Adaptive Neuro-Fuzzy Networks with the Aid of Fuzzy Granulation

Authors:
Keun-Chang Kwak;Dong-Hwa Kim
Affiliations:
The author is with the Dept. of Electrical and Computer Eng., University of Alberta, Edmonton, Canada. E-mail: kwak@ece.ualberla.ca,;The author is with the Dept. of Control an Instrumentation Eng., Hanbat National University, DaeJeon, Korea.
Venue:
IEICE - Transactions on Information and Systems
Year:
2005

Citing 0
Cited 3

Development of quantum-based adaptive neuro-fuzzy networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Combining global model and local adaptive neuro-fuzzy network

ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
A clustering algorithm for radial basis function neural network initialization

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present the method for identifying an Adaptive Neuro-Fuzzy Networks (ANFN) with Takagi-Sugeno-Kang (TSK) fuzzy type based on fuzzy granulation. We also develop a systematic approach to generating fuzzy if-then rules from a given input-output data. The proposed ANFN is designed by the use of fuzzy granulation realized via context-based fuzzy clustering. This clustering technique builds information granules in the form of fuzzy sets and develops clusters by preserving the homogeneity of the clustered patterns associated with the input and output space. The experimental results reveal that the proposed model yields a better performance in comparison with Linguistic Models (LM) and Radial Basis Function Networks (RBFN) based on context-based fuzzy clustering introduced in the previous literature for Box-Jenkins gas furnace data and automobile MPG prediction.