Abstract
The neural networks are excellent means of learning where training algorithms may be used for the tuning of the various parameters of the neural network. The fuzzy systems are extensively used for their fuzzy approach to problem modeling and solving. In this chapter we would present how the problem modeling capabilities of the fuzzy systems combines with the learning ability of the neural networks to create the Adaptive Neuro Fuzzy Inference Systems. We later see how these systems may be evolved using an evolutionary approach to make evolutionary neuro fuzzy systems. The other part of the chapter would focus upon the mechanism of fuzzy neural networks. These are neural networks that take fuzzy inputs and generate fuzzy outputs. Here we would transform the various neural computations into fuzzy arithmetic for problem solving. The neural networks are many times regarded as black boxes. We hence need specialized mechanisms to extract out rules from these networks for understanding and implementation. This would be discussed as the last part of the chapter.
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Shukla, A., Tiwari, R., Kala, R. (2010). Chapter 15 Hybridizing Neural and Fuzzy Systems. In: Towards Hybrid and Adaptive Computing. Studies in Computational Intelligence, vol 307. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14344-1_15
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DOI: https://doi.org/10.1007/978-3-642-14344-1_15
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