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 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:	Machine learning
Research Field:	Neural networks
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Information systems, technologies and services not elsewhere classified
UTAS Author:	Xu, S (Dr Shuxiang Xu)
UTAS Author:	Kang, B-H (Professor Byeong Kang)
ID Code:	88625
Year Published:	2013
Deposited By:	Information and Communication Technology
Deposited On:	2014-02-11
Last Modified:	2018-03-18
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