TY - GEN
T1 - A fast orthogonalized FIR adaptive filter structure using recurrent hopfield-like network
AU - Nakano-Miyatake, Mariko
AU - Perez-Meana, Hector
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1999.
PY - 1999
Y1 - 1999
N2 - Transversal FIR adaptive filters with LMS like adaptation algorithms have been widely used in many practical applications because their computational cost is low and the transversal stmctnre is unconditionally stable. However the slow convergence rate of transversal filters with LMS adaptation algorithms may restrict their use in several practical applications. To increase the convergence rates of transversal filters, several algorithms based on the Newton Rapson method, such as the recursive least square algorithm, has been proposed. It provides the fastest convergence rates, although its computational cost is in general high, and its low cost versions, such as the Fast Kahnan algorithm are, in some cases, numerically unstable. On the other hand, in real time signal processing, a significant amount of computational effort can be saved if the input signals are represented in terms of a set of orthogonal signal components. This is because the representation admits processing schemes in which each of these orthogonal signal components are independently processed. This paper proposes a parallel form FIR adaptive filter structure based on a generalized subband decomposition, implemented in either, a digital or analog way, in which the input signal is split into a set of orthogonal signal component. Subsequently, these orthogonal signal components are filtered by a bank of FIR filters whose coefficient vectors are updated with a Gauss-Newton type adaptive algorithm, which is implemented by using modified recurrent Neural Network. Proposed scheme reduces the computational cost avoids numerical stability problems, since there is not any explicit matrix inversion. Results obtained by computer simulations show the desirable features of the proposed structure.
AB - Transversal FIR adaptive filters with LMS like adaptation algorithms have been widely used in many practical applications because their computational cost is low and the transversal stmctnre is unconditionally stable. However the slow convergence rate of transversal filters with LMS adaptation algorithms may restrict their use in several practical applications. To increase the convergence rates of transversal filters, several algorithms based on the Newton Rapson method, such as the recursive least square algorithm, has been proposed. It provides the fastest convergence rates, although its computational cost is in general high, and its low cost versions, such as the Fast Kahnan algorithm are, in some cases, numerically unstable. On the other hand, in real time signal processing, a significant amount of computational effort can be saved if the input signals are represented in terms of a set of orthogonal signal components. This is because the representation admits processing schemes in which each of these orthogonal signal components are independently processed. This paper proposes a parallel form FIR adaptive filter structure based on a generalized subband decomposition, implemented in either, a digital or analog way, in which the input signal is split into a set of orthogonal signal component. Subsequently, these orthogonal signal components are filtered by a bank of FIR filters whose coefficient vectors are updated with a Gauss-Newton type adaptive algorithm, which is implemented by using modified recurrent Neural Network. Proposed scheme reduces the computational cost avoids numerical stability problems, since there is not any explicit matrix inversion. Results obtained by computer simulations show the desirable features of the proposed structure.
UR - http://www.scopus.com/inward/record.url?scp=84957667274&partnerID=8YFLogxK
U2 - 10.1007/BFb0098205
DO - 10.1007/BFb0098205
M3 - Contribución a la conferencia
SN - 3540660690
SN - 9783540660699
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 478
EP - 487
BT - Foundations and Tools for Neural Modeling - International Work-Conference on Artificial and Natural Neural Networks, IWANN 1999, Proceedings
A2 - Mira, José
A2 - Sánchez-Andrés, Juan V.
PB - Springer Verlag
T2 - 5th International Work-Conference on Artificial and Natural Neural Networks, IWANN 1999
Y2 - 2 June 1999 through 4 June 1999
ER -