Self-optimizing for the Structure of CMAC Neural Network

摘要：

CMAC neural network has been widely applied on the real-time control of the nonlinear systems, such as robot control, aerocraft control and etc. However, the required memory size increases exponentially with the input dimension of CMAC, it may conduct to serious computational challenges in its on-line application. In this paper, experimental protocol is used for illustrating how the structure of CMAC influence the approximation qualities and required memory size. It is found that an optimal structure carrying the minimum modeling error could be achieved. The self-optimizing algorithm is then developed to adjust the structure of CMAC neural network in order to accomplish the minimum modeling error with minimum required memory size, without increase the structure complexness of the network.

关键词： CMAC neural network Structure parameter Self-optimizing

作者: Weiwei Yu K.Madani C. Sabourin

作者单位: School of Mechatronic Engineering Northwestern Polytechnical University XVan, Shaanxi Province, 7100 Images, Signals and Intelligence Systems Laboratory Senart Fontainebleau Institute of Technology PAR

会议类型: 国际会议

会议名称: 2010 Third International Symposium on Knowledge Acquisition and Modeling(第三届知识获取与建模国际研讨会 KAN 2010)

会议地点: 武汉

会议语种:英文

页码: 432-436

在线出版日期: 2010-10-20（万方平台首次上网日期，不代表论文的发表时间）