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Then, to test the hierarchical clustering-based method and compare with the obtained results by two other methods, a comparative study using experimental analysis is designed.
Numerical results illustrate the method, and compare with the previous algorithm.
Then, we propose an evolutionary method and compare its performance with the exact method.
We assess several variations on a prediction method, and compare them to simple sequence comparisons.
Some numerical simulation results verify the effectiveness of the proposed method and compare the energy efficiency.
In addition, a numerical example is used to demonstrate the proposed method and compare with the vector autoregression (VAR) model.
In this paper, we introduce a new kernel-based nonlinear ICA method and compare it to standard BSS techniques.
We also investigate the stability of the method and compare results obtained from simulated annealing (SA) and genetic algorithms (GA).
Through hardware experiments, we verify the performance of the proposed method and compare it with existing methods.
Finally, we discuss some computational issues related to the proposed method and compare it with state-of-the-art techniques.
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Numerical simulation is used to present the main characteristics of the method and compare its performance against other techniques currently used in experimental modal analysis.
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