Learning polynomial feedforward neural networks by genetic programming and backpropagation

Authors:
N. Y. Nikolaev;H. Iba
Affiliations:
Dept. of Math. & Comput. Sci., Univ. of London, UK;-
Venue:
IEEE Transactions on Neural Networks
Year:
2003

Citing 0
Cited 15

On the construction of a nonlinear recursive predictor

Journal of Computational and Applied Mathematics - Special issue: International conference on mathematics and its application
Multilogistic regression by means of evolutionary product-unit neural networks

Neural Networks
Integrated Analytic Framework for Neural Network Construction

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Iteratively Reweighted Fitting for Reduced Multivariate Polynomial Model

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
A flexible visual inspection system based on neural networks

International Journal of Systems Science - Innovative Production Machines and Systems, Guest Editors: Duc-Truong Pham, Anthony Soroka and Eldaw Eldukhri
Evolutionary Artificial Neural Network Design and Training for wood veneer classification

Engineering Applications of Artificial Intelligence
Review of development of nonconventional neural architectures at the Czech technical university in Prague

NN'09 Proceedings of the 10th WSEAS international conference on Neural networks
Combining evolution strategy and gradient descent method for discriminative learning of bayesian classifiers

Proceedings of the 11th Annual conference on Genetic and evolutionary computation
EvoArch: An evolutionary algorithm for architectural layout design

Computer-Aided Design
On the construction of a nonlinear recursive predictor

Journal of Computational and Applied Mathematics
Research of immune neural network model based on extenics

LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Automatic induction of projection pursuit indices

IEEE Transactions on Neural Networks
Prediction of grain yield using SIGA-BP neural network

AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Solving large n-bit parity problems with the evolutionary ANN ensemble

ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Optimization of self-organizing polynomial neural networks

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.01

Visualization

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 generalization performance. Empirical results show that this approach finds PFNN which outperform considerably some previous constructive polynomial network algorithms on processing benchmark time series.