生化过程的神经网络组合模型

用神经网络描述未知的反应动力学参数，结合反应器物料平衡方程，提出了生化过程的神经网络组合模型。并提出了特别适合微生物发酵过程的Ｍｏｎｏｄ饱和型和基质抑制型的神经元传递函数。在Ｈｅｂｂ学习的基础上，引入教师指导信号，提出了神经网络误差一次反向传播的快速学习算法。将此组合模型用于某流加发酵过程状态变量和动力学参数的在线估计，仿真研究获得了满意的结果。组合模型具有训练速度快、预测精度高等优点，为动力学结

Composite Model of Neutral Networks for Biotechnological Processes

Composite model for bioprocesses is presented by combining mass balance cquations of bioreactors and with neural networks which serve as estimators of unmeasutedprocess kinetic parameters. The novel neuron transfer functions of Monod saturation andsubstiate inhibition form are developed. They are very useful in modeling fed-batch cellgrowth and other bioprocesses. Based on Hebbian learning rule, a fast learning algorithm(FLA ) with error back-propagation at one time is proposed for the training of feedforward neural networks by introducing supervisory learning signal. Composit model is applied for theprediction and estimation of state variables , specific cell growth rate and specific substrateconsumption rate. Simulation results show that composite model is able to interpolate and extrapolate much more accurately. It is easier to analyze and interpret , requires fewer trainingexamples and has faster convergence speed ,stronger noise immunity and robustness.