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检索条件"主题词=LMS algorithm"

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Adaptive Noise Cancellation Using lms algorithm in Monte Carlo Simulation 2

Adaptive Noise Cancellation Using LMS Algorithm in Monte Car...

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2nd International Conference on Electronics and Communication Systems (ICECS)

作者： Pitchaiah, T. Sridevi, P. V. Rao, S. Koteswara Vignans Fdn Sci Technol & Res VFSTR Univ Dept ECE Guntur India Andhra Univ AU Coll Engn A Dept ECE Vishakapatnam India Vignans Inst Engn Women Dept ECE Vishakapatnam India

ISBN: (纸本)9781479972258

This brief presents the concept of adaptive noise cancellation using lms algorithm. The method uses a "primary input" containing the corrupted signal and a "reference input" containing noise correlated in some unknown way with the primary noise. The reference input is adaptively filtered and subtracted from the primary input to obtain the signal estimate. Extensive Monte Carlo Simulation is carried out and the results are presented using MATLAB.

关键词： Adaptive filter Adaptive noise cancellation lms algorithm

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Adaptive Array Beam Forming Using a Combined RLS-lms algorithm

Adaptive Array Beam Forming Using a Combined RLS-LMS Algorit...

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14th Asia-Pacific Conference on Communications

作者： Srar, Jalal Abdulsayed Chung, Kah-Seng Curtin Univ Technol Dept Elect & Comp Engn Perth WA Australia

ISBN: (纸本)9784885522321

A new adaptive algorithm, called Rlms, which combines the use of Recursive Least Square (RLS) and Least Mean Square (lms), is proposed for array beam forming. The convergence of the Rlms algorithm is analyzed, in terms of mean square error, in the presence of additive white Gaussian noise. Computer simulation results show that the convergence performance of Rlms is superior to either RLS or lms operating on its own. Furthermore, the convergence of Rlms is quite insensitive to changes in either signal-to-noise ratio, or the initial value of the input correlation matrix for the RLS section, or the step size adopted for the lms section.

关键词： RLS algorithm lms algorithm Rlms algorithm array processing adaptive array beam forming

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STOCHASTIC ANALYSIS OF THE lms algorithm FOR NON-STATIONARY WHITE GAUSSIAN INPUTS

STOCHASTIC ANALYSIS OF THE LMS ALGORITHM FOR NON-STATIONARY ...

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IEEE Statistical Signal Processing Workshop (SSP)

作者： Bershad, Neil J. Bermudez, Jose C. M. Univ Calif Irvine Dept Elect Eng & Comp Sci Irvine CA 92717 USA Univ Fed Santa Catarina Dept Elect Engn BR-88040900 Florianopolis SC Brazil

ISBN: (纸本)9781457705700

This paper studies the stochastic behavior of the lms algorithm for a system identification framework when the input signal is a non-stationary white Gaussian process. The unknown system is modeled by the standard random walk model. An approximate theory is developed which is based upon the instantaneous average power in the adaptive filter taps. The stability of the algorithm is investigated using this model. Monte Carlo simulations of the algorithm provides strong support for the theoretical approximation.

关键词： Adaptive filters Analysis lms algorithm stochastic algorithms

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A Simple Sparsity-aware Feature lms algorithm 27

A Simple Sparsity-aware Feature LMS Algorithm

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27th European Signal Processing Conference (EUSIPCO)

作者： Chaves, Gabriel S. Lima, Markus V. S. Yazdanpanah, Hamed Diniz, Paulo S. R. Ferreira, Tadeu N. Fed Univ Rio de Janeiro UFRJ Poli POB 68504 BR-21941972 Rio De Janeiro RJ Brazil Fed Univ Rio de Janeiro UFRJ COPPE POB 68504 BR-21941972 Rio De Janeiro RJ Brazil Univ Sao Paulo Dept Comp Sci Inst Math & Stat Rua Matao 1010 BR-05508090 Sao Paulo Brazil Fluminense Fed Univ UFF Engn Sch Rua Passo Patria 56 Block E Room 406 BR-24210240 Niteroi RJ Brazil

ISBN: (纸本)9789082797039

Many real systems have inherently some type of sparsity. Recently, the feature least-mean square (F-lms) has been proposed to exploit hidden sparsity. Unlike the existing algorithms, the F-lms algorithm performs a linear combination of the adaptive coefficients to reveal and then exploit the hidden sparsity. However, many systems have also plain besides hidden sparsity, and the F-lms algorithm is not able to exploit the former. In this paper, we propose a new algorithm, named simple sparsity-aware F-lms (SSF-lms) algorithm, that is capable of exploiting both kinds of sparsity simultaneously. The hidden sparsity is exploited just like in the F-lms algorithm, whereas the plain sparsity is exploited by means of the discard function applied to the filter coefficients. By doing so, the proposed SSF-lms algorithm not only outperforms the F-lms algorithm when plain sparsity is also observed, but also requires fewer arithmetic operations. Numerical results show that the proposed algorithm has faster speed of convergence and reaches lower steady-state mean-squared error (MSE) than the F-lms and classical algorithms, when the system has plain and hidden sparsity.

关键词： adaptive filtering lms algorithm feature matrix discard function sparsity

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Performance Analysis of Bessel Beamformer with lms algorithm for Smart Antenna Array

Performance Analysis of Bessel Beamformer with LMS Algorithm...

