A novel multiple neural networks modeling method based on FCM

  • Authors:

  • Jian Cheng;Yi-Nan Guo;Jian-Sheng Qian

  • Affiliations:

  • School of Information and Electrical Engineering, China, University of Mining and Technology, Xuzhou, Jiangsu, China;School of Information and Electrical Engineering, China, University of Mining and Technology, Xuzhou, Jiangsu, China;School of Information and Electrical Engineering, China, University of Mining and Technology, Xuzhou, Jiangsu, China

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
  • Year:
  • 2006

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Abstract

A single neural network model developed from a limited amount of sample data usually lacks robustness and generalization. Neural network model robustness and prediction accuracy can be improved by combining multiple neural networks. In this paper a new method of the multiple neural networks for nonlinear modeling is proposed. A whole training sample data set is partitioned into several subsets with different centers using fuzzy c-means clustering algorithm (FCM), and the individual neural network is trained by each subset to construct the subnet respectively. The degrees of memberships are used for combining the outputs of subnets to obtain the final result, which are gained from the relationship between a new input sample data and each cluster center. This model has been evaluated and applied to estimate the status-of-loose of jig washer bed. Simulation results and actual application demonstrate that this model has better generalization, better prediction accuracy and wider potential application online.