Neural network driven fuzzy inference system

R. J. Kuo, P. H. Cohen, Soundar Rajan Tirupatikumara

Research output: Contribution to conferencePaperpeer-review

5 Scopus citations

Abstract

Based on theoretical results, fuzzy systems are universal approximators. In this paper, we propose a novel learning approach, self-organizing and self-adjusting fuzzy modeling (SOSAFM), for inference rules. Basically, the proposed system consists of two stages, the self-organizing state (SOS) and the self-adjusting stage (SAS). In the first stage, the input data is divided into several groups by applying Kohonen's feature maps. Gaussian distribution functions are employed as the standard form of the membership functions. Methods of statistics are used to determine the center and width of the membership function for each group. Regarding the consequences, the linear regression method is used. After the above procedures, we can decide the initial parameters of fuzzy system. Then, the error backpropagation-type learning method is used to fine-tune the parameters. The simulation results show that the proposed approach is better than conventional neural networks in both accuracy and speed.

Original languageEnglish (US)
Pages1532-1536
Number of pages5
StatePublished - Dec 1 1994
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: Jun 27 1994Jun 29 1994

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period6/27/946/29/94

All Science Journal Classification (ASJC) codes

  • Software

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