A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines
Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Journal Volume
37
Journal Issue
5
Pages
1321-1331
Date Issued
2007
Author(s)
Abstract
This paper investigates the feasibility of applying a relatively novel neural network technique, i.e., extreme learning machine (ELM), to realize a neuro-fuzzy Takagi-Sugeno-Kang (TSK) fuzzy inference system. The proposed method is an improved version of the regular neuro-fuzzy TSK fuzzy inference system. For the proposed method, first, the data that are processed are grouped by the k-means clustering method. The membership of arbitrary input for each fuzzy rule is then derived through an ELM, followed by a normalization method. At the same time, the consequent part of the fuzzy rules is obtained by multiple ELMs. At last, the approximate prediction value is determined by a weight computation scheme. For the ELM-based TSK fuzzy inference system, two extensions are also proposed to improve its accuracy. The proposed methods can avoid the curse of dimensionality that is encountered in backpropagation and hybrid adaptive neuro-fuzzy inference system (ANFIS) methods. Moreover, the proposed methods have a competitive performance in training time and accuracy compared to three ANFIS methods. ? 2007 IEEE.
Subjects
庥-means clustering; Adaptive neuro-fuzzy inference system (ANFIS); Extreme learning machine (ELM); Takagi-Sugeno-Kang (TSK) fuzzy inference system
Other Subjects
Approximation theory; Computational methods; Fuzzy inference; Fuzzy neural networks; Fuzzy rules; Adaptive neuro-fuzzy inference system (ANFIS); Extreme learning machine (ELM); Takagi-Sugeno-Kang (TSK) fuzzy inference system; Weight computation scheme; Learning systems; algorithm; article; artificial intelligence; artificial neural network; automated pattern recognition; computer simulation; fuzzy logic; methodology; statistical model; system analysis; Algorithms; Artificial Intelligence; Computer Simulation; Fuzzy Logic; Logistic Models; Neural Networks (Computer); Pattern Recognition, Automated; Systems Integration
Type
journal article