A Dual Structural Radial Basis Function Network for Recursive Function Estimation

摘要：

We present a dual structural radial basts function (RBF) network for recursive function estimation. This network is a hybrid system which consists of two sub-RBF networks. One sub-network models the relationship between the current network output and the past ones, and the other one describes the relationship between the current network output and the inputs. We propose a new variant of extended normalized RBF (ENRBF) network to implement each subRBF net. This variant uses an adjustable p-order single-term polynomial, rather than the first-order one, to fit the relations between each hidden unit and each output unit. It not only includes the existing ENRBF net as its special case, but also has better fitting ability in general under the moderate number of hidden units. The experiments have shown the proposed nets performance.

作者: Yiu-ming Cheung Lei Xu

作者单位: Department of Computer Science,Hong Kong Baptist University, Hong Kong, PRC Department of Computer Science and Engineering,The Chinese University of Hong Kong, PRC

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 1131-1135

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）