This paper presents an explanation of a fuzzy model considering the correlation among components of input data. Generally, fuzzy models have a capability of dividing an input space into several subspaces compared 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 an ineffective partition of the input space. In order to solve this problem, this paper proposes a new fuzzy modeling algorithm, which partitions the input space more effectively than conventional fuzzy modeling algorithms by taking into consideration the correlation among components of sample data. As a way to use the correlation and divide the input space, the method of principal component is used. Finally, the results of the computer simulation are given to demonstrate the validity of this algorithm.
Bibliographical noteFunding Information:
Manuscript received April 19, 1996; revised November 25, 1997. This work was supported by the Ministry of Information and Communication (MIC) of Korea. E. Kim and M. Park are with the Department of Electronic Engineering, Yonsei University, Sudaemun-ku, Seoul, 120-749 Korea. M. Park is with the Department of Electronic Engineering, Seoul National Polytechnic University, Seoul, 139-743 Korea. S. Kim is with the Department of Electrical and Electronic Engineering, Soonchunhyang University, Chungnam, Korea. Publisher Item Identifier S 1063-6706(98)08266-6.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics