A. Ebrahimzadeh

Babol Noshirvani University of Technology, Barfrush, Māzandarān, Iran

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Publications (3)0.19 Total impact

  • S. Mavaddaty, A. Ebrahimzadeh
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    ABSTRACT: This paper proposes a novel method for blindly separating unobservable independent component signals based on the use of a bee colony optimization algorithm (BCO). It is intended for its application to the problem of blind source separation (BSS) on linear instantaneous mixtures. In this work, results obtained by BCO algorithm for solving BSS problem based on a set of cost functions are compared. These cost functions based on the fusion of two important paradigms, higher order statistics and information theory are established to measure the statistical dependence of the outputs of the demixing system. This paper demonstrates the possible benefits offered by BCO in combination with BSS, such as robustness against local minima and a high degree of flexibility in the evaluation function. Results show that the performance of the BCO is better than or similar to other evolutionary algorithms such as particle swarm optimization (PSO) with applying mutual information in combination with kurtosis on its own cost function.
    Electrical Engineering (ICEE), 2012 20th Iranian Conference on; 01/2012
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    S. Mavaddaty, A. Ebrahimzadeh
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    ABSTRACT: Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evaluate and compare the performance of these methods, we have focused on separation of noisy and noiseless sources. Simulations results demonstrate that the proposed method for employing fitness function has rapid convergence, simplicity and a more favorable signal to noise ratio for separation tasks based on particle swarm optimization and continuous genetic algorithm than binary genetic algorithm. Also, particle swarm optimization enjoys shorter computation time than the other two algorithms for solving these optimization problems for multiple sources.
    Radioelectronics and Communications Systems 01/2011; 54(6):315-324. · 0.19 Impact Factor
  • Samira Mavaddaty, Ataollah Ebrahimzadeh
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    ABSTRACT: Blind source separation is an important issue for signals processing. In this paper, a blind source separation based on continuous and binary genetic algorithm is proposed. The proposed method includes several main steps of preprocessing which are centering, whitening and orthogonalization. The separate matrix is updated by high order statistics of kurtosis. Most of papers have been focused on three sources. But, in this paper the blind source separation for more than three sources is investigated. It is shown that continuous genetic algorithm considerably would be better to get result than binary genetic algorithm for solving the blind source separation problem for different number of sources with high accuracy, fast convergence performance and suitable SNR. In result enhanced separation of mixed signals plus noise or interference has obtained.
    Advances in Computing, Control, and Telecommunication Technologies, International Conference on. 01/2009;

Publication Stats

0.19 Total Impact Points

Institutions

  • 2011
    • Babol Noshirvani University of Technology
      Barfrush, Māzandarān, Iran