Title :
Implementation of GA-trained GRNN for Intelligent Fast Charger for Ni-Cd Batteries
Author :
Petchjatuporn, Panom ; Khaehintung, Noppadol ; Sunat, Khamron ; Sirisuk, Phaophak ; Kiranon, Wiwat
Author_Institution :
Dept. of Control & Instrum. Eng., Mahanakorn Univ. of Technol., Bangkok
Abstract :
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
Keywords :
battery chargers; computational complexity; generalisation (artificial intelligence); genetic algorithms; microcomputer applications; neural nets; polynomials; radial basis function networks; secondary cells; 16F876A RISC microcontroller; GA technique; Ni-Cd; battery charging time; computational complexity; generalized regression neural network controller; intelligent fast charger; intelligent genetic algorithm; nickel-cadmium batteries; polynomial forms; radial basis functions; Batteries; Control systems; Genetic algorithms; Genetic engineering; Intelligent networks; Microcontrollers; Neural networks; Reduced instruction set computing; Telecommunication control; Temperature; RISC microcontroller; fast charging; genetic algorithm; radial basis functions; the generalized regression neural network;
Conference_Titel :
Power Electronics and Motion Control Conference, 2006. IPEMC 2006. CES/IEEE 5th International
Conference_Location :
Shanghai
Print_ISBN :
1-4244-0448-7
DOI :
10.1109/IPEMC.2006.4777963
