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A novel higher order artificial neural networks

conference contribution
posted on 2023-05-23, 04:57 authored by Shuxiang XuShuxiang Xu
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 Science

Editors

Jane Wei-Zhen Lu, Andrew YT Lerung, Vai Pan Iu, Kai Meng Mok

Pagination

1507-1511

ISBN

978-0-7354-0778-7

Department/School

School of Information and Communication Technology

Publisher

American Institute of Physics

Place of publication

United States of America

Event title

International Symposium on Computational Mechanics (ISCM) and Enhancement and Promotion of Computational Methods in Engineering and Science (EPMESC)

Event Venue

Hong Kong, Macau

Date of Event (Start Date)

2009-11-30

Date of Event (End Date)

2009-12-03

Rights statement

Copyright © 2010 American Institute of Physics

Repository Status

  • Restricted

Socio-economic Objectives

Information systems, technologies and services not elsewhere classified

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