摘要
The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.
The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.
作者
张秀玲
高武杨
来永进
程艳涛
ZHANG Xiu-ling;GAO Wu-yang;LAI Yong-jin;CHENG Yan-tao
基金
Project(E2015203354)supported by Natural Science Foundation of Steel United Research Fund of Hebei Province,China
Project(ZD2016100)supported by the Science and the Technology Research Key Project of High School of Hebei Province,China
Project(LJRC013)supported by the University Innovation Team of Hebei Province Leading Talent Cultivation,China
Project(16LGY015)supported by the Basic Research Special Breeding of Yanshan University,China