A Novel Neuro-Fuzzy Inference System with Multi-Level Membership Function for Classification Applications
Cheng‐Jian Lin, Chi-Yung Lee, Cheng-Hung Chen · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2007
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.