Zhang Yu, Xu Jing-Liang, Yuan Zhen-Hong, Qi Wei, Liu Yun-Yun, He Min-Chao
Key Laboratory of Renewable Energy and Gas Hydrate & Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China.
Int J Mol Sci. 2012;13(7):7952-7962. doi: 10.3390/ijms13077952. Epub 2012 Jun 26.
Two artificial intelligence techniques, namely artificial neural network (ANN) and genetic algorithm (GA) were combined to be used as a tool for optimizing the covalent immobilization of cellulase on a smart polymer, Eudragit L-100. 1-Ethyl-3-(3-dimethyllaminopropyl) carbodiimide (EDC) concentration, N-hydroxysuccinimide (NHS) concentration and coupling time were taken as independent variables, and immobilization efficiency was taken as the response. The data of the central composite design were used to train ANN by back-propagation algorithm, and the result showed that the trained ANN fitted the data accurately (correlation coefficient R(2) = 0.99). Then a maximum immobilization efficiency of 88.76% was searched by genetic algorithm at a EDC concentration of 0.44%, NHS concentration of 0.37% and a coupling time of 2.22 h, where the experimental value was 87.97 ± 6.45%. The application of ANN based optimization by GA is quite successful.
将两种人工智能技术，即人工神经网络（ANN）和遗传算法（GA）相结合，用作优化纤维素酶在智能聚合物Eudragit L-100上共价固定化的工具。以1-乙基-3-（3-二甲基氨基丙基）碳二亚胺（EDC）浓度、N-羟基琥珀酰亚胺（NHS）浓度和偶联时间作为自变量，以固定化效率作为响应。采用中心复合设计的数据，通过反向传播算法对人工神经网络进行训练，结果表明训练后的人工神经网络能准确拟合数据（相关系数R² = 0.99）。然后通过遗传算法在EDC浓度为0.44%、NHS浓度为0.37%和偶联时间为2.22 h的条件下搜索到最大固定化效率为88.76%，此时实验值为87.97±6.45%。基于遗传算法的人工神经网络优化应用相当成功。
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