Learning polynomial feedforward neural networks by genetic programming and backpropagation

This paper presents an approach to learning polynomial feedforward neural networks (PFNNs). The approach suggests, first, finding the polynomial network structure by means of a population-based search technique relying on the genetic programming paradigm, and second, further adjustment of the best d...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2003-03, Vol.14 (2), p.337-350
Main Authors: Nikolaev, N.Y., Iba, H.
Format: Article
Language:English
Subjects:
Atmospheric modeling
Backpropagation algorithms
Biological system modeling
Feedforward neural networks
Genetic programming
Multilayer perceptrons
Neural networks
Polynomials
Power system modeling
Predictive models
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Summary:This paper presents an approach to learning polynomial feedforward neural networks (PFNNs). The approach suggests, first, finding the polynomial network structure by means of a population-based search technique relying on the genetic programming paradigm, and second, further adjustment of the best discovered network weights by an especially derived backpropagation algorithm for higher order networks with polynomial activation functions. These two stages of the PFNN learning process enable us to identify networks with good training as well as generalization performance. Empirical results show that this approach finds PFNN which outperform considerably some previous constructive polynomial network algorithms on processing benchmark time series.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/TNN.2003.809405