Design of self-learning fuzzy neural controller for fermentation process

In fermentation process, when routine control algorithm has been used, the effect of control is bad. Even it is difficult to realize a stable control. Advance control algorithm usually needs much more prior knowledge and depends on the accuracy model of process. However, fuzzy logic control technology is applied to control the plants having fuzzy, uncertainty, high-order, heavy lag without accurate mathematics model. The neural network has the advantage of self-learning, memory ability, fault-tolerant and parallel processing etc. The count propagation network (CPN) was taken as framework, combining an improved fuzzy control algorithm, to realize the fuzzy-neural control of fermentation process. The method has the ability of self-organizing and self-learning the control knowledge which is needed for fermentation process. The rule-base initially is empty, and is self-constructed gradually, to meet the performance index. Simulation results prove that the method can realize the ability of self-learning.