TY - GEN
T1 - A continuous time structure for filtering and prediction using hopfield neural networks
AU - Perez-Meana, Hector
AU - Nakano-Miyatake, Mariko
PY - 1997
Y1 - 1997
N2 - Transversal FIR adaptive filters have been widely used in echo and noise cancelers systems, in equalization of communication channels, in speech coders and predictive deconvolution systems in seismic exploration etc., almost all of these systems implemented in a digital way. This is because with the advance of digital technology it became possible to implement sophisticated and efficient adaptive filter algorithms. However, even with the great advance of the digital technology, the transversal FIR adaptive filters still present several limitations when required to handle in real time frequencies higher than those in the audio range, or when a relative large number of taps are required. To avoid the limitations of digital FIR adaptive filters, several different structures have been proposed. Among them, the analog filters appears to be e desirable alternative to transversal FIR digital adaptive filters because they have the ability to handle very high frequencies, and their size and power requirements are potentially much smaller than their digital counterparts. This paper propose a continuous time transversal adaptive filter structure whose coefficients are estimated in a continuous time way by using an artificial Hopfield Neural Network. Simulation results using the proposed structure in cancellation, prediction and.
AB - Transversal FIR adaptive filters have been widely used in echo and noise cancelers systems, in equalization of communication channels, in speech coders and predictive deconvolution systems in seismic exploration etc., almost all of these systems implemented in a digital way. This is because with the advance of digital technology it became possible to implement sophisticated and efficient adaptive filter algorithms. However, even with the great advance of the digital technology, the transversal FIR adaptive filters still present several limitations when required to handle in real time frequencies higher than those in the audio range, or when a relative large number of taps are required. To avoid the limitations of digital FIR adaptive filters, several different structures have been proposed. Among them, the analog filters appears to be e desirable alternative to transversal FIR digital adaptive filters because they have the ability to handle very high frequencies, and their size and power requirements are potentially much smaller than their digital counterparts. This paper propose a continuous time transversal adaptive filter structure whose coefficients are estimated in a continuous time way by using an artificial Hopfield Neural Network. Simulation results using the proposed structure in cancellation, prediction and.
UR - http://www.scopus.com/inward/record.url?scp=27844595991&partnerID=8YFLogxK
U2 - 10.1007/bfb0032584
DO - 10.1007/bfb0032584
M3 - Contribución a la conferencia
SN - 3540630473
SN - 9783540630470
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1241
EP - 1250
BT - Biological and Artificial Computation
PB - Springer Verlag
T2 - 4th International Work-Conference on Artificial and Natural Neural Networks, IWANN 1997
Y2 - 4 June 1997 through 6 June 1997
ER -