Evaluation of Randomized Variable Translation Wavelet Neural Networks View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Khairul Anam , Adel Al-Jumaily

ABSTRACT

A variable translation wavelet neural network (VT-WNN) is a type of wavelet neural network that is able to adapt to the changes in the input. Different learning algorithms have been proposed such as backpropagation and hybrid wavelet-particle swarm optimization. However, most of them are time costly. This paper proposed a new learning mechanism for VT-WNN using random weights. To validate the performance of randomized VT-WNN, several experiments using benchmark data form UCI machine learning datasets were conducted. The experimental results show that RVT-WNN can work on a broad range of applications from the small size up to the large size with comparable performance to other well-known classifiers. More... »

PAGES

3-12

References to SciGraph publications

Book

TITLE

Soft Computing in Data Science

ISBN

978-981-10-7241-3
978-981-10-7242-0

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-981-10-7242-0_1

DOI

http://dx.doi.org/10.1007/978-981-10-7242-0_1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092889792


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