A new robust training algorithm for a class of single-hidden layer feedforward neural networks
A new robust training algorithm for a class of single-hidden layer feedforward neural networks
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DOI：
10.1016/j.neucom.2010.11.033
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发表时间：
2011-09
期刊：
影响因子：
6
通讯作者：
Z. Man;Kevin Lee;Dianhui Wang;Z. Cao;C. Miao
中科院分区：
文献类型：
--
作者：
Z. Man;Kevin Lee;Dianhui Wang;Z. Cao;C. Miao
A robust training algorithm for a class of single-hidden layer feedforward neural networks (SLFNs) with linear nodes and an input tapped-delay-line memory is developed in this paper. It is seen that, in order to remove the effects of the input disturbances and reduce both the structural and empirical risks of the SLFN, the input weights of the SLFN are assigned such that the hidden layer of the SLFN performs as a pre-processor, and the output weights are then trained to minimize the weighted sum of the output error squares as well as the weighted sum of the output weight squares. The performance of an SLFN-based signal classifier trained with the proposed robust algorithm is studied in the simulation section to show the effectiveness and efficiency of the new scheme.
