| Learning polynomial feedforward neural networks by genetic programming and backpropagation. | |
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MedLine Citation:
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PMID: 18238017 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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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. |
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Authors:
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N Y Nikolaev; H Iba |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council Volume: 14 ISSN: 1045-9227 ISO Abbreviation: IEEE Trans Neural Netw Publication Date: 2003 |
Date Detail:
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Created Date: 2008-02-01 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101211035 Medline TA: IEEE Trans Neural Netw Country: United States |
Other Details:
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Languages: eng Pagination: 337-50 Citation Subset: - |
Affiliation:
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Dept. of Math. and Comput. Sci., Univ. of London, UK. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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