Abstract
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.
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Abraham, A. Adaptation of Fuzzy Inference System Using Neural Learning. In: Nedjah, N., Macedo Mourelle, L. (eds) Fuzzy Systems Engineering. Studies in Fuzziness and Soft Computing, vol 181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11339366_3
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DOI: https://doi.org/10.1007/11339366_3
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Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-540-32397-6
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