The Volterra filter is one of the digital filters that can describe nonlinearity. In this paper, we analyze the dynamic behaviors of an adaptive signal-processing system including the Volterra filter by a statistical-...
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The Volterra filter is one of the digital filters that can describe nonlinearity. In this paper, we analyze the dynamic behaviors of an adaptive signal-processing system including the Volterra filter by a statistical-mechanical method. On the basis of the self-averaging property that holds when the tapped delay line is assumed to be infinitely long, we derive simultaneous differential equations in a deterministic and closed form, which describe the behaviors of macroscopic variables. We obtain the exact solution by solving the equations analytically. In addition, the validity of the theory derived is confirmed by comparison with numerical simulations.
In many areas, it is extremely important to accurately estimate the amplitude and phase of a signal even if the frequency contained in the signal to be analyzed is known. For cases in which the signal frequencies to b...
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In many areas, it is extremely important to accurately estimate the amplitude and phase of a signal even if the frequency contained in the signal to be analyzed is known. For cases in which the signal frequencies to be analyzed are in a nonharmonic relation and white noise and interference are superposed on the signal, a method (BPlms method) combining the IIR type BPF and the lms algorithm has been proposed to derive the Fourier coefficients of the signal components accurately. In the present paper, in order to investigate a method of improving the BPlms method, an approximate equation for the estimated accuracy is derived. Also, the validity of the approximate equation is confirmed by computer simulations under various conditions. As a result of the approximate analysis, it is shown that the normalized frequency difference between the signal and the interference can be increased by combining a down-sampling process so that the estimation accuracy can be improved. Finally, by simulation, the proposed method and the BPlms method are compared in terms of estimation accuracy. An example of application to the pitch estimation of musical sounds is given. (C) 2002 Wiley Periodicals, Inc.
In this paper, we investigate the identification problem of the Hammerstein nonlinear systems. In order to approximate the nonlinear block and for the convenience of analyzing the convergence of the proposed algorithm...
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In this paper, we investigate the identification problem of the Hammerstein nonlinear systems. In order to approximate the nonlinear block and for the convenience of analyzing the convergence of the proposed algorithm, we modify the conventional spline interpolation such that the nonlinear output signal can be expressed in a universal form. Besides, we develop a fractional order lms (Least-Mean-Square) algorithm to identify the linear block and control points of the nonlinear block and establish the convergence properties of the algorithm by employing the stability theory of fractional order difference systems. Finally, we also provide two numerical examples to illustrate the effectiveness of the proposed algorithm. (C) 2019 Elsevier B.V. All rights reserved.
Percussion-based inspection of structures has attracted widespread attention in recent years. However, the percussion acoustic signals collected in the marine environment usually have a low signal-to-noise ratio (SNR)...
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Percussion-based inspection of structures has attracted widespread attention in recent years. However, the percussion acoustic signals collected in the marine environment usually have a low signal-to-noise ratio (SNR) and are difficult to use directly due to the interference by a multitude of marine noises. The frequency contents of the ambient noises usually overlap with those of the percussion acoustic signals, thus limiting the denoising using traditional methods. This paper proposes a denoising method using the least mean square (lms) algorithm to obtain the approximate percussion signal. The noisy percussion signals and marine noise are recorded syn-chronously by two hydrophones. Then the lms algorithm processes the collected signals and provides the fre-quency peaks that cannot be extracted with conventional methods. The proposed method is validated by experiments conducted in a noiseless laboratory environment and a noisy, naturally occurring marine envi-ronment. The results reveal that the proposed method is excellent in denoising the raw signal, and the error is about 3% in terms of differences in the estimated value of the primary peak frequency. This study demonstrates the broad potential for the method to be applied toward damage detection for underwater structures.
In the power system, there are many power electronic equipment in use. There are a lot of harmonics in the power system because of their nonlinear properties. These harmonics have a huge impact on the power grid. To f...
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In the power system, there are many power electronic equipment in use. There are a lot of harmonics in the power system because of their nonlinear properties. These harmonics have a huge impact on the power grid. To filter out these harmonics, some harmonic detection methods are needed. This work suggests using the lms algorithm to detect each harmonic in order due to the complexity of all current harmonic detection algorithms or the fact that only the total harmonic components other than the fundamental wave may be discovered. This technique can quickly determine the amplitude and phase size of each harmonic component in addition to having a straightforward procedure. By creating a Matlab Simulink simulation model, the efficacy of this approach is confirmed.
