scholarly journals Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7732
Author(s):  
Azam Khalili ◽  
Vahid Vahidpour ◽  
Amir Rastegarnia ◽  
Ali Farzamnia ◽  
Kenneth Teo Tze Kin ◽  
...  
Keyword(s):  
Convergence Rate ◽  
Data Exchange ◽  
Learning Curves ◽  
Coordinate Descent ◽  
Mean Square ◽  
Least Mean Square ◽  
Local Estimate ◽  
Error Performance ◽  
Multi Agent ◽  
Agent Networks

The incremental least-mean-square (ILMS) algorithm is a useful method to perform distributed adaptation and learning in Hamiltonian networks. To implement the ILMS algorithm, each node needs to receive the local estimate of the previous node on the cycle path to update its own local estimate. However, in some practical situations, perfect data exchange may not be possible among the nodes. In this paper, we develop a new version of ILMS algorithm, wherein in its adaptation step, only a random subset of the coordinates of update vector is available. We draw a comparison between the proposed coordinate-descent incremental LMS (CD-ILMS) algorithm and the ILMS algorithm in terms of convergence rate and computational complexity. Employing the energy conservation relation approach, we derive closed-form expressions to describe the learning curves in terms of excess mean-square-error (EMSE) and mean-square deviation (MSD). We show that, the CD-ILMS algorithm has the same steady-state error performance compared with the ILMS algorithm. However, the CD-ILMS algorithm has a faster convergence rate. Numerical examples are given to verify the efficiency of the CD-ILMS algorithm and the accuracy of theoretical analysis.

scholarly journals Optimal Neuro-Fuzzy Equalizers for Detecting Nonlinear Distortion Channels of the Perpendicular Magnetic Recording System

2021 ◽  
Vol 19 (2) ◽  
pp. 190-199
Author(s):  
Rati Wongsathan ◽  
Pornchai Supnithi
Keyword(s):  
Data Storage ◽  
Magnetic Recording ◽  
Back Propagation ◽  
Mean Square ◽  
Least Mean Square ◽  
Back Propagation Algorithm ◽  
Error Performance ◽  
Detector Performance ◽  
Neuro Fuzzy ◽  
Nonlinear Distortions

Nonlinear distortions caused by partial erasure and nonlinear transition shifts interacting with inter-symbol interference, are a major hindrance to data storage systems, since they degrade detector performance. This work aims to design and optimize the neuro-fuzzy equalizer (NFE) using the multi-objective genetic algorithm (MOGA) to detect nonlinear high-density magnetic recording (MR) channels. Through the GA-assisted back-propagation algorithm and least mean square optimization, the complexity in terms of decision rules is reduced by 25% and significantly provides 65% lower signal processing computation. When applied to the perpendicular (MR) system, the proposed NFE outperforms existing equalizers such as the neural network-based equalizer, fuzzy logic equalizer, and conventional NFE for the Volterra and jitter media noise channels using 1–3 dB and 1.5–3.5 dB signal-to-noise ratio gains at the bit-error-rate of 10-4, respectively. Furthermore, compared to the other models, the NFE provides a more effective output mean square error performance for retrieving the original bit data.

scholarly journals Accurate Parameter Estimation for Unbalanced Three-Phase System

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yuan Chen ◽  
Hing Cheung So
Keyword(s):  
Parameter Estimation ◽  
Frequency Estimation ◽  
Nonlinear Least Squares ◽  
Phase System ◽  
Mean Square ◽  
Least Mean Square ◽  
Error Performance ◽  
Control Console ◽  
Three Phase ◽  
And Control

Smart grid is an intelligent power generation and control console in modern electricity networks, where the unbalanced three-phase power system is the commonly used model. Here, parameter estimation for this system is addressed. After converting the three-phase waveforms into a pair of orthogonal signals via theαβ-transformation, the nonlinear least squares (NLS) estimator is developed for accurately finding the frequency, phase, and voltage parameters. The estimator is realized by the Newton-Raphson scheme, whose global convergence is studied in this paper. Computer simulations show that the mean square error performance of NLS method can attain the Cramér-Rao lower bound. Moreover, our proposal provides more accurate frequency estimation when compared with the complex least mean square (CLMS) and augmented CLMS.

Smart VRP-NLMS algorithm for estimation of power system frequency

2019 ◽  
Vol 38 (1) ◽  
pp. 362-381
Author(s):  
Seyed Reza Aali ◽  
Mohammad Reza Besmi ◽  
Mohammad Hosein Kazemi
Keyword(s):  
Convergence Rate ◽  
Frequency Estimation ◽  
Lms Algorithm ◽  
Positive Sequence ◽  
Mean Square ◽  
Least Mean Square ◽  
Size Parameter ◽  
Step Size ◽  
Content Type ◽  
Nlms Algorithm

Purpose The purpose of this paper is to study variation regularization with a positive sequence extraction-normalized least mean square (VRP-NLMS) algorithm for frequency estimation in a three-phase electrical distribution system. A simulation test is provided to validate the performance and convergence rate of the proposed estimation algorithm. Design/methodology/approach Least mean square (LMS) algorithms for frequency estimation encounter problems when voltage contains unbalance, sags and harmonic distortion. The convergence rate of the LMS algorithm is sensitive to the adjustment of the step-size parameter used in the update equation. This paper proposes VRP-NLMS algorithm for frequency estimation in a power system. Regularization parameter is variable in the NLMS algorithm to adjust step-size parameter. Delayed signal cancellation (DSC) operator suppresses harmonics and negative sequence component of the voltage vector in a two-phase Î ± β plane. The DSC part is placed in front of the NLMS algorithm as a pre-filter and a positive sequence of the grid voltage is extracted. Findings By adapting of the step-size parameter, speed and accuracy of the LMS algorithm are improved. The DSC operator is augmented to the NLMS algorithm for more improvement of the performance of this adaptive filter. Simulation results validate that the proposed VRP-NLMS algorithm has a less misalignment of performance with more convergence rate. Originality/value This paper is a theoretical support to simulated system performance.

Adaptive filter–based power curve modeling to estimate wind turbine power output

2019 ◽  
pp. 0309524X1986842
Author(s):  
Bharti Dongre ◽  
Rajesh Kumar Pateriya
Keyword(s):  
Wind Turbine ◽  
Adaptive Filter ◽  
Power Output ◽  
Learning Curves ◽  
Least Square ◽  
Power Curve ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Power Curves

This article presents a comparative study of adaptive filter–based power curve models to estimate wind turbine power output. In the real world, wind turbines are never subjected to ideal conditions; thus, adaptive filter–based power curves serve best when estimating the power in a time-varying environment. Adaptive filter–based power curve is implemented using various algorithms like least mean square, kernel least mean square, recursive least square, and kernel recursive least square algorithms. All models have been developed on National Renewable Energy Laboratory datasets. The performance of various models has been compared on the basis of parameters like mean absolute error, root mean square error, and R-squared score. In addition to this, the learning curves of each method have been obtained to show the performance variation over time.

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