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Purpose: To improve fixed filter tap-length and step-size adaptive filtering control algorithm for micro vibration suppression applications using piezoelectric stack actuators, a hybrid DE-Jaya optimized variable step-size and tap-length adaptive filtering control algorithm is proposed to balance the convergence speed and steady-state error. Methods: To describe the rate-dependent hysteresis characteristic of the piezoelectric stack actuator, a Hammerstain model combining discrete time non-symmetrical Bouc–Wen model is proposed. The proposed algorithm (named FX-VSSVFT-LMS algorithm) employs self-optimal tuning variable step-size and variable fractional tap-length. An improved optimization algorithm named hybrid DE-Jaya algorithm is proposed to automatically tune the algorithm parameters of FX-VSSVFT-LMS algorithm to obtain appropriate algorithm parameters, in which a hybrid mutant operator and Jaya operator to balance between convergence speed and solution accuracy. Results: A 3DOF micro-vibration isolation experimental platform is built using piezoelectric stack actuators. Under the same vibration disturbance, experimental tests are done. Conclusion: Comparison experiments show the proposed FX-VSSVFT-LMS algorithm with optimal parameters tuning has better convergence performance and control performance. [ABSTRACT FROM AUTHOR]

Estimation in a cooperative and distributed manner in wireless sensor networks (WSNs) has considered much attention in recent years. When this distributed estimation is performed adaptively, the concept of adaptive networks will develop. In such networks, proper selection of the unknown parameter length is an issue in itself. A deficient filter length results in an additional steady-state error while selecting a large length will impose a more computational load on the nodes, which is critical in sensor networks due to the lack of energy resources. This motivates the use of variable tap-length adaptive filters in the context of the adaptive networks. This has been achieved in adaptive networks using the distributed fractional tap-length (FT) algorithm. This algorithm requires proper selection of the length adaptation parameters, such as error width and length adaptation step-size. This paper proposes an automatic method for selecting these parameters. In the proposed method, these parameters are adapted based on the estimated gradient vector. The proposed method is fully distributed and presented in a diffusion strategy. Simulation results show that the proposed algorithm has both the advantage of fast length convergence and an unbiased steady-state tap-length. [ABSTRACT FROM AUTHOR]

Abstract The variable-tap-length algorithms try to find out the optimum number of adaptive filter coefficients that are necessary to model an unknown plant. The most recent variable-tap-length algorithms claim themselves to be independent of the order of the unknown channel, even though the convergence rate of an adaptive finite impulse response filter that is used for identifying an unknown plant in a system identification framework may be affected by the characteristics of the input signal and time-varying filter parameters. The system dimension may vary notably from one application to others, such as acoustic echo cancellation in a big conference hall needs thousands of filter weights, whereas feedback cancellation in hearing-aids requires less than a hundred coefficients. Hence, there is a need to analyze the convergence of the variable-tap-length algorithm for these time-varying applications of adaptive filters of different dimensions. In this paper, we develop and propose a novel technique to measure the convergence and transient behavior of the variable-tap-length adaptive algorithm. Using this analysis, we also arrive at conclusion that the median rate of convergence declines with the rise in system dimension to a limit determined by the reference input. It is proved that the limiting point can be obtained by the level of the autocorrelation of the input signal. [ABSTRACT FROM AUTHOR]

Featured Application: The method can be applied to active noise control (ANC). As all actual environments are unknown, the tap-length considerably influences ANC. The proposed algorithm can determine the optimal tap-length to optimize computation time and avoid the under-modeling problem. The effect of noise reduction can be improved to a certain extent. Convergence speed and steady-state performance are important factors to estimate the variable tap-length algorithm. The step-size of tap-length (ST) and the error width considerably impact the performance of the fractional variable tap-length algorithm. A mixed fractional variable tap-length algorithm that simultaneously applies time-varying ST and error width strategies is presented in this paper. The ST and the error width are analyzed in detail. This method uses the same variable to control both aspects, so their changes are relatively consistent. The parameters have strong adaptive ability at different stages. The proposed algorithm can solve some defects of the previous algorithms, including slow convergence rate, weak steady-state performance and robustness. A novel mean square deviation (MSD) gradient experiment is performed. Experimental results show good agreement with the theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, we propose a joint adaptive combining and variable tap-length multiuser detector (MUD) for amplify-and-forward (AF) underwater acoustic cooperative interleave-division multiple access (IDMA) communication system. The proposed MUD jointly realizes tap-length adjustment, adaptive combining, and multiuser detection. In contrast to the existing methods, the proposed detector can adaptively combine the received signals from different nodes at destination, and does not need the assumption that full and perfect channel state information (CSI) of all the links at the receiver is known. Moreover, the proposed detector can adaptively adjust the tap coefficient vector and tap-length of each branch according to the specific channel profile of each branch. Simulation results validate the feasibility and show the advantages of the proposed detector against existing counterparts. [ABSTRACT FROM AUTHOR]

