基于神经网络和遗传算法的木糖醇发酵培养基优化研究

发酵过程机理复杂、影响因素众多。菌种的生理生化特性及发酵的工艺确定之后 ,适宜的培养基配方成了发酵水平、原料成本高低的决定因素。为了优化培养基配方 ,采用遗传算法是一种行之有效的方法。遗传算法 (ＧＡ)是基于达尔文进化论和孟德尔遗传学说来实现随机、自适应、并行性全局搜索的一种无须数学模型的优化算法。与其它搜索方法相比 ,ＧＡ的优越性主要有 :(1)在搜索过程中ＧＡ不易陷入局部最优 ,即使所定义的目标函数非连续、不规则或伴有噪声 ,它也能以很大的概率找到全局最优解 ;(2 )由于ＧＡ固有的并行性 ,使得它非常适合于大规模并…

The Medium Optimization of Xylitol Fermentation Based on Neural Networks and Genetic Algorithms

Using genetic algorithms(GA)for medium optimization of xylitol fermentation, and coupling neural networks model for predicting xylitol concentration is introduced. The medium compose determinated by GA is as input data of the neural networks, while the output data predicted by neural networks is as suitable value of GA for predicting. The optimum medium is further validated by experimentation. The good result, which save the experimental workload and charge, enhance the level of xylitol fermentation as well as reduced the medium consume is obtained.