Title :
Design of ANN (artificial neural networks)-fast backpropagation algorithm gain scheduling controller of active filtering
Author :
Gulez, Kayhan ; Watanabe, Hiroshi ; Harashima, Fumio
Author_Institution :
Dept. of Electron. Syst. Eng., Tokyo Metropolitan Inst. of Technol., Japan
Abstract :
The application of ANN (artificial neural networks) to active circuitry to increase the performance per size, prevent dependency on some parameters of electromagnetic interference (EMI) filter and determine the circuit gain directly are considered. The major problems are power line frequency rejection and the compensation of the feedback loop, which is influenced by the wide-ranging utility impedance. While analysis and simulations show, in the literature, that these problems prevent the practical application of active filtering to power supplies especially at less than 100 kHz, the approximation easily demonstrates a good promise to ensure the design of the architecture of a gain scheduling controller by using ANN for active filtering
Keywords :
active filters; backpropagation; circuit CAD; electromagnetic interference; gain control; network synthesis; neural nets; EMI filter; active circuitry; active filtering; artificial neural networks; circuit gain; electromagnetic interference; fast backpropagation algorithm; feedback loop compensation; gain scheduling controller; power line frequency rejection; power supplies; simulations; utility impedance; Active filters; Analytical models; Artificial neural networks; Backpropagation; Circuits; Electromagnetic interference; Feedback loop; Frequency; Impedance; Performance gain;
Conference_Titel :
TENCON 2000. Proceedings
Conference_Location :
Kuala Lumpur
Print_ISBN :
0-7803-6355-8
DOI :
10.1109/TENCON.2000.893532