A new modified particle swarm optimization algorithm for adaptive equalization

Al‐Awami, Ali T.; Zerguine, Azzedine; Cheded, Lahouari; Zidouri, Abdelmalek; Saif, Waleed A.

doi:10.1016/j.dsp.2010.05.001

Cited by 64 publications

(42 citation statements)

References 9 publications

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“…10(a). The PSO-ANFIS equalizer was also compared to the MLSE, ANFIS, Functional Link Artificial Neural Network (FLANN) [13], PSO-Variable Constriction Factor (PSO-VCF) [14], Artificial Neural Network-PSO (ANN-PSO) [16] and Recursive Least Squares (RLS) [23] equalizers for Bit Error Rate (BER)-Signal-to-Noise Ratio (SNR) performance in Fig. 11.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Update the velocity and the position vectors corresponding to Eqs. (14) and (15) for every particle. 7. g = g + 1.…”

Section: Stepwise Summarization Of the Proposed Methodologymentioning

confidence: 99%

“…The Particle Swarm Optimization (PSO) was used to train the functional link ANN to implement an adaptive channel equalizer in [13], but this equalizer does not have a Neuro Fuzzy structure with effective clustering methods. In [14], a method of training the functional link ANN (FLANN) using PSO algorithm adopted with LMS is investigated, however its performance was not satisfactory in terms of BER. PSO was also employed in Decision Feedback Equalizer (DFE) in [15], but DFE is well-known with outperforming in linear equalization.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

PSO tuned ANFIS equalizer based on fuzzy C-means clustering algorithm

Çavdar

2016

AEU - International Journal of Electronics and Communications

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Section: Simulation Resultsmentioning

confidence: 99%

“…Update the velocity and the position vectors corresponding to Eqs. (14) and (15) for every particle. 7. g = g + 1.…”

Section: Stepwise Summarization Of the Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

PSO tuned ANFIS equalizer based on fuzzy C-means clustering algorithm

Çavdar

2016

AEU - International Journal of Electronics and Communications

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“…The PSO has been recently applied in several embedded optimization problems, such as adaptive equalization [7], road sign detection [8], facial recognition [9], robot planning [10], robot learning [11], among others. Most of the previous works implements the PSO using single or multi processors [12] architectures.…”

Section: A the Standard Pso Operationmentioning

confidence: 99%

A flexible implementation of the PSO algorithm for fine- and coarse-grained reconfigurable embedded systems

Rueckauer

Muñoz

Stripf

et al. 2013

2013 International Conference on Reconfigurable Computing and FPGAs (ReConFig)

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The large execution times demanded for solving complex optimization problems in embedded systems is one of the main challenges in the field of engineering optimization. One solution is the acceleration by a specialized hardware implementation. However, this is coming along with a loss of flexibility especially for the realization of the application-specific fitness function. In this paper we present novel solutions for the flexible implementation of the Particle Swarm Optimization (PSO) algorithm by targeting the coarse-gained reconfigurable Kahrisma architecture. Effectiveness of the proposed solutions was demonstrated for benchmark test problems by numerical simulations achieved by Kahrisma and the MicroBlaze softcore processor mapped on fine-grained reconfigurable technology using the Open Virtual Platform (OVP) simulator as well as an FPGA implementation. Convergence results demonstrate that the proposed solutions achieve the optimal points for different scenarios. Finally, execution time results demonstrate that the Kahrisma implementation with 4-issue width provides the required flexibility to design high performance embedded optimization systems.

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“…Fast convergence rate of PSO has made PSO algorithm be used in time limited applications and considered as a potential tool for optimization problems for complex engineering applications [31], [32]. Another advantage of PSO is that it is not sensitive to parameter selection and does not involve with any differential equation [23].…”

Section: The Proposed Mf-dfe Blind Equalizermentioning

confidence: 99%