Fuzzy models, modular networks, and hybrid learning Academic Article

abstract

• This paper proposes a new approach that can integrate fuzzy logic and neural networks in a "natural" manner. Unlike most existing fuzzy-neural models which usually make use of the structure of feedforward multilayer networks, the proposed model takes advantage of the structure of a kind of modular networks. We show that fuzzy models have a direct correspondence with the modular networks. Based on this correspondence, we develop an efficient hybrid learning scheme which combines an unsupervised learning algorithm (fuzzy-c-means algorithm) and a supervised algorithm (LMS algorithm). The utility of the proposed approach is illustrated using the well-known Zimmermann and Zysno data [36]. 1996 - Elsevier Science B.V. All rights reserved.

authors

• Langari, Gholamreza

published proceedings

• FUZZY SETS AND SYSTEMS

author list (cited authors)

• Langari, R., & Wang, L.

citation count

• 23

complete list of authors

• Langari, R||Wang, L

publication date

• January 1996

publisher

• Elsevier  Publisher

published in

• Fuzzy Sets and Systems  Journal

keywords

• Fuzzy Systems
• Learning Algorithms
• Modular Networks
• Sugeno-type Models

Digital Object Identifier (DOI)

• 10.1016/0165-0114(95)00151-4

start page

• 141

end page

• 150

volume

• 79

issue

• 2

URL

• http://dx.doi.org/10.1016/0165-0114(95)00151-4