IEEE Transactions on Signal Processing, ISSN 1053-587X, 05/2008, Volume 56, Issue 5, pp. 1853 - 1864

This paper studies the statistical behavior of an affine combination of the outputs of two least mean-square (LMS...

Algorithm design and analysis | stochastic algorithms | Adaptive algorithm | convex combination | least mean square (LMS) | Adaptive filters | Stochastic processes | Resonance light scattering | Steady-state | analysis | Least squares approximation | Convergence | Performance analysis | affine combination | Convex combination | Stochastic algorithms | Least mean square (LMS) | Affine combination | Analysis | adaptive filters | ENGINEERING, ELECTRICAL & ELECTRONIC | Signal processing | Evaluation | Usage | Gaussian processes | Least squares | Studies | Engineering Sciences | Signal and Image Processing | Computer Science

Algorithm design and analysis | stochastic algorithms | Adaptive algorithm | convex combination | least mean square (LMS) | Adaptive filters | Stochastic processes | Resonance light scattering | Steady-state | analysis | Least squares approximation | Convergence | Performance analysis | affine combination | Convex combination | Stochastic algorithms | Least mean square (LMS) | Affine combination | Analysis | adaptive filters | ENGINEERING, ELECTRICAL & ELECTRONIC | Signal processing | Evaluation | Usage | Gaussian processes | Least squares | Studies | Engineering Sciences | Signal and Image Processing | Computer Science

Journal Article

IEEE Transactions on Signal Processing, ISSN 1053-587X, 06/2015, Volume 63, Issue 11, pp. 2733 - 2748

The diffusion LMS algorithm has been extensively studied in recent years. This efficient strategy allows to address distributed optimization problems...

Algorithm design and analysis | Context | Least squares approximations | distributed optimization | Estimation | Adaptive clustering | Vectors | multitask learning | diffusion strategy | Signal processing algorithms | Cost function | stochastic performance | collaborative processing | ADAPTIVE NETWORKS | CONSENSUS | LEAST-MEAN SQUARES | ALGORITHMS | SENSOR NETWORKS | STRATEGIES | ENGINEERING, ELECTRICAL & ELECTRONIC | ADAPTATION | SUBGRADIENT METHODS | OPTIMIZATION | Signal processing | Stochastic processes | Research | Computer Science - Systems and Control

Algorithm design and analysis | Context | Least squares approximations | distributed optimization | Estimation | Adaptive clustering | Vectors | multitask learning | diffusion strategy | Signal processing algorithms | Cost function | stochastic performance | collaborative processing | ADAPTIVE NETWORKS | CONSENSUS | LEAST-MEAN SQUARES | ALGORITHMS | SENSOR NETWORKS | STRATEGIES | ENGINEERING, ELECTRICAL & ELECTRONIC | ADAPTATION | SUBGRADIENT METHODS | OPTIMIZATION | Signal processing | Stochastic processes | Research | Computer Science - Systems and Control

Journal Article

IEEE Transactions on Antennas and Propagation, ISSN 0018-926X, 11/2010, Volume 58, Issue 11, pp. 3545 - 3557

A new adaptive algorithm, called least mean square- least mean square (LLMS) algorithm, which employs an array image factor, , sandwiched in between two least mean square (LMS...

Algorithm design and analysis | least mean square-least mean square (LLMS) and least mean square (LMS) algorithms | Array signal processing | Signal processing algorithms | Eigenvalues and eigenfunctions | Rayleigh fading | Arrays | Least squares approximation | Adaptive array beamforming | Convergence | error vector magnitude (EVM) | STABILITY | TELECOMMUNICATIONS | ENGINEERING, ELECTRICAL & ELECTRONIC | Beamforming | Usage | Mobile communication systems | Wireless communication systems | Analysis | Least squares | Design and construction | Antenna arrays | Studies | Computer simulation | Algorithms | Error signals | Least mean squares | Least mean squares algorithm | Adaptive algorithms | Images | Engineering Sciences | Signal and Image Processing | Computer Science

Algorithm design and analysis | least mean square-least mean square (LLMS) and least mean square (LMS) algorithms | Array signal processing | Signal processing algorithms | Eigenvalues and eigenfunctions | Rayleigh fading | Arrays | Least squares approximation | Adaptive array beamforming | Convergence | error vector magnitude (EVM) | STABILITY | TELECOMMUNICATIONS | ENGINEERING, ELECTRICAL & ELECTRONIC | Beamforming | Usage | Mobile communication systems | Wireless communication systems | Analysis | Least squares | Design and construction | Antenna arrays | Studies | Computer simulation | Algorithms | Error signals | Least mean squares | Least mean squares algorithm | Adaptive algorithms | Images | Engineering Sciences | Signal and Image Processing | Computer Science

