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A novel higher order artificial neural networks
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.
History
Publication title
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 ScienceEditors
Jane Wei-Zhen Lu, Andrew YT Lerung, Vai Pan Iu, Kai Meng MokPagination
1507-1511ISBN
978-0-7354-0778-7Department/School
School of Information and Communication TechnologyPublisher
American Institute of PhysicsPlace of publication
United States of AmericaEvent title
International Symposium on Computational Mechanics (ISCM) and Enhancement and Promotion of Computational Methods in Engineering and Science (EPMESC)Event Venue
Hong Kong, MacauDate of Event (Start Date)
2009-11-30Date of Event (End Date)
2009-12-03Rights statement
Copyright © 2010 American Institute of PhysicsRepository Status
- Restricted