安徽工程大学学报2012，Vol.27Issue(1)：5-8,4.
基于神经网络与遗传算法优化γ-氨基丁酸的发酵条件
Fermentation conditions optimization for γ-aminobutyric acid production based on neural network and genetic algorithms
摘要
Abstract
The solid fermentation conditions tor γ-ammobutyrm acla prouuction by ,wu optimized. Back-propagation neural network was established to model the relationships between the 7- aminobutyric acid yield and fermentation conditions. Genetic algorithm（GA）was applied for integrated optimization of the model. The trained network with the structure of 4-11-1 had a high generalization, the correlation coefficient between simulating outputs and the test data was 0. 989. The obtained optimal conditions were：temperature 31.7 ~C ,initial p H 4.6, initial water content 69.8 %, and inoculum volume 13.2%. Under above conditions, the yield of γ-aminobutyric acid reached 0. 518 mg/g, 19.6% higher than before.关键词
γ-氨基丁酸/神经网络/遗传算法/优化Key words
γ-aminobutyric acid/BP neural network/genetic algorithms/optimization分类
生物科学引用本文复制引用
吕闻闻,张庆庆,汤文晶,许鹏..基于神经网络与遗传算法优化γ-氨基丁酸的发酵条件[J].安徽工程大学学报,2012,27(1):5-8,4.基金项目
芜湖市重点科技基金资助项目（芜科计字[2009]190号文 （）
