| 基于BP神经网络的控制算法及在制药生产中的应用 |
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| 引用本文： | 王旭,姜晓伟.基于BP神经网络的控制算法及在制药生产中的应用[J].中国科技信息,2012(1):108-109,111. |
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| 作者姓名： | 王旭 姜晓伟 |
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| 作者单位： | 1. 武汉中原瑞德生物制品有限贯任公司质量管理部,武汉,430023
2. 华中科技大学控制科学与工程系,武汉,430074 |
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| 基金项目： | 国家自然科学基金资助项目 |
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| 摘 要： | 制药工业是我国国民经济的主要产业,药品生产质量的要求也越来越严格.药品生产车间洁净区的环境条件是影响药品生产质量的主要因素之一,针对药品生产车间温度控制系统大滞后、大惯性、非线性,以及难以建立精确的数学模型等特点。在传统PID控制的基础上,基于神经网络具有任意非线形表达能力,以及可以通过对系统性能的学习来实现P I D参数的在线调整,确定最佳组合的P I D参数来实现车间温度的有效控制,本文通过仿真分析表明控制效果良好,具有调节时间短、无超调、稳态误差小等优点。
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| 关 键 词： | 药品生产 温度控制系统 BP神经网络 PID控制 |
The Control Algorithm Based on BP Neural Betwork and the Application in Pharmaceutical Production |
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| Wang Xu,Jiang Xiaowei.The Control Algorithm Based on BP Neural Betwork and the Application in Pharmaceutical Production[J].CHINA SCIENCE AND TECHNOLOGY INFORMATION,2012(1):108-109,111. |
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| Authors: | Wang Xu Jiang Xiaowei |
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| Institution: | 1 Department of Quality Management,Wuhan Zhongyuan Ruide Biological Products Co.,LTD,Wuhan 430023,China;2 Department of Control Science and Engineering,Huazhong University of Science and Technology,Wuhan 430074,China |
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| Abstract: | The pharmaceutical industry is the main economical industry of our country;the pharmaceutical production quality requirements are increasingly strict.The clean environment of pharmaceutical production workshop is one of the major factors that affect the quality of drug production;the temperature control system of workshop is always including time delay,big inertia,nonlinear,and,difficult to build accurate mathematical model and other characteristics.Based on the traditional PID control,the control algorithm based on BP neural network has the expressing ability of nonlinear,can realize online adjustment of PID parameters from the study of performance of the system,and determine the best combination of PID parameters to achieve the effective control of temperature workshop.The simulation analysis shows that the control effect is good,that is have short setting time,no overshoot,steady-state error is smaller,etc. |
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| Keywords: | Pharmaceutical production temperature control system BP neural network PID control |
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