Title :
A new family of concurrent algorithms for adaptive Volterra and linear filters
Author :
Chaturvedi, Ajit Kumar ; Sharma, Govind
Author_Institution :
Dept. of Electr. Eng., Indian Inst. of Technol., Kanpur, India
fDate :
9/1/1999 12:00:00 AM
Abstract :
A novel idea for introducing concurrency in least squares (LS) adaptive algorithms by sacrificing optimality has been proposed. The resultant class of algorithms provides schemes to fill the wide gap in the convergence rates of LS and stochastic gradient (SG) algorithms. It will be particularly useful in the real time implementations of large-order linear and Volterra filters for which both the LS and SG algorithms are unsuited
Keywords :
adaptive filters; adaptive signal processing; convergence of numerical methods; filtering theory; gradient methods; least squares approximations; stochastic systems; LS adaptive algorithms; adaptive Volterra filters; adaptive linear filters; concurrent algorithms; convergence rates; large-order filters; least squares adaptive algorithms; parallel recursive least squares algorithm; real time implementations; stochastic gradient algorithms; Adaptive algorithm; Adaptive filters; Algorithm design and analysis; Concurrent computing; Convergence; Equations; Least mean squares methods; Least squares methods; Nonlinear filters; Signal processing algorithms;
Journal_Title :
Signal Processing, IEEE Transactions on
