Parameter Prediction of Marine Seawater Cooling System Based on Chaos-Elman Combined Model

Abstract

To improve the accuracy of short-term prediction of marine seawater cooling system parameters and reduce the accuracy loss caused by chaotic data, a combined prediction model combining chaos theory and Elman feedback neural network was proposed in this paper. Firstly, the C-C algorithm and G-P algorithm were used to calculate the two reconstruction parameters respectively, and according to the properties of the maximum Lyapunov exponent, it could be determined that the original one-dimensional time series had chaotic characteristics. Secondly, phase space reconstruction technique was used to map the one-dimensional seawater outlet temperature time series of intercooler to the high-dimensional space to find its chaotic phase trajectory, and the number of adjacent points was determined by HQ (Hannan Quinn) information criterion. Finally, the Chaos-Elman combined prediction model was used to train and test 300 sets of seawater outlet temperature data of intercooler, and the accuracy of prediction was compared with that of single structure model. The experimental results were as follows: the average prediction accuracy based on chaos theory was 95.5%, the prediction accuracy of Elman neural network was 95.1%, and the prediction accuracy of Chaos-Elman combined model could reach 98.7%. In this study, the Chaos-Elman combined model has better prediction accuracy than the single model. It can be concluded that the Chaos-Elman combined model can be used for the short-term prediction of seawater cooling system parameters, and the prediction accuracy and reliability are high.