A weighted grey CMAC neural network with output differentiability

Chih Ming Chen, Chin Ming Hong

研究成果: 書貢獻/報告類型會議論文篇章

4 引文 斯高帕斯(Scopus)


The Cerebellar Model Arithmetic Computer (CMAC) is a table lookup neurocomputing technique. It can be viewed as a basis function network (BFN) and performs well in terms of its fast learning speed, local generalization capability for approximating nonlinear functions. However, a disvantage is that the derivative of its output cannot be preserved due to the CMAC uses a constant basis function within each quantized state. This creates the limitation and inconvenience while the derivative information is needed in real-world applications. This paper proposes a Weight Grey CMAC (WGCMAC) that includes the conventional CMAC weight addressing scheme and the weighted grey prediction model to resolve this problem. Based on the weighted grey prediction model, we present an efficient learning algorithm for the proposed WGCMAC. Experiments confirm that the WGCMAC not only has a faster learning speed than the conventional CMAC, but also provides output derivatives and more precise learning result. Besides, compared with other enhanced CMAC models providing output derivatives, the proposed method has fastest learning speed.

主出版物標題Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS
編輯M.H. Smith, W.A. Gruver, L.O. Hall
出版狀態已發佈 - 2001
事件Joint 9th IFSA World Congress and 20th NAFIPS International Conference - Vancouver, BC, 加拿大
持續時間: 2001 七月 252001 七月 28


其他Joint 9th IFSA World Congress and 20th NAFIPS International Conference
城市Vancouver, BC

ASJC Scopus subject areas

  • Computer Science(all)
  • Media Technology

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