Journal Article

IEEE Signal Processing Letters, ISSN 1070-9908, 2009, Volume 16, Issue 9, pp. 774 - 777

Journal Article

Signal Processing, ISSN 0165-1684, 12/2015, Volume 117, pp. 192 - 197

The so-called constrained least mean-square algorithm is one of the most commonly used linear-equality-constrained adaptive filtering algorithms...

Linearly-constrained adaptive filtering | Mean-square stability | Performance analysis | Mean-square deviation | Constrained least mean-square | Linearly-constrained adaptive Filtering | ENGINEERING, ELECTRICAL & ELECTRONIC | Energy conservation | Algorithms

Linearly-constrained adaptive filtering | Mean-square stability | Performance analysis | Mean-square deviation | Constrained least mean-square | Linearly-constrained adaptive Filtering | ENGINEERING, ELECTRICAL & ELECTRONIC | Energy conservation | Algorithms

Journal Article

IEEE signal processing letters, ISSN 1558-2361, 2016, Volume 23, Issue 12, pp. 1786 - 1790

Zero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for online sparse system identification...

transient behavior | zero-attracting least-mean square (ZA-LMS) | Mean square error methods | Gaussian distribution | sparse system identification | Random variables | Electronic mail | Performance analysis | Transient analysis | Covariance matrices | MEAN-SQUARE ALGORITHM | ENGINEERING, ELECTRICAL & ELECTRONIC | Signal and Image Processing | Computer Science

transient behavior | zero-attracting least-mean square (ZA-LMS) | Mean square error methods | Gaussian distribution | sparse system identification | Random variables | Electronic mail | Performance analysis | Transient analysis | Covariance matrices | MEAN-SQUARE ALGORITHM | ENGINEERING, ELECTRICAL & ELECTRONIC | Signal and Image Processing | Computer Science

Journal Article

IEEE Transactions on Signal Processing, ISSN 1053-587X, 10/2012, Volume 60, Issue 10, pp. 5107 - 5124

In this work, we analyze the mean-square performance of different strategies for distributed estimation over least-mean-squares (LMS) adaptive networks...

Algorithm design and analysis | distributed estimation | Adaptive systems | centralized estimation | Noise | Estimation | diffusion LMS | Vectors | Least squares approximation | Adaptive networks | diffusion strategy | incremental strategy | Signal processing algorithms | energy conservation | fusion center | CONVERGENCE ANALYSIS | FILTERS | LEAST-MEAN SQUARES | ALGORITHMS | FORMULATION | STRATEGIES | ENGINEERING, ELECTRICAL & ELECTRONIC | LEARNING CHARACTERISTICS | CONVEX COMBINATION | STATIONARY | Wireless sensor networks | Usage | Innovations | Least squares | Mathematical optimization | Simulation methods | Ad hoc networks (Computer networks) | Studies | Networks | Diffusion barriers | Mathematical analysis | Blocking | Strategy | Transaction processing | Diffusion | Optimization

Algorithm design and analysis | distributed estimation | Adaptive systems | centralized estimation | Noise | Estimation | diffusion LMS | Vectors | Least squares approximation | Adaptive networks | diffusion strategy | incremental strategy | Signal processing algorithms | energy conservation | fusion center | CONVERGENCE ANALYSIS | FILTERS | LEAST-MEAN SQUARES | ALGORITHMS | FORMULATION | STRATEGIES | ENGINEERING, ELECTRICAL & ELECTRONIC | LEARNING CHARACTERISTICS | CONVEX COMBINATION | STATIONARY | Wireless sensor networks | Usage | Innovations | Least squares | Mathematical optimization | Simulation methods | Ad hoc networks (Computer networks) | Studies | Networks | Diffusion barriers | Mathematical analysis | Blocking | Strategy | Transaction processing | Diffusion | Optimization

Journal Article

IEEE/ACM Transactions on Networking, ISSN 1063-6692, 02/2016, Volume 24, Issue 1, pp. 3 - 14

We investigate the performance of distributed least-mean square (LMS) algorithms for parameter estimation over sensor networks where the regression data of each node are corrupted by white measurement noise...

