吉林大学学报(工学版) ›› 2012, Vol. 42 ›› Issue (01): 170-175.

• paper • Previous Articles     Next Articles

Improved hyperball CMAC neural network algorithm based on clustering

LI Hui1,2, DUAN Pei-yong3, ZHANG Qing-fan4   

  1. 1. Key Laboratory of Renewable Energy Utilization Technologies in Buildings of Ministry of Education, Shandong Jianzhu University, Ji'nan 250101, China;
    2. Shandong Key Laboratory of Building Energy-saving Technologies, Shandong Jianzhu University, Ji'nan 250101, China;
    3. Shandong Key Laboratory of Intelligent Buildings Technologies, Shandong Jianzhu University,Ji'nan 250101, China;
    4. School of Control Science and Engineering, Shandong University, Ji'nan 250061, China
  • Received:2010-07-29 Online:2012-01-01 Published:2012-01-01

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Abstract:

The number of nodes of the cerebelar model articulation controller (CMAC) neural network increases exponentially with the input dimensions. To overcome such drawback, an improved hyperball CAMC neural network algorithm based on clustering was proposed. A fuzzy clustering algorithm was adopted to determine the node number and node values of the neural network by clustering the input data. A fuzzy inference optimization algorithm was proposed to calculate the initial weight value of the neural network based on input-output data. Compared with the original hyperball CAMC, the improved algorithm can effectively reduce the neural network nodes and improve the learning accuracy. The multi-step time-delay nonlinear dynamic system simulation results demonstrate the feasibility and superiority of the proposed algorithm.

Key words: artificial intelligence, CMAC neural network, clustering, fuzzy inference, learning

CLC Number: 

  • TP183


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