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On-line estimation of biomass concentration using a neural network and information about metabolic state

机译：使用神经网络和代谢状态信息在线估算生物量浓度

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摘要

This paper deals with the design of a neural network-based biomass concentration estimation system. This system is enhanced by the incorporation of information about the actual metabolism of the microorganism cultivated, which is taken from an on-line knowledge-based system. Two different design approaches have been investigated using the fed-batch cultivation of baker's yeast as the model process. In the first, metabolic state (MS) data were passed as additional input to the neural network; in the second, these data were used to select a neural network suitable for the specific MS. Two neural network types—feed-forward (Levenberg-Marquardt) and cascade correlation—were applied to this system and tested, and the performances of these neural networks were compared.
机译：本文研究了基于神经网络的生物质浓度估算系统的设计。该系统通过并入有关栽培微生物实际代谢的信息而得到增强，该信息取自基于在线知识的系统。使用面包酵母的分批补料培养作为模型过程，研究了两种不同的设计方法。首先，将代谢状态（MS）数据作为附加输入传递到神经网络；第二，这些数据用于选择适合特定MS的神经网络。将两种神经网络类型-前馈（Levenberg-Marquardt）和级联相关性应用于此系统并进行了测试，并比较了这些神经网络的性能。

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