%0 Journal Article
%T GSH Fermentation Process Modeling Using Entropy-criterion Based RBF Neural Network Model<br>基于熵准则的鲁棒的RBF谷胱甘肽发酵建模
%A Zuoping Tan
%A Shitong Wang
%A Zhaohong Deng
%A Guocheng Du
%A <br>谭左平
%A 王士同
%A 邓赵红
%A 堵国成
%J 生物工程学报
%D 2008
%I 
%X 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.
%K GSH
%K relative entropy
%K RBF neural network
%K Parzen window
%K robustness<br>谷胱甘肽
%K 相对熵
%K RBF神经网络
%K Parzen窗法
%K 鲁棒性
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=A66E90C274451689E69F6F0291467824&aid=DB419B9EA2E27D0B92425209E54B638C&yid=67289AFF6305E306&vid=B91E8C6D6FE990DB&iid=94C357A881DFC066&sid=C81F81170838C444&eid=BC60A9A1D91963F5&journal_id=1000-3061&journal_name=生物工程学报&referenced_num=0&reference_num=22