Study on self- adapting control based on immune neural network for a car-engine

Because the parameters of a car-engine are of the discreteness, nonlinear and uncertain characteristics, it is hard to control accurately the excess air coefficient and the spark advance angle in dynamic procedure. A self-adapting control based on immune neural network was proposed. It absorbs well the advantages both off-line optimization of the fuzzy control parameters on genetic arithmetic and on-line regulation on an immune neural network. Self-adaptive immune regulation based on BP neural network was probed on the basis of the fuzzy control parameterspsila optimization. The experiment results demonstrate that the dynamic performances, economic performances and self- adaptation are all improved obviously.