In this paper we introduce a novel adaptation algorithm for adaptive filtering of FIR and IIR digital filters within the context of system identification. The standard lms algorithm is hybridized with GA (Genetic Algo...
详细信息
In this paper we introduce a novel adaptation algorithm for adaptive filtering of FIR and IIR digital filters within the context of system identification. The standard lms algorithm is hybridized with GA (Genetic algorithm) to obtain a new integrated learning algorithm, namely, lms-GA. The main aim of the proposed learning tool is to evade local minima, a common problem in standard lms algorithm and its variants and approaching the global minimum by calculating the optimum parameters of the weights vector when just estimated data are accessible. In the proposed lms-GA technique, first, it works as the standard lms algorithm and calculates the optimum filter coefficients that minimize the mean square error, once the standard lms algorithm gets stuck in local minimum, the lms-GA switches to GA to update the filter coefficients and explore new region in the search space by applying the cross-over and mutation operators. The proposed lms-GA is tested under different conditions of the input signal like input signals with colored characteristics, i.e., correlated input signals and investigated on FIR adaptive filter using the power spectral density of the input signal and the Fourier-transform of the input's correlation matrix. Demonstrations via simulations on system identification of IIR and FIR adaptive digital filters revealed the effectiveness of the proposed lms-GA under input signals with different characteristics.
Starting from the characteristic of voice signal itself, a new variable step-size lms algorithm based on DCT is proposed in order to improve the comprehensive performance in adaptive noise cancellation, which combines...
详细信息
Starting from the characteristic of voice signal itself, a new variable step-size lms algorithm based on DCT is proposed in order to improve the comprehensive performance in adaptive noise cancellation, which combines the merits of normalized DCT-lms algorithm and variable step-size lms algorithm, and give full play to the decorrelation capability of DCT and rapid convergence effect of variable step-size algorithm. The c simulation results show that the new algorithm has faster convergence rate and smaller steady state error compared with traditional lms and Nlms algorithm, however, the computational complexity is comparable to Nlms algorithm at the same time.
This work presents a new block-sparse least mean square (BS-lms) algorithm with adaptive and nonuniform group sizes. Motivated by the fact that large and zero coefficients in block-sparse systems assemble in clusters ...
详细信息
ISBN:
(纸本)9781538668122;9781538668115
This work presents a new block-sparse least mean square (BS-lms) algorithm with adaptive and nonuniform group sizes. Motivated by the fact that large and zero coefficients in block-sparse systems assemble in clusters of variant sizes, we divide the adaptive filter tap-weights into groups of nonuniform size. A predetermined threshold value is utilized for real-time group partitioning Large coefficient groups consist of clusters of coefficients whose magnitudes are above the threshold value, while zero coefficient groups consist of clusters of coefficients whose magnitudes are below the threshold value. Then, coefficients in large coefficient groups are updated by following the standard lms algorithm, while those in zero coefficient groups suffer from an attractive force towards zero. A practical choice guideline is provided for the threshold value to facilitate applications. The proposed algorithm is tested in the identification of single- and multiple-block-sparse systems. Simulation results show that the proposed scheme effectively locates and tracks the non-zero blocks in the unknown systems, outperforming existing block-sparsity-inspired lms algorithms with uniform group sizes.
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 an...
详细信息
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.
This article focuses on the application of adaptive filter based on the lms algorithm. An adaptive filter of the closed-loop system is introduced, including the elimination of interference signal, the prediction of us...
详细信息
This article focuses on the application of adaptive filter based on the lms algorithm. An adaptive filter of the closed-loop system is introduced, including the elimination of interference signal, the prediction of useful signal, and the approximation of expected signal. lms (Least Mean Square) algorithm is used to meet the optimum norm of error between estimated signal and expected signal. The structure of lms algorithm is presented and the simulation of lms algorithm is carried out. The results indicate that the convergence performances of lms algorithm are prefect, and the input signal can converge to the expected signal. The application of adaptive filtering technology in this article includes the correction of channel mismatch by an adaptive linear filter, the improvement of system performance by an adaptive equalizer, and the filter of frequency signal by an adaptive notch filter. The analysis on adaptive linear filter shows that the constant channel mismatch can be corrected quite well by the correction algorithm. The analysis on adaptive equalizer shows that the error rate of system with an adaptive equalizer has significant improvement gains over that of system without an adaptive equalizer. The smaller the error rate, the larger the SNR. The relationship between error rate and multi-path loss show that the error rate is largest when the loss factor is 0.5. The analysis on adaptive notch filter shows that the interference signal with two different known frequencies can be eliminated effectively by the adaptive notch filter. The filtered signals accord with the corresponding useful signals very well. (C) 2016 Published by Elsevier GmbH.
