Deficiency in the current trend of training of neural network systems, suggestions and solutions
D. Tien, P. M. Nobar · 2002
Although artificial neural networks have been experimented extensively, many users pay little or no attention to the internal strum of such systems. As a result, inefficient algorithms were commonly used and much result was obtained on an ad hoc basic. The conventional training methods are not suitable for networks with large number of neurons. Furthermore, the learning rate constant can easily affect the convergence and the rate of convergence. In this paper, a number of non-linear optimisation algorithms have been proposed for training neural network systems with large number of neurons. Because of the strong mathematical background of these algorithms they can be used to train difficult neural networks with a single layer. The results have shown that the speed of the networks can be increased by several hundred times.