Algorithm design and analysis | distributed parameter estimation | Least squares approximations | Noise | Signal processing algorithms | Estimation | Vectors | network optimization | Noise measurement | Bias-compensated least-mean square (LMS) | diffusion adaptation | SYSTEM | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | PARAMETER-ESTIMATION | ALGORITHM | LEAST-MEAN SQUARES | TELECOMMUNICATIONS | FORMULATION | ENGINEERING, ELECTRICAL & ELECTRONIC | ADAPTATION | SUBGRADIENT METHODS | OPTIMIZATION | COMPUTER SCIENCE, THEORY & METHODS | Parameter estimation | Algorithms

Algorithm design and analysis | distributed parameter estimation | Least squares approximations | Noise | Signal processing algorithms | Estimation | Vectors | network optimization | Noise measurement | Bias-compensated least-mean square (LMS) | diffusion adaptation | SYSTEM | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | PARAMETER-ESTIMATION | ALGORITHM | LEAST-MEAN SQUARES | TELECOMMUNICATIONS | FORMULATION | ENGINEERING, ELECTRICAL & ELECTRONIC | ADAPTATION | SUBGRADIENT METHODS | OPTIMIZATION | COMPUTER SCIENCE, THEORY & METHODS | Parameter estimation | Algorithms

Journal Article

IEEE Transactions on Signal Processing, ISSN 1053-587X, 07/2015, Volume 63, Issue 13, pp. 3448 - 3460

... relies on a diffusion-based implementation of different, yet coupled Least Mean Squares (LMS...

Context | Parameter estimation | Least squares approximations | Adaptive distributed networks | diffusion algorithm | node-specific parameter estimation | Estimation | Signal processing algorithms | Europe | Peer-to-peer computing | cooperation | Diffusion algorithm | Node-specific parameter estimation | Cooperation | SYNCHRONIZATION | SIGNAL ESTIMATION | LEAST-MEAN SQUARES | NETWORKS | ALGORITHMS | FORMULATION | STRATEGIES | ENGINEERING, ELECTRICAL & ELECTRONIC | Signal detection (Electronics) | Usage | Research | Analysis | Least squares | Algorithms | Estimating techniques

Context | Parameter estimation | Least squares approximations | Adaptive distributed networks | diffusion algorithm | node-specific parameter estimation | Estimation | Signal processing algorithms | Europe | Peer-to-peer computing | cooperation | Diffusion algorithm | Node-specific parameter estimation | Cooperation | SYNCHRONIZATION | SIGNAL ESTIMATION | LEAST-MEAN SQUARES | NETWORKS | ALGORITHMS | FORMULATION | STRATEGIES | ENGINEERING, ELECTRICAL & ELECTRONIC | Signal detection (Electronics) | Usage | Research | Analysis | Least squares | Algorithms | Estimating techniques

Journal Article

IEEE transactions on signal processing, ISSN 1941-0476, 2009, Volume 57, Issue 4, pp. 1316 - 1327

The quaternion least mean square (QLMS) algorithm is introduced for adaptive filtering of three- and four-dimensional processes, such as those observed in atmospheric modeling (wind, vector fields...

Multidimensional signal processing | Adaptive filters | Sonar | Adaptive signal processing | Least squares approximation | data fusion via vector spaces | wind modeling | Quaternions | Signal processing algorithms | multidimensional adaptive filters | quaternion signal processing | Filtering algorithms | Signal processing | Radar signal processing | Adaptive multistep ahead prediction | Wind modeling | Multidimensional adaptive filters | Data fusion via vector spaces | Quaternion signal processing | RANDOM VECTORS | COMPLEX | PREDICTION | ENGINEERING, ELECTRICAL & ELECTRONIC | Robust statistics | Usage | Research | Algorithms | Least mean squares | Mathematical analysis | Benchmarking | Vectors (mathematics) | Statistics

Multidimensional signal processing | Adaptive filters | Sonar | Adaptive signal processing | Least squares approximation | data fusion via vector spaces | wind modeling | Quaternions | Signal processing algorithms | multidimensional adaptive filters | quaternion signal processing | Filtering algorithms | Signal processing | Radar signal processing | Adaptive multistep ahead prediction | Wind modeling | Multidimensional adaptive filters | Data fusion via vector spaces | Quaternion signal processing | RANDOM VECTORS | COMPLEX | PREDICTION | ENGINEERING, ELECTRICAL & ELECTRONIC | Robust statistics | Usage | Research | Algorithms | Least mean squares | Mathematical analysis | Benchmarking | Vectors (mathematics) | Statistics

Journal Article

Expert Systems With Applications, ISSN 0957-4174, 2010, Volume 37, Issue 12, pp. 8019 - 8026

.... In the proposed technique, the Lagrange multiplier is modified based on Least Mean Square (LMS) algorithm, which in turn modifies the weight vector to reduce the classification error...

