Improving tuning capability of the adjusting neural network

The adjusting neural network (AJNN) we (1995) proposed previously has the capability for parameter tuning of a control model, namely it can perform parameter tuning accurately with small tuning numbers. However, when parameter errors are relatively large, its tuning capability may occasionally deteriorate, which leads to an increase of tuning numbers. In this paper, we discuss two ways of overcoming this weakness of the AJNN. We propose a new learning algorithm for the AJNN and develop the AJNN architecture. We simulate the effectiveness of both approaches and compare these results with results from our previous AJNN using the problem of temperature control for a reheating furnace plant.