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Adaptation of Fuzzy Inference System Using Neural Learning

  • A. Abraham
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 181)

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.

Keywords

Membership Function Fuzzy System Fuzzy Rule Rule Base Fuzzy Inference System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  • A. Abraham
    • 1
  1. 1.Computer Science DepartmentOklahoma State UniversityUSA

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