The paper presents performance analysis of least-mean-square algorithm based adaptive filter embedded with constant false alarm rate (CFAR) detector for the purpose of better detection of target under non-homogeneous ...
详细信息
The paper presents performance analysis of least-mean-square algorithm based adaptive filter embedded with constant false alarm rate (CFAR) detector for the purpose of better detection of target under non-homogeneous clutter environment in radar application. The objective of this paper is to develop a method by redesigning the radar detector in such a way to emphasize the target response and de-emphasize the clutter response. The hardware implementation using pipeline technique for the adaptive filter reveals its capability to support high sampling frequency, which is an ardent necessity for high performance radar. The moderate area-delay-product and low power consumption have made it suitable for hardware realization for such application. The extensive MATLAB simulation of proposed design shows remarkable improvement of detection performance in terms of signal-to-noise ratio of 17dB considering probability of detection at 0.8 over the generic cell averaging CFAR (CA-CFAR). Copyright (c) 2015 John Wiley & Sons, Ltd.
With the advancement in the field of design of adaptive filter it is expected that the convergence will improve correctness in the estimates of output. Adaptive filter system focus on integrity in the existing lms alg...
详细信息
ISBN:
(纸本)9781509006656
With the advancement in the field of design of adaptive filter it is expected that the convergence will improve correctness in the estimates of output. Adaptive filter system focus on integrity in the existing lms algorithm including FIR filter, with the computation using different data formats. Adaptive filter system support various applications with the objective to provide stable system performance. Current adaptive filter system needs in depth investigation in the computing domain to enhance the quality of the estimates. Hence interval arithmetic domain is used to increase the precision of computation. Proposed solution to this research task in this aspect is examined for a sine signal.
In this paper, we propose an l(0)-norm penalized shrinkage linear least mean squares (l(0)-SH-lms) algorithm for an adaptive decision feedback equalizer (DFE). The proposed algorithm utilizes the priori and the poster...
详细信息
ISBN:
(纸本)9781467399784
In this paper, we propose an l(0)-norm penalized shrinkage linear least mean squares (l(0)-SH-lms) algorithm for an adaptive decision feedback equalizer (DFE). The proposed algorithm utilizes the priori and the posteriori errors to calculate the varying step-size. Thus a larger coefficient produces a larger increment to accelerate the convergence, and a small coefficient gives a smaller increment to improve the estimation accuracy, so the algorithm can adapt to the time-varying channel efficiently. Meanwhile, a l(0)-norm penalty term is introduced in the cost function to improve the applicability to a sparse system. Simulation results show that, compared with the conventional lms-type algorithms, the proposed algorithm achieves better performance in both the convergence rate and steady-state misalignment for the sparse channels. When the proposed algorithm is applied to the DFE, the equalization performance is clearly improved.
This paper describes a new control approach called Adaptive linear neuron (ADALINE) based Least mean square (lms) algorithm used in a three phase four wire (3P4W) distribution system under single phase fault condition...
详细信息
ISBN:
(纸本)9781509001286
This paper describes a new control approach called Adaptive linear neuron (ADALINE) based Least mean square (lms) algorithm used in a three phase four wire (3P4W) distribution system under single phase fault condition. Three phase reference supply currents are generated by using the proposed control technique thereafter they compared with the corresponding sensed supply currents to produce switching pulses for the IGBTs of a four leg voltage source converter (4-leg VSC) based distribution static compensator (DSTATCOM). In this paper, the DSTATCOM is used for voltage regulation, power factor correction and elimination of distorted/unbalanced source current & excessive neutral current in the system. MATLAB/SimPowerSystem environment is used to simulate the distribution system under both fault and without fault conditions.
In this paper, a new competent control approach is implemented to investigate the performance of a Distribution STATic COMpensator (DSTATCOM) in a three phase balanced nonlinear load supplied by a three-phase AC mains...
详细信息
ISBN:
(纸本)9781509001286
In this paper, a new competent control approach is implemented to investigate the performance of a Distribution STATic COMpensator (DSTATCOM) in a three phase balanced nonlinear load supplied by a three-phase AC mains. The modern control approach, ADALINE based lms algorithm is used to produce the switching pulses for the voltage source inverter (VSI) of DSTATCOM followed by the generation of reference source current. The performance of the DSTATCOM is analysed under single phase fault condition. System stabilization, harmonic mitigation of source current and alleviation of unbalanced supply current are achieved successfully using the control approach under single phase fault condition. The simulations of the proposed system are performed in a MATLAB/Simulink software.
暂无评论