Flatness predictive model based on T-S cloud reasoning network implemented by DSP

作者：张秀玲; 高武杨; 来永进; 程艳涛cloudreasoningneuralnetworkmodelflatnesspredictivehardwareimplementationdigitalsignalprocessorgeneticalgorithmandsimulatedannealing

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 900HC reversible cold rolling mill.Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.

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