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

邹志云; 于德弘; 冯文强; 于鲁平; 郭宁

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

Updated：2026-01-06

- An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process
- Chinese Journal of Chemical Engineering Vol. 16, Issue 1, Pages: 62-66(2008)
- 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：
- Funds：
- DOI：
  CLC：
- Published：2008
- Accepted：
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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]. , 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：

摘要

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.

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