Neural-network-based d-step-ahead predictors for nonlinear systems with time delay

Abstract Three neural-network-based d -step-ahead prediction strategies for nonlinear processes with time-delay are presented here. They are, respectively, a recursive d -step-ahead neural predictor, a non-recursive d -step-ahead neural predictor, and a Smith-type neural predictor that can also be used for d -step-ahead prediction. Both the recursive and the non-recursive predictors have been extended to the case of long-range prediction. It is known that both the non-recursive d -step-ahead predictor and the Smith predictor have been applied to linear processes with time-delay. It would be useful to extend these strategies to nonlinear systems. Therefore, an extension of both the non-recursive d -step-ahead predictor and the Smith predictor principle to the nonlinear case is presented here. These nonlinear predictors can be constructed by using neural networks. This is very useful for the time-delay compensation of nonlinear processes. The neural-network-based predictors have been applied to the predictions of some nonlinear processes. A comparison based on simulation is given in this paper. Finally, the proposed neural-network-based predictors are used to predict the manifold pressure process in an automotive engine. The predictive result of the corresponding first-principles model-based nonlinear predictor is also illustrated for comparison. The experimental results show that the neural-network-based predictive methods have obtained better performance.

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