  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
