An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process

›› 2008, Vol. 16 ›› Issue (1): 62-66.

• SELECTED PAPERS FROM THE 4^th INTERNATIONAL SYMPOSIUM ON DESIGN, OPERATION AND CONTROL OF CHEMICAL PROCESSES • Previous Articles Next Articles

An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process

ZOU Zhiyun^1,2, YU Dehong², FENG Wenqiang², YU Luping², GUO Ning²

1. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China;
2. Beijing Research Institute of Pharmaceutical Chemistry, Beijing 102205, China

Received:2007-01-15 Revised:2007-09-10 Online:2008-02-28 Published:2008-02-28
Supported by:
China Scholarship Council Grant (No.21302095).

An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process

邹志云^1,2, 于德弘², 冯文强², 于鲁平², 郭宁²

1. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China;
2. Beijing Research Institute of Pharmaceutical Chemistry, Beijing 102205, China

通讯作者: ZOU Zhiyun, E-mail: zouzhiyun@sohu.com
基金资助:
China Scholarship Council Grant (No.21302095).

Abstract

Abstract: 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 Neur On-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.

Key words: intelligent system, neural networks, adaptive learning, adaptive prediction, bioreactor process

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 Neur On-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.

关键词: intelligent system, neural networks, adaptive learning, adaptive prediction, bioreactor process

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]. , 2008, 16(1): 62-66.

邹志云, 于德弘, 冯文强, 于鲁平, 郭宁. An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process[J]. , 2008, 16(1): 62-66.

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An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process

An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process

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