A comparative study of expansion functions for evolutionary hybrid functional link artificial neural networks for data mining and classification

This paper presents a comparison between different expansion function for a specific structure of neural network as the functional link artificial neural network (FLANN). This technique has been employed for classification tasks of data mining. In fact, there are a few studies that used this tool for solving classification problems, and in the most case, the trigonometric expansion function is the most used. In this present research, we propose a hybrid FLANN (HFLANN) model, where the optimization process is performed using 3 known population based techniques such as genetic algorithms, particle swarm and differential evolution. This model will be empirically compared using different expansion function and the best function one will be selected.