| 一种生化反应器智能自适应学习和预测神经元网络系统 |
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| 引用本文： | 邹志云,于德弘,冯文强,于鲁平,郭宁.一种生化反应器智能自适应学习和预测神经元网络系统[J].中国化学工程学报,2008,16(1):62-66. |
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| 作者姓名： | 邹志云 于德弘 冯文强 于鲁平 郭宁 |
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| 作者单位： | 1. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China;2. Beijing Research Institute of Pharmaceutical Chemistry, Beijing 102205, China |
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| 基金项目： | Supported by China Scholarship Council Grant （No.21302095）. |
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| 摘 要： | The adaptive learning and prediction of a highly nonlinear and time-varying bioreactor benchmark process is studied using Neur-On-Line, a graphical tool kit for developing and deploying neural networks in the G2 real time intelligent environment,and a new modified Broyden, Fletcher, Goldfarb, and Shanno （BFGS） quasi-Newton algorithm. The modified BFGS algorithm for the adaptive learning of back propagation （BP） neural networks is developed and embedded into NeurOn-Line by introducing a new search method of learning rate to the full memory BFGS algorithm. Simulation results show that the adaptive learning and prediction neural network system can quicklv track the time-varving and nonlinear behavior of the bioreactor.
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| 关 键 词： | intelligent system neural networks adaptive learning adaptive prediction bioreactor process |
| 收稿时间： | 2007-01-15 |
| 修稿时间： | 2007-09-10 |
An intelligent neural networks system for adaptive learning and prediction of a bioreactor benchmark process |
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| ZOU Zhiyun,YU Dehong,FENG Wenqiang,YU Luping,GUO Ning.An intelligent neural networks system for adaptive learning and prediction of a bioreactor benchmark process[J].Chinese Journal of Chemical Engineering,2008,16(1):62-66. |
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| Authors: | ZOU Zhiyun YU Dehong FENG Wenqiang YU Luping GUO Ning |
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| Affiliation: | 1. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China;2. Beijing Research Institute of Pharmaceutical Chemistry, Beijing 102205, China |
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| Abstract: | The adaptive learning and prediction of a highly nonlinear and time-varying bioreactor benchmark proc-ess is studied using NeurOn-Line, a graphical tool kit for developing and deploying neural networks in the G2 real time intelligent environment, and a new modified Broyden, Fletcher, Goldfarb, and Shanno (BFGS) quasi-Newton algorithm. The modified BFGS algorithm for the adaptive learning of back propagation (BP) neural networks is developed and embedded into NeurOn-Line by introducing a new search method of learning rate to the full memory BFGS algorithm. Simulation results show that the adaptive learning and prediction neural network system can quickly track the time-varying and nonlinear behavior of the bioreactor. |
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| Keywords: | intelligent system neural networks adaptive learning adaptive prediction bioreactor process |
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