Classification of heart sounds | Heart sounds | Phonocardiogram (PCG) | Least Mean Square (LMS) | Murmurs | Support Vector Machine (SVM) | Support vector machine (SVM) | Least mean square (LMS) | SYSTEM | DIAGNOSIS | PHONOCARDIOGRAM | ALGORITHM | ARTIFICIAL NEURAL-NETWORK | HEART-SOUND SEGMENTATION | AUSCULTATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | SUPPORT VECTOR MACHINE | Algorithms | Detectors | Heart | Support vector machines | Least squares method | Mathematical analysis | Classification | Acoustics | Vectors (mathematics) | Recording

Classification of heart sounds | Heart sounds | Phonocardiogram (PCG) | Least Mean Square (LMS) | Murmurs | Support Vector Machine (SVM) | Support vector machine (SVM) | Least mean square (LMS) | SYSTEM | DIAGNOSIS | PHONOCARDIOGRAM | ALGORITHM | ARTIFICIAL NEURAL-NETWORK | HEART-SOUND SEGMENTATION | AUSCULTATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | SUPPORT VECTOR MACHINE | Algorithms | Detectors | Heart | Support vector machines | Least squares method | Mathematical analysis | Classification | Acoustics | Vectors (mathematics) | Recording

Journal Article

Electronics Letters, ISSN 0013-5194, 7/2018, Volume 54, Issue 15, pp. 951 - 953

Network echo path impulse response is single-block-sparse in nature. In order to obtain a single-block-sparse estimate of the unknown echo path, a new least mean squares (LMS...

Signal processing | single-block-sparse estimate | least mean squares methods | adaptive filters | transient response | nonzero block | existing block-sparsity | LMS algorithms | echo suppression | network echo cancellation | mixed $l_{2\comma 1} | echo | path impulse response | unknown echo path | norm | ENGINEERING, ELECTRICAL & ELECTRONIC | mean squares algorithm | original mean-square-error cost function | block-sparsity-aware LMS algorithm | single block sparsity | uniformly partitioned filter tap-weight vector | acoustic signal processing

Signal processing | single-block-sparse estimate | least mean squares methods | adaptive filters | transient response | nonzero block | existing block-sparsity | LMS algorithms | echo suppression | network echo cancellation | mixed $l_{2\comma 1} | echo | path impulse response | unknown echo path | norm | ENGINEERING, ELECTRICAL & ELECTRONIC | mean squares algorithm | original mean-square-error cost function | block-sparsity-aware LMS algorithm | single block sparsity | uniformly partitioned filter tap-weight vector | acoustic signal processing

Journal Article

Signal Processing, ISSN 0165-1684, 11/2014, Volume 104, pp. 70 - 79

A new reweighted l1-norm penalized least mean square (LMS) algorithm for sparse channel estimation is proposed and studied in this paper...

Gradient descent | Least mean square (LMS) | Sparsity | Channel estimation

Gradient descent | Least mean square (LMS) | Sparsity | Channel estimation

Journal Article

Circuits, systems, and signal processing, ISSN 1531-5878, 2019, Volume 38, Issue 10, pp. 4817 - 4839

.... Unlike the existing q-LMS algorithm, the proposed approach fully utilizes the concept of q-calculus by incorporating a time-varying q parameter...

Engineering | Signal,Image and Speech Processing | Jackson derivative | Adaptive algorithms | Least mean squares algorithm | q -calculus | Electronics and Microelectronics, Instrumentation | q -LMS | Circuits and Systems | System identification | Electrical Engineering | LMS ALGORITHM | DERIVATION | CONVERGENCE | q-LMS | q-calculus | FAMILY | ENGINEERING, ELECTRICAL & ELECTRONIC | Computer science | Analysis | Algorithms | Errors | Computer simulation | Mathematical analysis | Machine learning | Convergence

Engineering | Signal,Image and Speech Processing | Jackson derivative | Adaptive algorithms | Least mean squares algorithm | q -calculus | Electronics and Microelectronics, Instrumentation | q -LMS | Circuits and Systems | System identification | Electrical Engineering | LMS ALGORITHM | DERIVATION | CONVERGENCE | q-LMS | q-calculus | FAMILY | ENGINEERING, ELECTRICAL & ELECTRONIC | Computer science | Analysis | Algorithms | Errors | Computer simulation | Mathematical analysis | Machine learning | Convergence

Journal Article

2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA), 03/2017, pp. 1 - 6

In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm...

