A Novel Neuro-Fuzzy Inference System with Multi-Level Membership Function for Classification Applications
Cheng-Jian Lin*, Chi-Yung Lee**, and Cheng-Hung Chen*
*Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung, Taiwan 413, R.O.C.
**Department of Computer Science and Information Engineering, Nankai Institute of Technology, Nantou, Taiwan 542, R.O.C.
In this paper, a novel neuro-fuzzy inference system with multi-level membership function (NFIS_MMF) for classification applications is proposed. The NFIS_MMF model is a five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK). Layer 2 of the NFIS_MMF model contains multi-level membership functions, which are multilevel activation functions. A self-constructing learning algorithm, which consists of the self-clustering algorithm (SCA), fuzzy entropy, and the backpropagation algorithm, is also proposed to construct the NFIS_MMF model and perform parameter learning. Simulations were conducted to show the performance and applicability of the proposed model.
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