An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process
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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
中国化学工程学报(英文版)2008年16卷第1期 页码:62-66
Affiliations:
1. School of Mechanical Engineering, Xi’an Jiaotong University,Xi’an,China,710049
2. Beijing Research Institute of Pharmaceutical Chemistry,Beijing,China,102205
Author bio:
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纸质出版:2008
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邹志云, 于德弘, 冯文强, 等. An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process[J]. 中国化学工程学报(英文版), 2008,16(1):62-66.
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, 等. An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process[J]. 中国化学工程学报(英文版), 2008,16(1):62-66.DOI:
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.DOI:
An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process
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.
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