Training a functional link neural network using an artificial bee colony for solving a classification problems
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 succ...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
ArXiv
2012
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/8047/1/J4152_b23e85ffc116c4d53a41f77cebc585db.pdf http://eprints.uthm.edu.my/8047/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|