Paper Title:
A Fusion CMAC Neural Network Based on Global Dynamic Information
  Abstract

CMAC neural network has two advantages: the local generalization and no local maximum value. Currently, ICA-CMAC and FCMAC models are used extensively. However, the two models cannot reasonably characterize the direction and magnitude of network weight in the weight correction algorithm. To solve the problem, an improved CMAC learning algorithm is proposed. It takes iterative errors, iteration number and a window function as the performance. Based on information fusion strategy, it introduces global information into the calculation to optimize the network weight. Through a simulation test, it can be found that the model has significant improvement in terms of convergence speed and prediction control.

  Info
Periodical
Advanced Materials Research (Volumes 268-270)
Edited by
Feng Xiong
Pages
1763-1767
DOI
10.4028/www.scientific.net/AMR.268-270.1763
Citation
Z. L. Ma, X. P. Luo, "A Fusion CMAC Neural Network Based on Global Dynamic Information", Advanced Materials Research, Vols. 268-270, pp. 1763-1767, 2011
Online since
July 2011
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Price
$32.00
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