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

摘要：

The adaptive learning and prediction of a highly nonlinear and time-varying bioreactor benchmark process is studied using NeurOn-Line environment, a graphical tool kit for developing and deploying neural networks in the G2 real time intelligent environment, and a new modified BFGS (Broyden, Fletcher, Goldfarb, and Shanno) quasi-Newton algorithm. The modified BFGS algorithm for the adaptive learning of BP neural networks is developed and embedded into NeurOn-Line by introducing a new searching 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

作者: Zou Zhiyun Ma Jidong Li Fuqing Yu Luping Feng Wenqiang Yu Dehong

作者单位: School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an,710049,China;Beijing Research Insti Beijing Research Institute of Pharmaceutical Chemistry,P.O.Box 1043,Beijing 102205,China School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an,710049,China

会议类型: 国际会议

会议名称: 第四届亚洲过程系统工程会议暨2007年中国国际系统工程年会(The 4th International Symposium on Design,Operation & Control of Chemical Processes)(PSE ASIA 2007)

会议地点: 西安

会议语种:英文

在线出版日期: 2007-08-15（万方平台首次上网日期，不代表论文的发表时间）