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Matrix formulation of fuzzy rule-based systems

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3 Author(s)
Lotfi, A. ; Dept. of Electr. & Comput. Eng., Queensland Univ., Brisbane, Qld., Australia ; Andersen, H.C. ; Tsoi, A.C.

In this paper, a matrix formulation of fuzzy rule based systems is introduced. A gradient descent training algorithm for the determination of the unknown parameters can also be expressed in a matrix form for various adaptive fuzzy networks. When converting a rule-based system to the proposed matrix formulation, only three sets of linear/nonlinear equations are required instead of set of rules and an inference mechanism. There are a number of advantages which the matrix formulation has compared with the linguistic approach. Firstly, it obviates the differences among the various architectures; and secondly, it is much easier to organize data in the implementation or simulation of the fuzzy system. The formulation will be illustrated by a number of examples

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:26 ,  Issue: 2 )