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《China Mechanical Engineering》 2016-17
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Flatness Pattern Recognition via GA-T-S Cloud Inference Network Implemented by DSP

Li Haibin;Gao Wuyang;Lai Yongjin;Zhang Xiuling;Key Laboratory of Industrial Computer Control Engineering of Hebei Province,Yanshan University;National Engineering Research Center for Equipment and Technology of Cold Strip Rolling,Yanshan University;  
The existing neural networks we are mostly software simulation and the training time was long,thus that would not conducive to engineering applications.In view of the above problems,flatness pattern recognition via GA-T-S cloud inference network implemented by DSP was presented herein.Firstly,the DSP's design of T-S cloud inference network was implemented by using TI TMS320F2812 on the basis of flatness pattern recognition via GA-T-S cloud inference network.Then T-S cloud inference network parameters were optimized through genetic algorithm toolbox of MATLAB in off-line manner and these parameters were transmitted to DSP later.The network was run on MATLAB and DSP separately.Finally,the two results of T-S cloud inference network,which was run on MATLAB and DSP respectively,were compared and analyzed.Experimental results confirm that GA-T-S cloud inference network have high accuracy in terms of flatness pattern recognition,it can identify the defect types of flatness correctly.At the same time,the experimental results verify that the T-S cloud inference network can run on the hardware TI TMS320F2812 in a fast speed and it provides a basis for neural networks applied to practical engineering.
【Fund】: 国家自然科学基金资助项目(61007003);; 河北省自然科学基金-钢铁联合研究基金资助项目(E2015203354);; 河北省教育厅科学研究计划;; 河北省高等学校自然科学研究重点项目(ZD2016100)
【CateGory Index】: TG334.9;TP18
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