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Soft Sensor Modeling Based on PSO-FNN for Lysine Fermentation Process

文献类型： 外文期刊

作者： Huang, Yonghong 1 ; Xia, Chenglin 1 ; Sun, Yukun 1 ; Zhu, Xianglin 1 ; Wang, Yuejun 2 ;

作者机构： 1.Jiangsu Univ, Sch Elect & Informat Engn, Zhenjiang, Peoples R China

2.Chinese Acad Fishery Sci, Yellow Sea Fisheries Res Inst, Qingdao, Peoples R China

关键词： particle swarm optimization algorithm;fuzzy neural networks;soft sensor;modeling

期刊名称：2010 2ND INTERNATIONAL ASIA CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS (CAR 2010), VOL 2

ISSN： 1948-3414

年卷期： 2010 年

页码：

收录情况： SCI

摘要： 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.

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[1]Soft Sensor Modeling Based on PSO-FNN for Lysine Fermentation Process. Yonghong Huang,Chenglin Xia,Yukun Sun,Xianglin Zhu,Yuejun Wang. 2010

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