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A self-organizing CMAC network with gray credit assignment.

Ming-Feng Yeh1, Kuang-Chiung Chang

  • 1Department of Electrical Engineering, Lunghwa University of Science and Technology, Taoyuan, 33327 Taiwan, ROC.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|June 10, 2006
PubMed
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This study introduces a novel self-organizing CMAC (SOCMAC) network, merging Cerebellar Model Articulation Controller (CMAC) with Self-Organizing Maps (SOM). The SOCMAC enhances unsupervised learning for data clustering and classification tasks.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Neural Networks

Background:

  • Self-Organizing Maps (SOMs) are effective for unsupervised learning but lack efficient error distribution.
  • Cerebellar Model Articulation Controllers (CMACs) excel at distributing learning errors but are not inherently self-organizing.
  • Integrating these architectures could enhance unsupervised learning capabilities.

Purpose of the Study:

  • To develop a novel neural network architecture, the self-organizing CMAC (SOCMAC), by incorporating CMAC structure into SOM's Kohonen layer.
  • To enable the SOCMAC to perform SOM functions while distributing learning errors like a CMAC.
  • To investigate the unsupervised learning, convergence properties, and performance of the SOCMAC for data analysis.

Main Methods:

Related Experiment Videos

  • Integration of Cerebellar Model Articulation Controller (CMAC) network structure within the Kohonen layer of a Self-Organizing Map (SOM).
  • Development of an unsupervised learning mechanism for the SOCMAC, where the neighborhood region is implicit.
  • Introduction of a credit-assignment technique based on gray relational analysis to accelerate SOCMAC learning.
  • Analysis of the convergence properties of the SOCMAC network under the proposed updating rule.

    • Main Results:

    • The proposed SOCMAC network successfully integrates SOM functionality with CMAC's error distribution capabilities.
    • Unsupervised learning is achieved, with the neighborhood region implicitly defined by the network's structure.
    • Gray relational analysis-based credit assignment significantly hastens the learning process.
    • Convergence analysis demonstrates that both memory contents and state outputs of the SOCMAC converge almost surely.
    • Application to data-clustering and data-classification problems shows superior performance compared to existing SOMs.

    Conclusions:

    • The self-organizing CMAC (SOCMAC) network offers an effective approach to unsupervised learning by combining the strengths of CMAC and SOM.
    • The implicit neighborhood definition and gray relational analysis-based credit assignment contribute to efficient and accelerated learning.
    • The SOCMAC demonstrates robust convergence properties and achieves better performance in data clustering and classification tasks than traditional SOMs.