Title :
Gain estimation for an AC power line data network transmitter using a self-structured neural network and genetic algorithm
Author :
Lam, H.K. ; Ling, S.H. ; Leung, F.H.F. ; Tam, P.K.S. ; Lee, Y.S.
Author_Institution :
Dept. of Electron. & Inf. Eng., Hong Kong Polytech. Univ., Kowloon, China
Abstract :
This paper presents the estimation of the transmission gain for the AC power line data network in an intelligent home. The estimated gain ensures the transmission reliability and efficiency. A neural network with link switches is proposed to perform the estimation. Genetic algorithm with arithmetic crossover and nonuniform mutation is employed to tune the parameters and the structure of the proposed neural network. An application example will be given.
Keywords :
building wiring; carrier transmission on power lines; genetic algorithms; home automation; neural nets; power system analysis computing; power system parameter estimation; power transmission lines; AC power line data network transmitter; arithmetic crossover; data network; gain estimation; genetic algorithm; intelligent home; link switches; neural network; nonuniform mutation; parameters tuning; reliability; self-structured neural network; Arithmetic; Communication system control; Electrical products; Genetic algorithms; Genetic mutations; Home appliances; Intelligent networks; Neural networks; Neurotransmitters; Switches;
Conference_Titel :
IECON 02 [Industrial Electronics Society, IEEE 2002 28th Annual Conference of the]
Print_ISBN :
0-7803-7474-6
DOI :
10.1109/IECON.2002.1185266
