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JACIII Vol.11 No.4 pp. 365-372
doi: 10.20965/jaciii.2007.p0365
(2007)

Paper:

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.

Received:
March 29, 2006
Accepted:
August 11, 2006
Published:
April 20, 2007
Keywords:
fuzzy entropy, neuro-fuzzy inference system, classification, self-clustering algorithm, backpropagation
Abstract

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.

Cite this article as:
C. Lin, C. Lee, and C. Chen, “A Novel Neuro-Fuzzy Inference System with Multi-Level Membership Function for Classification Applications,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.4, pp. 365-372, 2007.
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Last updated on Jan. 21, 2019