[Objective]To provide a reference for dynamic .regulation of the fermentation process. [Method] Using conditions for tylosin fermentation as feature vectors, final yield during the fermentation process was predicted u...[Objective]To provide a reference for dynamic .regulation of the fermentation process. [Method] Using conditions for tylosin fermentation as feature vectors, final yield during the fermentation process was predicted using the Bayesian method. [ Rgesultl The Bayesian network which learned data of tylosin fermentation could better guide conditions of the fermentation process, and better prediction results were achieved. The yield of fermentation products could be effectively predicted using the obtained Bayesian network and could well guide the fermentation process. E Conclusion] Bayesian network has good predictive performance for microbial fermentation and has good practical yield and application prospects.展开更多
基金funded by the National High Technology Research and Development Program of China (2006AA10A208-2-3)
文摘[Objective]To provide a reference for dynamic .regulation of the fermentation process. [Method] Using conditions for tylosin fermentation as feature vectors, final yield during the fermentation process was predicted using the Bayesian method. [ Rgesultl The Bayesian network which learned data of tylosin fermentation could better guide conditions of the fermentation process, and better prediction results were achieved. The yield of fermentation products could be effectively predicted using the obtained Bayesian network and could well guide the fermentation process. E Conclusion] Bayesian network has good predictive performance for microbial fermentation and has good practical yield and application prospects.
