Neuro-fuzzy Systems

Andrea Tettamanzi () and Marco Tomassini ()
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Andrea Tettamanzi: University of Milan, Information Technology Department
Marco Tomassini: University of Lausanne, Computer Science Institute

Chapter Chapter 6 in Soft Computing, 2001, pp 201-231 from Springer

Abstract: Abstract This Chapter deals with neuro-fuzzy systems, i. e., those soft computing methods that combine in various ways neural networks and fuzzy concepts. Each methodology has its particular strengths and weaknesses that make it more or less suitable in a given context. For example, fuzzy systems can reason with imprecise information and have good explanatory power. On the other hand, rules for fuzzy inference have to be explicitly built into the system or communicated to it in some way; in other words the system cannot learn them automatically. Neural networks represent knowledge implicitly, are endowed with learning capabilities, and are excellent pattern recognizers. But they are also notoriously difficult to analyze: to explain how exactly hey reach their conclusions is far from easy while the knowledge is explicitly represented through rules in fuzzy systems.

Keywords: Membership Function; Hide Layer; Fuzzy System; Fuzzy Rule; Connection Weight (search for similar items in EconPapers)
Date: 2001
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DOI: 10.1007/978-3-662-04335-6_6

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