Abstrakti
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.
Alkuperäiskieli | Englanti |
---|---|
Sivut | 1038-1045 |
Sivumäärä | 8 |
Julkaisu | Computers and Chemical Engineering |
Vuosikerta | 30 |
Numero | 6-7 |
DOI - pysyväislinkit | |
Tila | Julkaistu - 15 toukok. 2006 |
OKM-julkaisutyyppi | A1 Julkaistu artikkeli, soviteltu |