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The Optimal Partition of Initial Input Space for Fuzzy Neural System : Measure of Fuzziness  

Baek, Deok-Soo (Iksan National College Dept. of Electronics & Information)
Park, In-Kue (Divition of Computer Engineering, Joongbu Univ.)
Publication Information
Journal of the Institute of Electronics Engineers of Korea TE / v.39, no.3, 2002 , pp. 97-104 More about this Journal
Abstract
In this paper we describe the method which optimizes the partition of the input space by means of measure of fuzziness for fuzzy neural network. It covers its generation of fuzzy rules for input sub space. It verifies the performance of the system depended on the various time interval of the input. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rule base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. According to the input interval the proposed inference procedure proves that the fast convergence of root mean square error (RMSE) owes to the optimal partition of the input space
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