Method for the selection of inputs and structure of feedforward neural networks

Feedforward neural networks of multi-layer perceptron 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...

Full description

Saved in:
Bibliographic Details
Published in:Computers & chemical engineering 2006-05, Vol.30 (6), p.1038-1045
Main Authors: Saxén, H., Pettersson, F.
Format: Article
Language:English
Subjects:
Detection of relevant inputs
Neural networks
Pruning algorithm
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Feedforward neural networks of multi-layer perceptron 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.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2006.01.007