Estimation of Some Crucial Variables in Erythromycin Fermentation Process Based on ANN Left-Inversion
For the on-line estimation of some directly immeasurable crucial variables in erythromycin fermentation process, this paper presents an Artificial Neural Network (ANN) left-inversion based on the “assumed inherent sensor” and its left-inversion concepts. The ANN left-inversion is composed of two relatively independent parts ( a static ANN used to approximate the complex nonlinear function and several differentiators used to represent its dynamic behaviors, so that the ANN left-inversion is a special kind of dynamic ANN in essence. Different from common dynamic ANNs, such a separate structure makes the ANN left-inversion easier to use, hence facilitating its application. The ANN left-inversion has been used to estimate such immeasurable variables as mycelia concentration, sugar concentration and chemical potency in erythromycin fermentation process. The experimental results show its validity.
KeywordsArtificial Neural Network Back Propagation Neural Network Hopfield Neural Network Crucial Variable Penicillin Production
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