Abstract
Some knowledge-based fuzzy neural network models for on-line learning, such as EFuNN and FuzzyARTMAP, were presented in the previous chapter. Fuzzy neural networks are connectionist models that are trained as neural networks, but their structure can be interpreted as a set of fuzzy rules. In contrast, neuro-fuzzy inference systems consist of a set of rules and an inference method that are embodied or combined with a connectionist structure for better adaptation. Evolving neuro-fuzzy inference systems are such systems, where both the knowledge and the inference mechanism evolve and change in time, with more examples presented to the system. In the models here knowledge is represented as both fuzzy rules and statistical features that are learned in an online lifelong learning mode. In the last three sections of the chapter different types of fuzzy rules, membership functions and receptive fields in ECOS (which include both evolving fuzzy neural networks and evolving neuro-fuzzy inference systems) are analysed and new modifications of EGOS are introduced.
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Kasabov, N. (2003). Evolving Neuro-Fuzzy Inference Systems. In: Evolving Connectionist Systems. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-3740-5_5
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DOI: https://doi.org/10.1007/978-1-4471-3740-5_5
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