Papers by Keyword: LM Algorithm

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Authors: Wei Ju Cai
Abstract: The paper proposed a modified LMS algorithm of variable step size based on a brief analysis of traditional LMS,variable step size LMS algorithm and its improved algorithm.The novel algorithm based on nonlinear functional relationship between the step-size and the error ,increases adaptively at the beginning of the algorithm or when the channel is varying with time ,and it would be smaller during the steady state.So the algorithm has the excellences of faster constringency,little steady error ,tracking the change of the system and avoiding the effects of the noise. The theoretical analysis and computer simulation prove that the algorithm is better than traditional LMS algorithm.
Authors: Xue Li Wu, Zi Zhong Tan, Liang Gao
Abstract: . Aiming at the disadvantage of the variable step size LMS adaptive filtering algorithms' convergence speed contradicting its steady-state error, a novel non-liner functional relationship between μ (n) and error signal e (n) was established. On the basis of the functional relationship, a new algorithm of variable step size LMS adaptive filtering was presented. The step size factor of the new algorithm is adjusted by the absolute value of the product of the current and former errors. It also uses the absolute estimation error compensation terms disturbance to speed up the convergence of adaptive filter tap weight vector. At the same time, the algorithm considers the relationship between step length of the last iteration and the former M error signal. As a result the algorithm has higher convergence characteristic and small steady state error. The theoretical analysis and simulation results show that the new algorithm has faster convergence speed, lower steady state error and better performance of noise suppression, also show the overall performance of this algorithm exceeds some others condition.
Authors: Fu Qing Tian, Rong Luo
Abstract: In the paper, a new variable step size LMS algorithm based on modified hyperbolic tangent is presented. In the algorithm, the step size is adjusted by the estimation of the autocorrelation between and .The algorithm introduces the compensation monomial to improve the convergence and the parameters to improve the shape and bottom characteristic of hyperbolic tangent. Therefore, the algorithm has faster convergence, better performance of noise suppression,lower steady state error and misadjustment. The theoretical analysis and simulation results all show that the overall performance of the new algorithm exceeds greatly some existent others under low SNR condition.
Authors: Shuo Ding, Xiao Heng Chang, Qing Hui Wu
Abstract: When approximating nonlinear functions, standard BP algorithms and traditional improved BP algorithms have low convergence rate and tend to be stuck in local minimums. In this paper, standard BP algorithm is improved by numerical optimization algorithm. Firstly, the principle of Levenberg-Marquardt algorithm is introduced. Secondly, to test its approximation performance, LMBP neural network is programmed via MATLAB7.0 taking specific nonlinear function as an example. Thirdly, its approximation result is compared with those of standard BP algorithm and adaptive learning rate algorithm. Simulation results indicate that compared with standard BP algorithm and adaptive learning rate algorithm, LMBP algorithm overcomes deficiencies ranging from poor convergence ability, prolonged convergence time, increasing iteration steps to nonconvergence. Thus with its good approximation ability, LMBP algorithm is the most suitable for medium-sized networks.
Authors: Gaurav Bharti, Dhananjay Ghangale, Dhanesh Manik
Abstract: — The performance of an ANC system depends on the convergence factor. A suitable value of convergence coefficient is extremely important as it impacts both the speed of convergence and the stability of the adaptive algorithm. Traditionally, in active noise control systems fixed convergence factor is used. In this paper, a novel approach of time-varying convergence factor μ is used and the results are compared with traditional feedforward and feedback noise control systems. This algorithm leads to faster convergence and provides reduced mean-squared error compared to the conventional fixed parameter algorithm.
Authors: P. Sirithummachak, C. Benjangkaprasert
Abstract: This paper proposes a sign-algorithm based on adaptive Laguerre filter structure for improving the performance of the direct sequence-code division multiple access (DS-CDMA) communication system. The proposed adaptive equalizer employs a combination of sign and least mean square (LMS) algorithms to minimizing the effect of inter-symbol interference (ISI) that arises due to multipath propagation, and the computational complexity of equalizer. The performance of proposed equalizer was compared with traditional equalizers such as LMS and RLS algorithms in terms of varying path. The function detail and performance results of the proposed equalizer are described by computer simulations in terms of the bit error rate (BER).
Authors: Zhuo Jing Yang, Jian Wei Zhang, Wen Jie Hao, Jin Ping Yang
Abstract: Because resistance of two-dimensional position sensitive detector's (PSD) photo surface is not absolute uniformity that its output is nonlinear. It is this feature enables the PSD difficult to measure small displacement. In order to solve this problem, BP neural network is proposed to solve the problem of PSD nonlinear correction after the study of traditional nonlinear correction method; BP neural network would have a strong ability of nonlinear mapping after training, and it can approach arbitrarily contact function by arbitrary precision, and MATLAB neural networking boxes can simulate BP neural network easily. Simulation and verification indicate that the method has a remarkable effect in solving nonlinear problems, and it can meet system requirements.
Authors: Shuo Ding, Xiao Heng Chang
Abstract: As a key factor in a testing system, sensor nonlinearity has always been the study focus in the field of engineering and techniques. In order to accurately reflect the practical characteristics of a fiber-optic micro-bend sensor, Levenberg-Marguardt (LM) algorithm is used to optimize the correction of the weight values of standard back propagation neural network (BPNN). The learning process of improved BPNN based on LM algorithm (LM-BPNN) is also illustrated mathematically, and LM-BPNN is applied in fitting the input and output characteristic curve of a fiber-optic micro-bend sensor. The simulation results show that LM-BPNN is superior both in its convergence rate and fitting precision over standard BPNN.
Authors: Jie Zhang
Abstract: Abstract: adaptive notch filter is a kind of apparatus which can eliminate single frequency or narrow-band interference, normal adaptive algorithm of notch filter is LMS algorithm, but the faster convergence velocity and the smaller steady error are difficult to gain simultaneously. Aimed at the weakness of LMS, the Particle Swarm Optimization (PSO) is studied deeply in the paper, based on the PSO; the quantum mechanic theory is added to improve it. Quantum Particle Swarm Optimization (QPSO) is researched and applied for adaptive notch filter which is proved more efficient in the noise control by MATLAB simulation. The new QPSO algorithm can balance the maladjustment and the searching ability of adaptive filter with a little calculation, the speed of convergence is faster than LMS and normal PSO algorithm.
Authors: X. Li, Z.L. Ding, F. Yuan
Abstract: The correlation method had once been considered as one of the best methods for the measurement of multiphase flow. However, if the behavior of flow does not fit the ergodic random process, the measured cross correlation plot will have a gross distortion when the different components of flow do not pervade within one another to the full extent. We measured a variety of parameters of three phase oil/water/gas flow in an oil pipeline. The change of flow pattern is so complex that the measured signals are always contaminated by stochastic noises. The weak signals are very easily covered by the noise so that it will result in great deviation. Wavelet transformation is an analytical method of both time and frequency domain. The method can achieve signal decomposition and location in time and frequency domain through adjustment and translation of scale. An LMS algorithm in wavelet transform is studied for denoising the signals based on the use of a novel smart capacitive sensor to measure three phase oil/water/gas flow in oil pipeline. The results of simulation and data processing by MATLAB reveal that wavelet analysis has better denoising effects for online measurement of crude oils with high measurement precision and a wide application range.
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