[Back-propagation neural network and genetic algorithm for multi-objective optimization of extraction technology of Cortex Fraxini].

作者：Ming Yang, Min-ying Yu, Xiufeng Shi, Teng Yan-ping · 发表于：PubMed · 年份：2008 · 被引用次数：2 · 研究领域：Wood and Agarwood Research、Traditional Chinese Medicine Studies、Physical Activity and Education Research

OBJECTIVE: To introduce Back-propagation (BP) neural network and genetic algorithm for multi-objective optimization of extraction technology of Cortex Fraxini. METHOD: BP neural network was established and optimized with uniform design. Genetic algotithm was used for multi-objective optimization of extraction technology of cortex fraxini. RESULT: the optimization of extraction was as follows: extraction temperature was 99 degrees C, concentration of EtOH was 50%, liquid-solid ratio was 7, extraction time was 94 min. The proportional error between predictive value and practical measured value was just -1.16% and -5.14%. CONCLUSION: Back-propagation neural network and genetic algorithm for multi-objective optimization of extraction technology of cortex fraxini is advisable.