NEUROFUZZY CONTROL OF BIOREACTOR SYSTEMS WITH PATTERN-RECOGNITION

It has been recognized that fuzzy control is quite effective in coping with the uncertain dynamics associated with bioprocesses. However, it is, in general, quite time-consuming to determine the IF-THEN type of rules and membership functions in conventional fuzzy control. In the present study, we consider adjusting the membership function on-line with the aid of neural networks, where the role of the neural networks is to recognize the patterns of changes in the DO concentration (oscillatory/ non-oscillatory), and ethanol concentration (increasing/decreasing/nearly constant) in baker's yeast fed-batch cultivation. This neuro-fuzzy control strategy retains the advantages of both neural nets and fuzzy control. The application to baker's yeast fermentation shows the power of this strategy as compared with conventional fuzzy control.