Soft sensor modeling based on PSO-FNN for lysine fermentation process

  • Authors:

  • Yonghong Huang;Chenglin Xia;Yukun Sun;Xianglin Zhu;Yuejun Wang

  • Affiliations:

  • School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China;School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China;School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China;School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China;Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, China

  • Venue:
  • CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
  • Year:
  • 2010

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Abstract

In fermentation process, fuzzy neural networks (FNN) is a novel machine learning method of soft sensor modeling, while the typical algorithm of FNN is inefficient because they can not optimize fuzzy rules and has long training time. Biological parameters can be measured online in real time which is helpful for the control of process optimization. So this paper introduces the use of the particle swarm optimization (PSO) for training FNN. Unlike the conventional back-propagation technique, the adaptation of the weights of the FNN approximator is done on-line using PSO. The PSO is based on the least squares error minimization with random initial condition and without any off-line pre-training. Experiment results show that, in contrast to the traditional fuzzy neural networks, the method has good prediction and is suitable to practical applications.