Convergence speed and steady-state performance are important factors to estimate the variable tap-length algorithm. The step-size of tap-length (ST) and the error width considerably impact the performance of the fractional variable tap-length algorithm. A mixed fractional variable tap-length algorithm that simultaneously applies time-varying ST and error width strategies is presented in this paper. The ST and the error width are analyzed in detail. This method uses the same variable to control both aspects, so their changes are relatively consistent. The parameters have strong adaptive ability at different stages. The proposed algorithm can solve some defects of the previous algorithms, including slow convergence rate, weak steady-state performance and robustness. A novel mean square deviation (MSD) gradient experiment is performed. Experimental results show good agreement with the theoretical analysis.

In this work, a variable-tap length, variable step normalized least mean square algorithm with variable error spacing is proposed. The algorithm finds the optimized tap-length that best balances the complexity and steady state performance in linear adaptive filters. The design provides a systematic procedure with mathematical analysis to select the variable key parameters that affect the structure adaptation. The proposed structure adaptation algorithm maintains a trade-off between the mean square error and convergence speed. A sliding window weight update method is presented along with the tap-length learning algorithm to reduce the structural as well as computational complexity. Guidelines for parameter selection to formulate the optimum tap-length in correspondence with the designed algorithm are shown and assumptions are specified. The proposed algorithm has performed better than the existing fractional tap-length learning methods for both low and high noise conditions. This is achieved because of the unique method adopted in this paper to set dynamic system independent parameters instead of predefined fixed settings. [ABSTRACT FROM AUTHOR]

A variable tap-length LMS algorithm based on adaptive parameters for tapped-delay-line (TDL) structure adaption is proposed in this paper. The objective of this work is to overcome some drawbacks of previous variable tap-length adaptive filter algorithms, including slow convergence speed and high sensitivity to noise. Novel tap-length iteration equation based on adaptive parameters and arctangent-limited value is proposed. The values of adaptive parameters can automatically change according to the filter state, which not only improves the convergence speed but also reduces the steady-state error. The arctangent-limited value can effectively reduce the instantaneous estimated error, which enhances the robustness to noise. Computer simulations are conducted to verify the performance of proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: An improved variable tap-length LMS algorithm is proposed in this paper. As compared with the original fractional tap-length LMS (FT-LMS) algorithm, the proposed algorithm can obtain both a fast convergence rate and a small steady-state error of the tap-length. Algorithm analysis is given which also provides a guideline for the parameter choice of the proposed algorithm. Simulations are performed under low noise and high noise conditions with time varying impulse responses. All simulation results show the advantages of the proposed algorithm as compared with the FT-LMS algorithm, and confirm the algorithm analysis. [Copyright &y& Elsevier]

In teleconferencing and communication systems, the use of Loudspeaker Enclosure Microphone device leads to undesired echoes. To reduce these echoes, an acoustic echo canceller is used. In this paper, we propose a Variable Tap-length Non-Parametric Variable Step-Size NLMS (VT-NPVSS-NLMS) algorithm based on adaptive filtering for acoustic echo cancellation. The step-size is adjusted without the need for tuning too many parameters, using only the square of the average autocorrelation of a priori and a posteriori estimates of error. Moreover, the tap-length is varied to facilitate a high convergence speed and a small bias in tap-length from the optimum length. Hence, such a combination of the variable step-size algorithm with a variable tap-length provides faster convergence and reduced steady-state mean square error. The performance of the proposed algorithm is evaluated in terms of steady-state and transient mean square error. The advantages of the proposed algorithm, as compared to other adaptive algorithms, are presented using simulation results. From the results, we infer that for the VT-NPVSS-NLMS, the convergence speed is increased and the steady-state mean square error is reduced when compared with the conventional NLMS and the variable-step-size NLMS algorithm with a fixed tap-length. [ABSTRACT FROM AUTHOR]