Performance comparison of BP and GRNN models of the neural network paradigm using a practical industrial application

Abstract

There is an increasing need to apply emerging technologies to achieve process improvements in a dynamic industrial environment. In particular, process control is increasingly popular as an area of manufacturing that can be significantly enhanced using neural networks. Neural networks offer a technology that has the capability, in the first instance, to model process behaviour without a-priori knowledge of the process or the need for complex calculations to model the process mathematically. This paper focuses on two particular networks in particular: backpropagation (BP) and general regression neural network (GRNN) models. As a measure of the performance of these two models, prediction accuracy is evaluated using a practical application in the aluminium smelting industry. The dynamic behaviour of aluminium smelting makes the particular application well-suited to neural network modelling.