基于神经网络—生长动力学模型对DM423生物量的软测量

Hybrid neural network based on software measurement for DM423 biomass during batch cultivation

以温度、培养时间、溶氧量、pH和葡萄糖浓度为输入变量,生物量浓度为输出变量,研究5-5-1静态多层前馈网络和神经网络—生长动力学两种模型估算的精确度。结果表明:静态多层前馈网络测试样本均方差为1.73×10-3,而神经网络—生长动力学混合模型测试样本均方差为0.25×10-3,其估算精确度优于单独使用静态多层前馈网络对生物量进行估算,动力学模型有较好的泛化能力。

The biomass concentration estimated by the static feedforward multiplayer neural network of 5-5-1topology and hybrid neural network-microbe growth model with five inputs of culture time,temperature,pH and dissolved oxygen and glucose concentration.The result showed that the static feedforward multiplayer neural network mean squared error（MSE）of testing samples 1.73×10^-3.And hybrid neural network-microbe growth model offered a much better generalization accuracy than that of single neural network model,with MSE of testing samples of 0.25×10^-3,it was found that there was some deviation between estimated biomass and actual values while microbe were growing in the stationary phase.