Adaptation models | fractional calculus | fractional LMS (FLMS) | Electronic mail | Convergence | robust variable step size (RVSS) | Least mean square (LMS) | high convergence | adaptive filter | plant identification | robust variable step size FLMS (RVSS-FLMS) | Signal processing algorithms | Signal processing | modified fractional LMS (MFLMS) | Robustness | adaptive step-size modified fractional LMS (AMFLMS) | Australia | channel equalization | low steady state error

Adaptation models | fractional calculus | fractional LMS (FLMS) | Electronic mail | Convergence | robust variable step size (RVSS) | Least mean square (LMS) | high convergence | adaptive filter | plant identification | robust variable step size FLMS (RVSS-FLMS) | Signal processing algorithms | Signal processing | modified fractional LMS (MFLMS) | Robustness | adaptive step-size modified fractional LMS (AMFLMS) | Australia | channel equalization | low steady state error

Conference Proceeding

Wireless Communications and Mobile Computing, ISSN 1530-8669, 08/2015, Volume 15, Issue 12, pp. 1649 - 1658

Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advantages of both least mean square (LMS...

least mean square/fourth (LMS/F) | sparse penalty | lp‐norm LMS/F | least mean square | least mean fourth | l0‐norm LMS/F | adaptive system identification | l p -norm LMS/F | l 0 -norm LMS/F | MULTIPATH INTERFERENCE | l(p)-norm LMS | COMPUTER SCIENCE, INFORMATION SYSTEMS | TELECOMMUNICATIONS | ENGINEERING, ELECTRICAL & ELECTRONIC | fourth (LMS | NORM | CHANNEL ESTIMATION | l-norm LMS | Algorithms | Least mean squares | Least mean squares algorithm | Adaptive algorithms | Performance enhancement | Mathematical models | Deviation | Standards

least mean square/fourth (LMS/F) | sparse penalty | lp‐norm LMS/F | least mean square | least mean fourth | l0‐norm LMS/F | adaptive system identification | l p -norm LMS/F | l 0 -norm LMS/F | MULTIPATH INTERFERENCE | l(p)-norm LMS | COMPUTER SCIENCE, INFORMATION SYSTEMS | TELECOMMUNICATIONS | ENGINEERING, ELECTRICAL & ELECTRONIC | fourth (LMS | NORM | CHANNEL ESTIMATION | l-norm LMS | Algorithms | Least mean squares | Least mean squares algorithm | Adaptive algorithms | Performance enhancement | Mathematical models | Deviation | Standards

Journal Article

IEEE Transactions on Signal Processing, ISSN 1053-587X, 08/2015, Volume 63, Issue 15, pp. 4022 - 4036

We propose a multihop diffusion strategy for a sensor network to perform distributed least mean-squares (LMS...

distributed estimation | mean-square deviation | multihop diffusion adaptation | Noise | Estimation | sensor networks | Steady-state | Relays | Network topology | Signal processing algorithms | Combination weights | Spread spectrum communication | energy constraints | convergence rate | ADAPTIVE NETWORKS | GOSSIP | CONSENSUS | LEAST-MEAN SQUARES | STATE ESTIMATION | STRATEGIES | ENGINEERING, ELECTRICAL & ELECTRONIC | ADAPTATION | OPTIMIZATION | Wireless sensor networks | Usage | Diffusion processes | Analysis | Steady state theory | Network architecture | Research

distributed estimation | mean-square deviation | multihop diffusion adaptation | Noise | Estimation | sensor networks | Steady-state | Relays | Network topology | Signal processing algorithms | Combination weights | Spread spectrum communication | energy constraints | convergence rate | ADAPTIVE NETWORKS | GOSSIP | CONSENSUS | LEAST-MEAN SQUARES | STATE ESTIMATION | STRATEGIES | ENGINEERING, ELECTRICAL & ELECTRONIC | ADAPTATION | OPTIMIZATION | Wireless sensor networks | Usage | Diffusion processes | Analysis | Steady state theory | Network architecture | Research

Journal Article

IEEE Transactions on Signal Processing, ISSN 1053-587X, 01/2014, Volume 62, Issue 2, pp. 403 - 418

.... We use a set of basis functions to characterize the space-varying nature of the parameters and propose a diffusion least mean-squares (LMS...

