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A novel higher order artificial neural networks

Citation

Xu, S, A novel higher order artificial neural networks, Proceedings of the Second International Symposium on Computational Mechanics and the 12th International Conference on the Enhancement and Promotion of Computational Methods in Engineering and Science, 30 November - 3 December 2009, Hong Kong, Macau, pp. 1507-1511. ISBN 978-0-7354-0778-7 (2010) [Refereed Conference Paper]


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Copyright Statement

Copyright © 2010 American Institute of Physics

DOI: doi:10.1063/1.3452131

Abstract

In this paper a new Higher Order Neural Network (HONN) model is introduced and applied in several data mining tasks. Data Mining extracts hidden patterns and valuable information from large databases. A hyperbolic tangent function is used as the neuron activation function for the new HONN model. Experiments are conducted to demonstrate the advantages and disadvantages of the new HONN model, when compared with several conventional Artificial Neural Network (ANN) models: Feedforward ANN with the sigmoid activation function; Feedforward ANN with the hyperbolic tangent activation function; and Radial Basis Function (RBF) ANN with the Gaussian activation function. The experimental results seem to suggest that the new HONN holds higher generalization capability as well as abilities in handling missing data.

Item Details

Item Type:Refereed Conference Paper
Keywords:artificial neural network, data mining, neuron activation function, higher order neural network
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Neural, Evolutionary and Fuzzy Computation
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Computer Software and Services not elsewhere classified
Author:Xu, S (Dr Shuxiang Xu)
ID Code:64941
Year Published:2010
Deposited By:Information and Communication Technology
Deposited On:2010-09-16
Last Modified:2014-12-22
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