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Neural Processing Letters
Published in: Neural Processing Letters 1/2016

01-08-2016

An Improved Neural Network with Random Weights Using Backtracking Search Algorithm

Authors: Bingqing Wang, Lijin Wang, Yilong Yin, Yunlong Xu, Wenting Zhao, Yuchun Tang

Published in: Neural Processing Letters | Issue 1/2016

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Abstract

This paper proposes a hybrid algorithm by combining backtracking search algorithm (BSA) and a neural network with random weights (NNRWs), called BSA-NNRWs-N. BSA is utilized to optimize the hidden layer parameters of the single layer feed-forward network (SLFN) and NNRWs is used to derive the output layer weights. In addition, to avoid over-fitting on the validation set, a new cost function is proposed to replace the root mean square error (RMSE). In the new cost function, a constraint is added by considering RMSE on both training and validation sets. Experiments on classification and regression data sets show promising performance of the proposed BSA-NNRWs-N.
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Metadata
Title
An Improved Neural Network with Random Weights Using Backtracking Search Algorithm
Authors
Bingqing Wang
Lijin Wang
Yilong Yin
Yunlong Xu
Wenting Zhao
Yuchun Tang
Publication date
01-08-2016
Publisher
Springer US
Published in
Neural Processing Letters / Issue 1/2016
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-015-9480-z

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