期刊
COMPUTERS & CHEMICAL ENGINEERING
卷 30, 期 6-7, 页码 1038-1045出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2006.01.007
关键词
neural networks; detection of relevant inputs; pruning algorithm
Feedforward neural networks of multi-layer perception type can be used as nonlinear black-box models in data-mining tasks. Common problems encountered are how to select relevant inputs from a large set of variables that potentially affect the outputs to be modeled, as well as high levels of noise in the data sets. In order to avoid over-fitting of the resulting model, the input dimension and/or the number of hidden nodes have to be restricted. This paper presents a systematic method that can guide the selection of both input variables and a sparse connectivity of the lower layer of connections in feedforward neural networks of multi-layer perceptron type with one layer of hidden nonlinear units and a single linear output node. The algorithm is illustrated on three benchmark problems. (c) 2006 Published by Elsevier Ltd.
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