Training a Functional Link Neural Network Using an Artificial Bee Colony for Solving a Classification Problems

TL;DR

This paper introduces a novel training method for a simple neural network model called FLNN using Artificial Bee Colony optimization, aiming to improve classification accuracy and avoid common training issues of complex neural networks.

Contribution

It proposes using ABC optimization to train FLNN, simplifying the neural network structure and enhancing classification performance compared to traditional methods.

Findings

ABC-trained FLNN achieves higher accuracy.

The method reduces training complexity.

Improved classification results over standard approaches.

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 it 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