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Indexed by:会议论文

Date of Publication:2013-07-04

Included Journals:EI、Scopus

Volume:7951 LNCS

Issue:PART 1

Page Number:91-98

Abstract:In this paper, we propose a novel generalized single-hidden layer feedforward network (GSLFN) by employing polynomial functions of inputs as output weights connecting randomly generated hidden units with corresponding output nodes. The main contributions are as follows. For arbitrary N distinct observations with n-dimensional inputs, the augmented hidden node output matrix of the GSLFN with L hidden nodes using any infinitely differentiable activation functions consists of L sub-matrix blocks where each includes n + 1 column vectors. The rank of the augmented hidden output matrix is proved to be no less than that of the SLFN, and thereby contributing to higher approximation performance. Furthermore, under minor constraints on input observations, we rigorously prove that the GLSFN with L hidden nodes can exactly learn L(n + 1) arbitrary distinct observations which is n + 1 times what the SLFN can learn. If the approximation error is allowed, by means of the optimization of output weight coefficients, the GSLFN may require less than N/(n + 1) random hidden nodes to estimate targets with high accuracy. Theoretical results of the GSLFN evidently perform significant superiority to that of SLFNs. ? 2013 Springer-Verlag Berlin Heidelberg.