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Training a functional link neural network using an artificial bee colony for solving a classification problems

Mohmad Hassim, Yana Mazwin and Ghazali, Rozaida (2012) Training a functional link neural network using an artificial bee colony for solving a classification problems. Journal of Computing, 4 (9). pp. 110-115. ISSN 21519617

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Abstract

Artificial Neural Networks have emerged as an important tool for classification and have been widely used to classify a non-linear separable pattern. The most popular artificial neural networks model is a Multilayer Perceptron (MLP) as is able to perform classification task with significant success. However due to the complexity of MLP structure and also problems such as local minima trapping, over fitting and weight interference have made neural network training difficult. Thus, the easy way to avoid these problems is to remove the hidden layers. This paper presents the ability of Functional Link Neural Network (FLNN) to overcome the complexity structure of MLP by using single layer architecture and propose an Artificial Bee Colony (ABC) optimization for training the FLNN. The proposed technique is expected to provide better learning scheme for a classifier in order to get more accurate classification result.

Item Type: Article
Uncontrolled Keywords: Neural networks; functional link neural network; learning; artificial bee colony
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 07 Mar 2018 08:42
Last Modified: 07 Mar 2018 08:42
URI: http://eprints.uthm.edu.my/id/eprint/9711
Statistic Details: View Download Statistic

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