Robustness of the Least Mean Square algorithm (lms) with respect to non-additive noise is considered in this paper. Under very general assumptions, an almost sure upper bound for the asymptotic deviation of algorithm ...
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Robustness of the Least Mean Square algorithm (lms) with respect to non-additive noise is considered in this paper. Under very general assumptions, an almost sure upper bound for the asymptotic deviation of algorithm iterations from the solution of the corresponding Wiener-Hopf equation has been determined.
In this paper we are focused on the Multi-Carrier Code Division Multiple Access (MC-CDMA) equalization problem. The equalization is performed using the Minimum Mean Square Error (MMSE) and Zero Forcing (ZF) equalizer ...
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In this paper we are focused on the Multi-Carrier Code Division Multiple Access (MC-CDMA) equalization problem. The equalization is performed using the Minimum Mean Square Error (MMSE) and Zero Forcing (ZF) equalizer based on the identified parameters representing the indoor scenario (European Telecommunications Standards Institute Broadband Radio Access Networks (ETSI BRAN A) channel model), and outdoor scenario (ETSI BRAN E channel model). These channels are normalized for fourth-generation mobile communication systems. However, for such high-speed data transmissions, the channel is severely frequency-selective due to the presence of many interfering paths with different time delays. The identification problem is performed using the Least Mean Squares (lms) algorithm and the Takagi-Sugueno (TS) fuzzy system. The comparison between these techniques, for the channel identification, will be made for different Signal to Noise Ratios (SNR).
A variety of different approaches in the variable step adjustment algorithm of the lms were researched to achieve fast convergence and robustness, but the complexity of the variable step algorithm was also become high...
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A variety of different approaches in the variable step adjustment algorithm of the lms were researched to achieve fast convergence and robustness, but the complexity of the variable step algorithm was also become higher. A variable step size lms algorithm using squared error and autocorrelation of error is proposed to achieve fast convergence and robustness under reasonable complexity. The performance of the proposed lms algorithm is analyzed in a stationary environment. The proposed algorithm is tested under an adaptive equalizer system and showed good convergence rate and robustness to disturbance.
Underwater acoustic channels are recognized for being one of the most difficult propagation media due to considerable difficulties such as： multipath, ambient noise, time-frequency selective fading. The exploitation ...
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Underwater acoustic channels are recognized for being one of the most difficult propagation media due to considerable difficulties such as： multipath, ambient noise, time-frequency selective fading. The exploitation of sparsity contained in underwater acoustic channels provides a potential solution to improve the performance of underwater acoustic channel estimation. Compared with the classic 10 and 11 norm constraint lms algorithms, the p-norm-like （Ip） constraint lms algorithm proposed in our previous investigation exhibits better sparsity exploitation performance at the presence of channel variations, as it enables the adaptability to the sparseness by tuning of p parameter. However, the decimal exponential calculation associated with the p-norm-like constraint lms algorithm poses considerable limitations in practical application. In this paper, a simplified variant of the p-norm-like constraint lms was proposed with the employment of Newton iteration m to approximate the decimal exponential calculation. Num simulations and the experimental results obtained in physical shallow water channels demonstrate the effectiveness of the proposed method compared to traditional norm constraint lms algorithms.
The design of adaptive nonlinear filters has sparked a great interest in the machine learning community. The present paper aims to present some recent developments in nonlinear adaptive filtering. It provides an in-de...
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The design of adaptive nonlinear filters has sparked a great interest in the machine learning community. The present paper aims to present some recent developments in nonlinear adaptive filtering. It provides an in-depth analysis of the performance and complexity of a class of kernel filters based on the least-mean-squares algorithm. A key feature that underlies kernel algorithms is that they map the data in a high-dimensional feature space where linear filtering is performed. The arithmetic operations are carried out in the initial space via evaluation of inner products between pairs of input patterns called kernels. The SNR improvement and the convergence speed of kernel-based least-mean-squares filters are evaluated on two types of applications: time series prediction and cardiac artifacts extraction from magnetoencephalographic data.
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