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6th International Conference on Open Source Systems and Technologies (ICOSST)

作者： Yasin, M. Akhtar, Pervez Natl Univ Sci & Technol Dept Elect & Power Engn Islamabad Pakistan

ISBN: (纸本)9781467330947;9781467330961

The objective of this paper is to analyze the mathematical model of Bessel beamformer with least mean square (lms) beamforming algorithm using offset quadrature phase shift keying (OQPSK) which is one of the efficient digital modulation techniques. The desired user is placed at an angle 20 degree and all other users as interfering signals in a Rayleigh fading scenario. The performance is judged in terms of signal recovery, directive gain, minimum mean square error (MMSE), saving in transmitted power and rate of convergence. The analysis demonstrates that minimization of MSE can be an important technique for the optimization of a weight vector to enhance gain and capacity.

关键词： component smart/adaptive antenna array system Bessel beamformer lms algorithm

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A hyperbolic lms algorithm for CORDIC based realization

A hyperbolic LMS algorithm for CORDIC based realization

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11th IEEE Workshop on Statistical Signal Processing (SSP 2001)

作者： Chakraborty, M Pervin, S Lamba, TS Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)0780370112

An alternate formulation of the lms algorithm is presented by expressing the mean square error as a convex function of a set of hyperbolic variables that are monotonically related to the filter tap weights. The proposed algorithm is ideally suitable for CORDIC based realization and possesses very good convergence characteristics as revealed via extensive simulation studies.

关键词： lms algorithm CORDIC arithmetic time varying system

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Research on Linear Evaluation Function Based on lms algorithm 29

Research on Linear Evaluation Function Based on LMS Algorith...

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第29届中国控制与决策会议

作者： Haoran Zhang Shuqin Li Meng Ding Beijing Information Science & Technology University Computer School

ISBN: (纸本)9781509046584

Evaluation function is one of the core components of the computer game *** traditional way to build evaluation function is usually to use a number of features on the board extracted by human,according to experience and game rules,which gives a set of parameters,and design a static linear evaluation function to calculate the evaluation value of current *** paper proposes a method to dynamically adjust and optimize the parameters in the evaluation function by least mean squares,and implements a dynamic evaluation *** based on Surakarta chess show that the method of dynamic evaluation is effective.

关键词： Computer Game Surakarta Chess linear evaluation function lms algorithm Least Mean Square algorithm

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An improved lms algorithm based on fractional order gradient direction 41

An improved LMS algorithm based on fractional order gradient...

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第41届中国控制会议

作者： Haozhe Zhang Lipo Mo School of Mathematics and Statistics Beijing Technology and Business University School of Artificial Intelligence and Electrical Engineering Guizhou Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper mainly focuses on the lms algorithm based on fractional order gradient information,which extends the first order gradient of traditional lms algorithm to fractional order α(0 <α≤1).Since the lms algorithm generally does not converge to the true extreme value point of the objective function when fractional order gradient is *** ensure convergence,the strategy of truncating the second order term of the fractional order gradient expansion of the objective function is *** step size condition for convergence is given and the performance of the algorithm with different fractional orders a is *** is shown that under certain conditions,a larger fractional order α will lead a faster convergence ***,the effectiveness of the proposed algorithm is illustrated by four simulation examples.

关键词： Fractional order calculus lms algorithm Fractional order gradient Truncating Convergence speed

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Active Noise Suppression of SISO model using a Filtered X-lms algorithm

Active Noise Suppression of SISO model using a Filtered X-LM...

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International Conference on Signal Acquisition and Processing

作者： Bafti, Payam Shafiei Ahmad, Raja Kamil Raja Mushin, Mazin T. Noor, S. B. Mohd Univ Putra Malaysia Fac Elect Engn Serdang 43400 Selangor Malaysia

ISBN: (纸本)9780769535944

A filtered-X lms algorithm is implemented for active noise control in free field, single channel adaptive proposed for one dimensional noise control, The single-channel algorithms is essentially extension by the sequential filtered-X lms algorithm to the block case. Analytical results are given for the stability and convergence of the algorithm. Simulation results are also given to demonstrate the performance of the algorithm in single-channel noise cancellation. The error,vas converge after 3.3 sample periods

关键词： lms algorithm Filtered-X lms Adaptive control SISO model

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Performance Analysis of Bessel Beamformer and lms algorithm for Smart Antenna Array in Mobile Communication System

Performance Analysis of Bessel Beamformer and LMS Algorithm ...

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2nd International Multi-Topic Conference

作者： Akhtar, Pervez Yasin, Muhammad Natl Univ Sci & Technol Dept Elect & Power Engn Islamabad Pakistan

ISBN: (纸本)9783642289613

In this paper, we analyze the performance of Bessel beamformer based on spatial filtering criteria and its comparison is made with least mean square (lms) algorithm in terms of gain enhancement towards desired user by suppressing interference, reduction in transmit power, minimize mean square error (MSE) and to find the optimum array weights. These algorithms are used in digital signal processor and connected with antenna which can dynamically update the main beam width, beam steering capability, side lobe levels and direction of null as the propagation environment or filtering requirement changes. Based on simulation results, it is revealed that Bessel beamformer provide remarkable improvements in terms of gain, interference suppression, reduction in transmit power, improvement in power gain and weights convergence over that of lms algorithm. With respect to lms, Bessel beamformer provides 2.02% improvements in interference suppression, 5 dB enhancements in gain, 99% improvement in convergence rate, 9.77% improvement in reduction in mean output power, 192% improvement in power gain and 107 % in null depth performance. Therefore. Bessel beamformer gives a cost effective solution in practical base station installations of mobile communication system in CDMA environment to enhance capacity and range.

关键词： Smart adaptive antenna array system Bessel beamformer lms algorithm

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