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The paper discusses compensation of several sinusoidal disturbance signals entering the system simultaneously. The disturbances may have different frequencies, amplitudes and phases. A modification of the well-known LMS algorithm is proposed, which makes a direct feedforward compensation algorithm possible. It is demonstrated that the problems of the standard LMS algorithm, when several disrurbance...
Recently, few stochastic gradient algorithms have been proposed and they are based on cost functions that have exponential dependence on the chosen error. However, we have experienced that the proposed cost function based on exponential of the squared error does not converge always. In this paper we modify this cost function in order to assure the convergence and a new IE (Improved Exponentiated)...
The conventional algorithms in the echo canceling system have drawback when they are faced with double-talk condition in noisy environment. Since the double-talk and noise signal are exist, then the error signal is contaminated to estimate the gradient correctly. In this paper, we define a new adaptive algorithm for tap adaptations, based on the correlation function processing, which is called Extended...
A fundamental problem with the Least Mean Squares (LMS) and Normalized LMS (NLMS) algorithms is their slow convergence given a colored input signal. For white input signals, on the other hand, these simple and popular algorithms yield quite satisfactory performance. There are a stunning number of different algorithms that try to deal with this problem, all of which, in some fashion, use decorrelation...
In practice, the length of the impulse response of the system to be identified is unknown and often infinite. When the system is modeled as an FIR filter, the length is usually shorter, and hence the name deficient-length filter. The learning rate, mean square error, and other properties of a deficient-length adaptive filter are different from that of a filter that is of sufficient length. In this...
This paper proposes a new noise-constrained normalized least mean squares (NC-NLMS) adaptive filtering algorithm and studies its mean and mean square convergence behaviors. The new NC-NLMS algorithm is obtained by extending the noise-constrained LMS (NC-LMS) algorithm of Wei, which was proposed to explore the prior information on the noise variance in identifying unknown finite impulse response channels...
In this paper, we propose a block least mean square algorithm with delayed weight adaptation for hardware implementation of finite impulse response (FIR) adaptive filters. We have referred to the proposed algorithm as delayed block least mean square (DBLMS) algorithm. Unlike the delayed least mean square (DLMS) algorithm, the DBLMS algorithm takes a block of L input samples and yields a block of L...
Complex signal representations are being frequently employed in various adaptive filtering applications such as wireless communications, beamforming, etc. In this paper, a novel complex optimum block adaptive algorithm with individual adaptation of parameters (Complex OBAI-LMS) is presented. The proposed technique effectively utilizes the degrees of freedom of the adaptive filter by individually adapting...
The Complex Least Mean Square algorithm (Complex LMS) has been widely used in various adaptive filtering applications, e.g. in the wireless communications and biomedical fields, due to its computational simplicity. However, the main drawback of the Complex LMS algorithm is its slow convergence. In addition, the performance is dependent on the choice of the convergence factor or learning rate. In this...
An algorithm for the convergent adaptation of a CMAC neural network in feedforward disturbance cancellation architectures is presented. This technique is a generalization of the Filtered-X LMS algorithm used in the case of linear adaptive filters. The fundamental advantage provided by the CMAC compensator is its effectiveness in systems with nonlinearities in the actuators, sensors, and signal transmission...
Theoretical and experimental analysis and description of wavelet-based filtering are given in the case of a stationary desired signal. The impulse responses of the adaptive filter and the unknown system producing the desired signal are represented by discrete-time wavelet series. The authors have found the coefficients that minimize the mean square error and pointed out the time-frequency localized...
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