GA-trained GRNN for Intelligent Ultra Fast Charger for Ni-Cd Batteries

This paper presents an intelligent genetic algorithm (GA) technique for training of a neural network controller to achieve a compact network and to decrease battery charging time. An ultra fast charging device for Nickel-Cadmium (Ni-Cd) batteries is designed through the generalized regression neural network (GRNN) and implemented with the MATLAB/SIMULINK for testing and operating on real system. The input-output data for training neural networks were collected from GA. The suitable data were selected to establish GRNN comprising only 13 processing elements. Each node of RBFs is an extendable support function that overcomes the drawback in the existing compact support radial basis functions (CSRBF). Experiments with real time implementation clearly show that the proposed technique not only requires less neural processing units but also yields less MSE than RBF technique.