title: [GSH fermentation process modeling using entropy-criterion based RBF neural network model].
abstract: The prediction accuracy and generalization of GSH fermentation process modeling are often deteriorated by noise existing in the corresponding experimental data. In order to avoid this problem, we present a novel RBF neural network modeling approach based on entropy criterion. It considers the whole distribution structure of the training data set in the parameter learning process compared with the traditional MSE-criterion based parameter learning, and thus effectively avoids the weak generalization and over-learning. Then the proposed approach is applied to the GSH fermentation process modeling. Our results demonstrate that this proposed method has better prediction accuracy, generalization and robustness such that it offers a potential application merit for the GSH fermentation process modeling.
authors: Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Information Engineering, Jiangnan University, Wuxi 214122, China. zuoping_tantan@126.com
authors: Zuoping
authors: Z
authors: Tan
authors: Shitong
authors: S
authors: Wang
authors: Zhaohong
authors: Z
authors: Deng
authors: Guocheng
authors: G
authors: Du
mesh_terms: D002175:Candida / Q000254:growth & development / Q000378:metabolism*; D019277:Entropy; D005285:Fermentation; D005978:Glutathione / Q000096:biosynthesis*; D016571:Neural Networks, Computer
chemical_list: D005978:Glutathione