Adaptation models | Least squares approximations | Estimation | distributed processing | Vectors | sensor networks | Covariance matrices | Convergence | interpolation | Signal processing algorithms | space-varying parameters | Diffusion adaptation | parameter estimation | Sensor networks | Distributed processing | Interpolation | Parameter estimation | Space-varying parameters | LEAST-MEAN SQUARES | NETWORKS | IDENTIFICATION | FORMULATION | DISTRIBUTED ESTIMATION | STRATEGIES | ENGINEERING, ELECTRICAL & ELECTRONIC | ADAPTATION | CONVERGENCE | OPTIMIZATION | Wireless sensor networks | Usage | Stochastic processes | Least squares | Innovations | Regression analysis | Time-domain analysis

Adaptation models | Least squares approximations | Estimation | distributed processing | Vectors | sensor networks | Covariance matrices | Convergence | interpolation | Signal processing algorithms | space-varying parameters | Diffusion adaptation | parameter estimation | Sensor networks | Distributed processing | Interpolation | Parameter estimation | Space-varying parameters | LEAST-MEAN SQUARES | NETWORKS | IDENTIFICATION | FORMULATION | DISTRIBUTED ESTIMATION | STRATEGIES | ENGINEERING, ELECTRICAL & ELECTRONIC | ADAPTATION | CONVERGENCE | OPTIMIZATION | Wireless sensor networks | Usage | Stochastic processes | Least squares | Innovations | Regression analysis | Time-domain analysis

Journal Article

Energies, ISSN 1996-1073, 2017, Volume 10, Issue 5, p. 645

In this paper, swash plate active vibration control techniques were investigated utilizing the weight-limited multi-frequency two-weight notch Least Mean Square (LMS...

Two-weight notch FxLMS filter | Swash plate vibration | Active vibration control | Axial piston machine | Adaptive Least Mean Square (LMS) filter | Two-weight notch LMS filter | adaptive Least Mean Square (LMS) filter | two-weight notch FxLMS filter | ENERGY & FUELS | axial piston machine | two-weight notch LMS filter | swash plate vibration | active vibration control | Controllers | Damping | Fidelity | Mean square values | Vibration | Computer simulation | Adjustment | Feasibility studies | Control systems | Real time | Delay | Filters | Axial flow pumps | Algorithms | Simulation | Compensation | Bandwidth | High speed | Modelling | Vibration control | Feasibility | Acceleration | Active control | Field programmable gate arrays

Two-weight notch FxLMS filter | Swash plate vibration | Active vibration control | Axial piston machine | Adaptive Least Mean Square (LMS) filter | Two-weight notch LMS filter | adaptive Least Mean Square (LMS) filter | two-weight notch FxLMS filter | ENERGY & FUELS | axial piston machine | two-weight notch LMS filter | swash plate vibration | active vibration control | Controllers | Damping | Fidelity | Mean square values | Vibration | Computer simulation | Adjustment | Feasibility studies | Control systems | Real time | Delay | Filters | Axial flow pumps | Algorithms | Simulation | Compensation | Bandwidth | High speed | Modelling | Vibration control | Feasibility | Acceleration | Active control | Field programmable gate arrays

Journal Article

Signal Processing, ISSN 0165-1684, 12/2015, Volume 117, pp. 355 - 361

... the intermediate estimates available within its closed neighborhood. We analyze the performance of a reduced-communication diffusion least mean-square (RC-DLMS...

Adaptive networks | Diffusion adaptation | Distributed estimation | Performance analysis | Communication reduction | Least mean-square | NETWORKS | STRATEGIES | ENGINEERING, ELECTRICAL & ELECTRONIC | Electrical engineering | Analysis | Algorithms

Adaptive networks | Diffusion adaptation | Distributed estimation | Performance analysis | Communication reduction | Least mean-square | NETWORKS | STRATEGIES | ENGINEERING, ELECTRICAL & ELECTRONIC | Electrical engineering | Analysis | Algorithms

Journal Article

No results were found for your search.

Cannot display more than 1000 results, please narrow the terms of your search.