Browsing by Author "Khalili, Azam"

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A Self-Organizing Diffusion Mobile Adaptive Network for Pursuing a Target
(Scientific & Academic Publishing, 2016) Rastegarnia, Amir; Khalili, Azam; Islam, Md Kafiul
In this paper we focus on designing self-organizing diffusion mobile adaptive networks where the individual agents are allowed to move in pursuit of an target. The well-known Adapt-then-Combine (ATC) algorithm is already available in the literature as a useful distributed diffusion-based adaptive learning network. However, in the ATC diffusion algorithm, fixed step sizes are used in the update equations for velocity vectors and location vectors. When the nodes are too far away from the target, such strategies may require large number of iterations to reach the target. To address this issue, we suggest two modifications on the ATC mobile adaptive network to improve its performance. The proposed modifications include (i) distance-based variable step size adjustment at diffusion algorithms to update velocity vectors and location vectors, (ii) to use a selective cooperation, by choosing the best nodes at every iteration, to reduce the number of communications. The performance of the proposed algorithm is evaluated by simulation tests where the obtained results show the superior performance of the proposed algorithm in comparison with the available ATC mobile adaptive network.
Frequency Estimation of Unbalanced Three-Phase Power Systems Using the Modified Adaptive Filtering
(Scientific & Academic Publishing, 2015-08) Rastegarnia, Amir; Khalili, Azam; Vahidpour, Vahid; Islam, Md Kafiul
In this paper, the problem of frequency estimation using adaptive filters is addressed based on the augmented complex normalized least mean squares (ACNLMS) technique. In other words, motivated from ACLMS technique, a new version called ACNLMS that outperforms the previous techniques is proposed. The proposed method makes use of two weight coefficients, like ACLMS, together with normalized time variant step size, thus its promising in increasing and enhancing both convergence rate and accuracy. Then, using derived weight coefficient, the frequency is estimated at each step. The performance and convergence analysis of the proposed method along with simulation results comparing with two existing techniques, CLMS and ACLMS, are provided in critical cases such as various unbalanced conditions and presence of harmonic distortion. Simulation results indicate that the proposed technique achieves a better performance in terms of convergence rate and frequency estimation accuracy as compared with CLMS and ACLMS techniques. Moreover, ACLMS achieves a smaller error variance than the other mentioned cases.
Steady-State Tracking Analysis of Adaptive Filter With Maximum Correntropy Criterion
(Springer, 2016-08-03) Khalili, Azam; Rastegarnia, Amir; Islam, Md Kafiul; Rezaii, Tohid Yousefi
This letter studies the tracking performance of a stochastic gradient-based adaptive algorithm, namely the maximum correntropy criterion algorithm, where a random walk is used to model the non-stationarity. In our analysis, we use the energy conservation argument to derive expressions for the steady-state excess mean square error (EMSE). We consider two different cases for measurement of noise distribution including the Gaussian noise and general non-Gaussian noise. For the Gaussian case, we derive a fixed-point equation that can be solved numerically to find steady-state EMSE value. For the general non-Gaussian case, we derive an approximate closed-form expression for EMSE. For both cases, unlike the stationary environment, the EMSE curves are not increasing functions of step size parameter. We use this observation to find the optimum step size learning parameter for general non-Gaussian case. The validity of the theoretical results are justified via simulation results.