HONNs with ELM algorithm for medical applications

Abstract

Higher Order Neural Networks (HONNs) are Artificial Neural Networks (ANNs) in which the net input to a computational neuron is a weighted sum of products of its inputs (rather than just a weighted sum of its inputs as in traditional ANNs). It was known that HONNs can implement invariant pattern recognition as well as handling high frequency and high order nonlinear business data. Extreme Learning Machine (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 develops an ELM algorithm for HONN models and applies it in several significant medical cases. The experimental results demonstrate significant advantages of HONN models with ELM algorithm such as faster training and improved generalization abilities (in comparison with standard HONN models).