Implementation of GA-trained GRNN for Intelligent Fast Charger for Ni-Cd Batteries

This paper presents the development of an intelligent genetic algorithm (GA) technique for training of a generalized regression neural network (GRNN) controller to achieve a compact network and to decrease battery charging time on a cost-effective RISC microcontroller. The suitable input-output data were selected from GA mechanism to establish GRNN. The computational complexity of GRNN can be reduced replaced by some simple polynomial forms. As a consequence, the fast charging device for nickel-cadmium (Ni-Cd) batteries can be efficiently implemented on a low-cost 16F876A RISC microcontroller. Experimental results are shown to demonstrate superiority of the proposed system