A Single Layer Linear Countrpropagation (SLLIC) Network for Prediction and Function Approximation

摘要

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.