Research And Implementation Of Strip Thickness Prediction System Based On Improved Border Collie Optimizing LSTM

With the vigorous development of automobile industry,machinery manufacturing industry,daily hardware and other fields,the demand for strip steel is increasing,and the quality requirements of strip steel are more stringent.The thickness accuracy of strip is an important indicator to measure the quality of strip,so controlling the thickness accuracy of strip export is the primary problem that needs to be solved urgently for the export of high-quality strip products in the rolling industry.However,in the actual rolling process,many factors affecting strip thickness have the characteristics of time-varying,coupling and serious nonlinearity.The traditional strip thickness prediction algorithm is difficult to accurately model the nonlinear relationship and often ignores some important factors.At the same time,the existing artificial intelligence algorithm for nonlinear strip sequence feature mining is not deep enough,and the prediction accuracy is not high.Therefore,finding a novel and suitable artificial intelligence algorithm for strip thickness prediction is the focus of this paper.In recent years,the deep learning algorithm has brought new solutions to the nonlinear time series prediction problem.In this paper,an improved Border Collie Optimization is proposed to optimize the strip thickness prediction system of longshort-term memory network model（IBCO-LSTM）.Because the prediction accuracy of LSTM is affected by key super-parameters such as the number of hidden layer neurons and learning rate,the excellent Border Collie Optimization is used to optimize the parameters of LSTM.At the same time,the algorithm has the problems of uneven distribution of initial population and inaccurate optimization state of some individuals in the population.Firstly,tent map is proposed to initialize the population distribution,improve the quality of the initial population distribution,and then improve the global search ability of the algorithm.Secondly,the dynamic weighting strategy is introduced into the velocity updating formula of some individuals in the population to improve the local optimization accuracy of the algorithm in the search space.Finally,IBCO is used to search the optimal parameters of LSTM,and the optimal IBCO-LSTM model is established to predict strip thickness.The system design and development of five modules,registration login,user management,data processing,prediction model building and thickness prediction module.The data preprocessing module uses mutual information to select the feature of the data,and then normalizes the data using the maximum normalization method.Finally,the data set is divided into training set and test set as the input model data set.The prediction model construction module uses the improved edge-grazing optimization algorithm to search the optimal parameters of LSTM and train the optimal model;the thickness prediction module uses the trained model to realize the thickness prediction of strip steel.This paper selects the measured strip data of a domestic steel group as the experimental data.The comparison experiment verifies that IBCO-LSTM model has better prediction effect than the traditional model.After repeated tests,it is confirmed that the IBCO-LSTM strip thickness prediction system proposed in this paper can be effectively applied in the rolling industry.