Applying the proposed modeling scheme to Nosiheptide fermentation process demonstrates that the hybrid modeling has good estimation performance.
将所建立的混合模型应用于诺西肽发酵过程中，结果表明，混合建模方法具有很好的估计性能。
Firstly, based on the staged unstructured model of Nosiheptide fermentation process, secondary variables were selected according to the implicit function existence theorem.
首先以分阶段的诺西肽发酵过程非结构模型为基础，根据隐函数存在定理进行辅助变量的合理选择；
This brings difficulties to control and optimize Nosiheptide fermentation process. To solve this problem, soft-sensor technique was used, and a staged modeling method was proposed.
针对这一问题，利用软测量技术来实现关键生化参数的在线估计，并提出了一种分阶段软测量建模方法。
This brings difficulties to control and optimize Nosiheptide fermentation process. To solve this problem, soft-sensor technique was used, and a staged modeling method was proposed.
针对这一问题，利用软测量技术来实现关键生化参数的在线估计，并提出了一种分阶段软测量建模方法。
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