Learning polynomial feedforward neural networks by genetic programming and backpropagation

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@Article{ieee-nn:Nikolaev+Iba:2003,

• author = "Nikolay Y. Nikolaev and H. Iba",
• title = "Learning polynomial feedforward neural networks by genetic programming and backpropagation",
• journal = "IEEE Transactions on Neural Networks",
• year = "2003",
• type = "Paper",
• volume = "14",
• month = mar,
• pages = "337--350",
• number = "2",
• keywords = "genetic algorithms, genetic programming, Atmospheric modelling, Backpropagation algorithms, Biological system modeling, Feedforward neural networks, Multilayer perceptrons, Neural networks, Polynomials, Power system modeling, Predictive models, backpropagation, feedforward neural nets, genetic algorithms, learning (artificial intelligence), multilayer perceptrons, Volterra models, backpropagation, feedforward neural networks, genetic programming, learning, multilayer perceptrons, polynomial activation, polynomial feedforward neural networks, polynomial network structure, time series prediction",
• ISSN = "1045-9227",
• DOI = "doi:10.1109/TNN.2003.809405",
• abstract = "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 generalisation performance. Empirical results show that this approach finds PFNN which outperform considerably some previous constructive polynomial network algorithms on processing benchmark time series.",

}

Genetic Programming entries for Nikolay Nikolaev Hitoshi Iba

Citations