A TRANSFORMED INPUT-DOMAIN APPROACH TO FUZZY MODELING

This paper presents an explanation of a fuzzy model considering the co rrelation among components of input data. Generally, fuzzy models have a capability of dividing an input space into several subspaces compar ed to a linear model. But hitherto suggested fuzzy modeling algorithms have not taken into consideration the correlation among components of sample data and have addressed them independently, which results in a n ineffective partition of the input space. In order to solve this pro blem, this paper proposes a new fuzzy modeling algorithm, which partit ions the input space more effectively than conventional fuzzy modeling algorithms by taking into consideration the correlation among compone nts of sample data. As a way to use the correlation and divide the inp ut space, the method of principal component is used. Finally, the resu lts of the computer simulation are given to demonstrate the validity o f this algorithm.