The Influence of Training Data Selection on Performance of Neural Networks for Control of Non-Linear Systems*
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Abstract This work is part of research aimed at using artificial neural network models for real time process control over wide operating ranges where linear models either fail or must be adapted on-line. This paper discusses the influence on control performance of different methods for selecting and randomization/normalization of data used in estimating the weights in artificial neural network. The non-linear model system used comprises level control of a tank with non-vertical walls in which the level is controlled by manipulating out-flow and disturbances occures in the inflow. The simulation results show, that the control performance of the network is considerably influenced by the way in which the data for weight estimation are generated. A small randomized data set gives performance comparable to a data set sequential in time, which is many times larger. The performance of the trained artificial neural network in controlling the level of the tank is also compared with that of an IMC-PID controller. Results clearly demonstrate the advantage of the artificial neural network over the IMC-PID tuned at a nominal operating. The artificial neural network gives better performance over a wide operating range, because it accounts for the non-linear nature of the process.