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Fermentation process modeling of exopolysaccharide using neural networks and fuzzy systems with entropy criterion
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作者 Zuo-Ping Tan Shi-Tong Wang +1 位作者 zhao-hong deng Guo-Cheng Du 《Journal of Biomedical Science and Engineering》 2010年第4期430-438,共9页
The prediction accuracy and generalization of fermentation process modeling on exopolysaccharide (EPS) production from Lactobacillus are often deteriorated by noise existing in the corresponding experimental data. In ... The prediction accuracy and generalization of fermentation process modeling on exopolysaccharide (EPS) production from Lactobacillus are often deteriorated by noise existing in the corresponding experimental data. In order to circumvent this problem, a novel entropy-based criterion is proposed as the objective function of several commonly used modeling methods, i.e. Multi-Layer Perceptron (MLP) network, Radial Basis Function (RBF) neural network, Takagi-Sugeno-Kang (TSK) fuzzy system, for fermentation process model in this study. Quite different from the traditional Mean Square Error (MSE) based criterion, the novel entropy-based criterion can be used to train the parameters of the adopted modeling methods from the whole distribution structure of the training data set, which results in the fact that the adopted modeling methods can have global approximation capability. Compared with the MSE- criterion, the advantage of this novel criterion exists in that the parameter learning can effectively avoid the over-fitting phenomenon, therefore the proposed criterion based modeling methods have much better generalization ability and robustness. Our experimental results confirm the above virtues of the proposed entropy-criterion based modeling methods. 展开更多
关键词 RELATIVE ENTROPY MSE-Criterion Based Modeling ROBUSTNESS Parzen WINDOW TSK Fuzzy System
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