The genetic search approach. A new learning algorithm for adaptive IIR filtering

Ng, Sin-Chun; Leung, S.H.; Chung, Chia‐Shin; Luk, A.; Lau, Wai-ping

doi:10.1109/79.543974

Cited by 108 publications

(43 citation statements)

References 12 publications

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“…In adaptive filtering, GA operates on a set of filter parameters (the population of chromosomes), in which a fitness values are specified to each individual chromosome. The cost function in adaptive filtering is taken as the Mean Square Error (MSE) which is given by [4] (15) where is the window size over which the errors will be accumulated;…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…An evolutionary algorithm named Genetic Algorithm (GA) is presented for multi-modal error surface searching in IIR adaptive filtering [4]. Nevertheless, the high computational complexity and slow convergence are the main drawbacks of utilizing such an algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Application of genetic algorithm and its variant in system identification are studied in [4], [19] respectively.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of a multi-modal error surface, the LMS algorithm like the vast majority of the learning techniques may drive the filter into a local minimum. Moreover, the initial choice of the filter coefficients and the proper selection of the step size particularly determine the convergence behavior of the LMS algorithm [4].…”

Section: Introductionmentioning

confidence: 99%

“…Adaptive digital filters can be classified into adaptive Finite Impulse Response (FIR) filter, or commonly known as an Adaptive Linear Combiner which is unconditionally stable, and Infinite Impulse Response (IIR) presents a prospective enhancement in the performance and less computation power than corresponding adaptive FIR filter. Common applications of adaptive filters are noise cancelation, inverse modeling, prediction, jammer suppression [2]- [4], and adaptive system identification, which is the main topic of this paper.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Adaptive IIR and FIR Filtering using Evolutionary LMS Algorithm in View of System Identification

Ibraheem¹

2018

IJCA

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Our aim in this paper is to show how simple adaptive IIR filter can be used in system identification. The main objective of our research is to study the LMS algorithm and its improvement by the genetic search approach, namely, LMS-GA, to search the multi-modal error surface of the adaptive IIR filter to avoid local minima and finding the optimal weight vector when only measured or estimated data are available. Convergence analysis of the LMS algorithm in the case of colored input signal, i.e., correlated input signal is demonstrated via the input's power spectral density and the Fourier transform of the autocorrelation matrix of the input signal. Simulations have been carried out on adaptive filtering of IIR filter and tested on white and colored input signals to validate the powerfulness of the genetic-based LMS algorithm.

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Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Application of genetic algorithm and its variant in system identification are studied in [4], [19] respectively.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive IIR and FIR Filtering using Evolutionary LMS Algorithm in View of System Identification

Ibraheem¹

2018

IJCA

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Satellite image segmentation using hybrid variable genetic algorithm

Awad

Chehdi

2009

Int J Imaging Syst Tech

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Image segmentation is an important task in image processing and analysis. Many segmentation methods have been used to segment satellite images. The success of each method depends on the characteristics of the acquired image such as resolution limitations and on the percentage of imperfections in the process of image acquisition due to noise. Many of these methods require a priori knowledge which is difficult to obtain. Some of them are parametric statistical methods that use many parameters which are dependent on image property. In this article, a new unsupervised nonparametric method is developed to segment satellite images into homogeneous regions without any a priori knowledge. The new method is called hybrid variable genetic algorithm (HVGA). The variability is found in the variable number of cluster centers and in the changeable mutation rate. In addition, this new method uses different heuristic processes to increase the efficiency of genetic algorithm in avoiding local optimal solutions. Experiments performed on two different satellite images (Landsat and Spot) proved the high accuracy and efficiency of HVGA compared with another two unsupervised and nonparametric segmentation methods genetic algorithm (GA) and self-organizing map (SOM). The verification of the results included stability and accuracy measurements using an evaluation method implemented from the functional model (FM) and field surveys.

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Untitled

2009

Int J Imaging Syst Tech

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The genetic search approach. A new learning algorithm for adaptive IIR filtering

Cited by 108 publications

References 12 publications

Adaptive IIR and FIR Filtering using Evolutionary LMS Algorithm in View of System Identification

Adaptive IIR and FIR Filtering using Evolutionary LMS Algorithm in View of System Identification

Satellite image segmentation using hybrid variable genetic algorithm

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