Recurrent-neural-network-based implementation of a programmablecascaded low-pass filter used in stator flux synthesis ofvector-controlled induction motor drive
Da Silva, L.E.B.
Bose, B.K.
Pinto, J.O.P.
Dept. of Electr. Eng., Tennessee Univ., Knoxville, TN;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Jun 1999
Volume: 46,
Issue: 3
On page(s): 662-665
ISSN: 0278-0046
References Cited: 5
CODEN: ITIED6
INSPEC Accession Number: 6277342
Digital Object Identifier: 10.1109/41.767076
Current Version Published: 2002-08-06
Abstract
The concept of programmable cascaded low-pass filter for stator
flux vector synthesis by ideal integration of stator voltages at any
frequency was introduced by Bose and Patel. A new form of implementation
of this filter is proposed that uses a combination of recurrent neural
network trained by Kalman filter and a polynomial neural network. The
proposed structure is simple, permits faster implementation by digital
signal processor, and gives improved performance
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
