DocumentCode
290444
Title
Adaptation of memory depth in the gamma filter
Author
Kuo, Jyh-Ming ; Celebi, Samel
Author_Institution
Comput. Neuroeng. Lab., Florida Univ., Gainesville, FL, USA
Volume
iv
fYear
1994
fDate
19-22 Apr 1994
Abstract
Gamma filters are a special class of generalized feedforward filters where feedbacks are allowed only locally. The authors present the conditions for the selection of optimal parameters which are the weights and the memory depth of the filter. The conditions for these two set of parameters are decoupled from each other. This allows a matched filter implementation which gives an estimate of the memory depth
Keywords
IIR filters; digital filters; feedforward; matched filters; optimisation; gamma filter; generalized feedforward filters; matched filter implementation; memory depth; optimal parameters; Differential equations; IIR filters; Kernel; Linear systems; Matched filters; Neural engineering; Neurofeedback; Nonlinear filters; Signal processing; Transfer functions;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Conference_Location
Adelaide, SA
ISSN
1520-6149
Print_ISBN
0-7803-1775-0
Type
conf
DOI
10.1109/ICASSP.1994.389803
Filename
389803
Link To Document