Nonlinear Adaptive Filtering with MEE, MCC, and Applications

2010 ◽  
pp. 181-218
Author(s):  
Deniz Erdogmus ◽  
Rodney Morejon ◽  
Weifeng Liu
Keyword(s):  
Adaptive Filtering ◽  
Nonlinear Adaptive Filtering

PARALLEL, LOCALLY CONNECTED ALGORITHMS FOR NONLINEAR ADAPTIVE FILTERING

1993 ◽  
Vol 04 (01) ◽  
pp. 85-98 ◽  
Author(s):  
HASSAN M. AHMED ◽  
FAWAD RAUF
Keyword(s):  
Adaptive Filtering ◽  
Layered Structure ◽  
Adaptive Filters ◽  
Computationally Efficient ◽  
Local Connectivity ◽  
Linear Filters ◽  
New Approach ◽  
Vlsi Layout ◽  
State Dependent ◽  
Nonlinear Adaptive Filtering

A new adaptive modular realization for nonlinear filters is presented whereby construction is both computationally efficient and readily implemented. The proposed layered structure consists of locally connected, locally adapted linear filters. Modularity and local connectivity make efficient VLSI layout easy and amenable to automation. The layered structure is based on "state dependent embedding", a new approach to the design of series based nonlinear adaptive filters.

scholarly journals Adaptive Inverse Control Based on Nonlinear Adaptive Filtering

1998 ◽  
Vol 31 (4) ◽  
pp. 211-216 ◽  
Author(s):  
Bernard Widrow ◽  
Gregory Plet ◽  
Edson Ferreira ◽  
Marcelo Lamego
Keyword(s):  
Adaptive Filtering ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
Nonlinear Adaptive Filtering

Nonlinear adaptive filtering using annealed neural networks

2002 ◽  
Author(s):  
R.A. Nobakht ◽  
S.H. Ardalan ◽  
D.E. van den Bout ◽  
G.L. Bilbro
Keyword(s):  
Neural Networks ◽  
Adaptive Filtering ◽  
Nonlinear Adaptive Filtering

scholarly journals Efficient implementation of the third order RLS adaptive Volterra filter

2006 ◽  
Vol 19 (1) ◽  
pp. 133-141 ◽  
Author(s):  
Georgeta Budura ◽  
C. Botoca
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Adaptive Filtering ◽  
Adaptive Filter ◽  
Nonlinear System Identification ◽  
Third Order ◽  
Volterra Filter ◽  
The Third ◽  
Nonlinear Adaptive Filtering ◽  
Made In

Nonlinear adaptive filtering techniques are widely used for the nonlinearities identification in many applications. This paper proposes a new implementation of the third order RLS Volterra filter based on the decomposition of the input vector. Its performances are evaluated in a typical nonlinear system identification application. Different degrees of nonlinearity for the nonlinear system are considered. Comparations, based on the adaptive filter error, are made in all cases with a linear identifier. The experimental results show that the proposed nonlinear identifier has better performances than the linear one.

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