Enhanced Artificial Neural Networks Model Based on a Single Layer Linear Counter propagation for Prediction and Function Approximation

¹Sameh Ghwanmeh, ¹Riyad Al-Shalabi,² Ghassan Kana'n,²1Luai Alnemi

¹⁾Computer Engineering Department, Hijjawi Faculty for Engineering Technology

Yarmouk University

sameh@yu.edu.jo

²⁾ Faculty of Information Technology                                                                                 

Department of Computer Information Systems

Ghassank@yu.edu.jo

         Abstract           

In this paper we investigate the use of neural networks in function approximation, data fitting, and prediction. Due to its

superior performance, the counterpropagation network was considered and an attempt was made to enhance its performance. As a result of this work, we propose a new neural network architecture named single layer linear counterpropagation (SLLIC) network. The SLLIC neural net has the following additional features: weight Initialization, automatic structure determination, and higher order neural network concepts. The SLLIC network was tested and results show that the performance of the system in terms of good approximation or prediction is comparable to and some times better than other neural nets architecture’s and traditional techniques.

  Keywords:

 neural networks, function approximation, prediction, forecasting.