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
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;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-1775-0
DOI :
10.1109/ICASSP.1994.389803
