuzzy associative memories, fuzzy rule extraction using self-organizing maps and systems capable of learning fuzzy set parameters. Different Mamdani and Takagi-Sugeno type integrated neuro-fuzzy systems are further introduced with a focus on some of the salient features and advantages of the different types of integrated neuro-fuzzy models that have been evolved during the last decade. Some discussions and conclusions are also provided towards the end of the chapter.