DocumentCode
2751050
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
Volume
1
fYear
2000
fDate
2000
Firstpage
18
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;
fLanguage
English
Publisher
ieee
Conference_Titel
TENCON 2000. Proceedings
Conference_Location
Kuala Lumpur
Print_ISBN
0-7803-6355-8
Type
conf
DOI
10.1109/TENCON.2000.893532
Filename
893532
Link To Document