Improved Parameter Identification Method Based on Moving Rate

To improve the problem that the parameter identification for fuzzy neural network has many time complexities in calculating, an improved T-S fuzzy inference method and an parameter identification method for fuzzy neural network are proposed. It mainly includes three parts. First, improved fuzzy infe...

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Bibliographic Details
Published in:arXiv.org 2014-12
Main Authors: Ho, Chol Man, Son Il Gwak, Song Ho Pak, Ha, Jong Won
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Complexity
Fuzzy logic
Inference
Neural networks
Parameter identification
Weather forecasting
Online Access:Get full text
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Summary:To improve the problem that the parameter identification for fuzzy neural network has many time complexities in calculating, an improved T-S fuzzy inference method and an parameter identification method for fuzzy neural network are proposed. It mainly includes three parts. First, improved fuzzy inference method based on production term for T-S Fuzzy model is explained. Then, compared with existing Sugeno fuzzy inference based on Compositional rules and type-distance fuzzy inference method, the proposed fuzzy inference algorithm has a less amount of complexity in calculating and the calculating process is simple. Next, a parameter identification method for FNN based on production inference is proposed. Finally, the proposed method is applied for the precipitation forecast and security situation prediction. Test results showed that the proposed method significantly improved the effectiveness of identification, reduced the learning order, time complexity and learning error.
ISSN:2331-8422