A fast multi-output RBF neural network construction method

Authors:
Dajun Du;Kang Li;Minrui Fei
Affiliations:
Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronical Engineering and Automation, Shanghai University, Shanghai 200072, China and School of Electronics, Electrica ...;School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast BT9 5 AH, UK;Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronical Engineering and Automation, Shanghai University, Shanghai 200072, China
Venue:
Neurocomputing
Year:
2010

Citing 24
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Abstract

This paper investigates the center selection of multi-output radial basis function (RBF) networks, and a multi-output fast recursive algorithm (MFRA) is proposed. This method can not only reveal the significance of each candidate center based on the reduction in the trace of the error covariance matrix, but also can estimate the network weights simultaneously using a back substitution approach. The main contribution is that the center selection procedure and the weight estimation are performed within a well-defined regression context, leading to a significantly reduced computational complexity. The efficiency of the algorithm is confirmed by a computational complexity analysis, and simulation results demonstrate its effectiveness.