A New Flatness Pattern Recognition Model Based on Artificial Fish School Neural Network
Aim at the problems occurring in a least square method model and a neural network model for flatness pattern recognition, A new approach of flatness pattern recognition based on artificial fish school neural network is proposed to meet the demand of high-precision flatness control for cold strip mill. The model is shown to fit the actual data pricisely and to overcome several disadvantages of the conventional BP neural network. Namely:slow convergence, low accuracy and difficulty in finding the global optimum. A series of tests have been conducted based on the data of the actual flatness pattern. The simulation results show that the speed and accuracy of the flatness pattern recognition model are obviously improved.
flatness pattern recognition artificial fish school neural network
Zhang Ruicheng Zheng Xin
College of Computer and Automatic Control Hebei Polytechnic University Hebei Tangshan 063009 China
国际会议
秦皇岛
英文
268-271
2010-11-05（万方平台首次上网日期，不代表论文的发表时间）