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A machine learning approach for modeling and its applications

Citation

Xu, S and Liu, Y and Kang, B-H and Gao, W, A machine learning approach for modeling and its applications, Proceedings of the European Modeling and Simulation Symposium, 25-27 September, 2013, Athens, Greece, pp. 659-663. ISBN 978-88-97999-16-4 (2013) [Refereed Conference Paper]

Copyright Statement

Copyright 2013 Dime UniversitÓ di Genova

Official URL: http://www.msc-les.org/conf/emss2013/

Abstract

This paper proposes a new learning algorithm for Higher Order Neural Networks for the purpose of modelling and applies it in three benchmark problems. Higher Order Neural Networks (HONNs) are Artificial Neural Networks (ANNs) in which the net input to a computational neuron is a weighted sum of its inputs and products of its inputs (rather than just a weighted sum of its inputs as in traditional ANNs). It was well known that HONNs can implement invariant pattern recognition. The new learning algorithm proposed is an Extreme Learning Machine (ELM) algorithm. ELM randomly chooses hidden neurons and analytically determines the output weights. With ELM algorithm only the connection weights between hidden layer and output layer are adjusted. This paper proposes an ELM algorithm for HONN models and applies it in an image processing problem, a medical problem, and an energy efficiency problem. The experimental results demonstrate the advantages of HONN models with the ELM algorithm in such aspects as significantly faster learning and improved generalization abilities (in comparison with standard HONN and traditional ANN models).

Item Details

Item Type:Refereed Conference Paper
Keywords:artificial neural network, higher order neural network, extreme learning machine, machine learning
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)
Author:Kang, B-H (Professor Byeong Kang)
ID Code:88625
Year Published:2013
Deposited By:Computing and Information Systems
Deposited On:2014-02-11
Last Modified:2017-11-13
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