Adaptive IIR phase equalizers based on stochastic search algorithms

Pai, Suresh P.; Jenkins, W.K.; Krusienski, Dean J.

doi:10.1109/acssc.2003.1291992

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…This procedure does not result in a closed form expression for the gradient but it does produce a recursive relation by which the gradients can be generated using equation (6). It is well known that the use of the output error in the formulation of the cost function prevents bias in the solution due to noise in the desired signal.…”

Section: (6b)mentioning

confidence: 98%

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Design and performance of adaptive systems based on structured stochastic optimization strategies

Krusienski

Jenkins

2005

IEEE Circuits Syst. Mag.

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The theory and design of linear adaptive filters based on FIR filter structures is well developed and widely applied in practice. However, the same is not true for more general classes of adaptive systems such as linear infinite impulse response adaptive filters (IIR) and nonlinear adaptive systems. This situation results because both linear IIR structures and nonlinear structures tend to produce multi-modal error surfaces for which stochastic gradient optimization strategies may fail to reach the global minimum. After briefly discussing the state of the art in linear adaptive filtering, the attention of this paper is turned to IIR and nonlinear adaptive systems for potential use in echo cancellation, channel equalization, acoustic channel modeling, nonlinear prediction, and nonlinear system identification . Structured stochastic optimization algorithms that are effective on multimodal error surfaces are then introduced, with particular attention to the Particle Swarm Optimization (PSO) technique. The PSO algorithm is demonstrated on some representative IIR and nonlinear filter structures, and both performance and computational complexity are analyzed for these types of nonlinear systems. Abstract WARREN PHOTOGRAPHIC

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Section: (6b)mentioning

confidence: 98%

“…There are many examples in the literature for which IIR MSE surfaces contain local minima, in addition to the global minimum. In these cases gradient search techniques may become entrapped in local minimum, resulting in poor performance due to improper convergence of the adaptive filter [3]- [6].…”

Section: (6b)mentioning

confidence: 99%

Design and performance of adaptive systems based on structured stochastic optimization strategies

Krusienski

Jenkins

2005

IEEE Circuits Syst. Mag.

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“…In [3][4][5] GA and input-output data in time domain are utilized to estimate the parameters of an IIR model in order to use in system identification where fitness function based on mean square error (MSE) between the unknown plant and the estimated model is used. On the other hand GA is extensively employed in adaptive signal processing applications.…”

Section: Introductionmentioning

confidence: 99%

IIR filter design using time and frequency responses by genetic algorithm for system identification

Mostajabi

Poshtan

2011

2011 1st International eConference on Computer and Knowledge Engineering (ICCKE)

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The design of adaptive infinite-impulse-response (IIR) filters has attracted increasing attention in recent years that is a beneficial subject in signal processing, control and system identification. In this paper the Genetic Algorithm is used to estimate parameters of high order IIR filters and to show more GA capability for this issue, time -frequency fitness function is employed. In simulations, the results of implementing GA with three different types of fitness functions are compared. These results show that the fitness function of time -frequency increases rapidly convergence of genetic algorithm, as it could be estimated an appropriate model in acceptable time.

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Constrained random search for IIR adaptive and non-linear filters

Pal

Jenkins

2005

48th Midwest Symposium on Circuits and Systems, 2005.

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Adaptive IIR phase equalizers based on stochastic search algorithms

Cited by 4 publications

References 2 publications

Design and performance of adaptive systems based on structured stochastic optimization strategies

Design and performance of adaptive systems based on structured stochastic optimization strategies

IIR filter design using time and frequency responses by genetic algorithm for system identification

Constrained random search for IIR adaptive and non-linear filters

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