Enhanced Neural Networks Model Based on a Single Layer Linear Counterpropagation for Prediction and Function Approximation

摘要

This study investigated the use of neural network s 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 research, we proposed 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.