Performance study of LMS based adaptive algorithms for unknown system identification (original) (raw)
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Analysis and Simulation of System Identification Based on LMS Adaptive Filtering Algorithm
This paper presents Adaptive Filtering and the least mean square algorithm which is widely used in Adaptive system. It realized the model and simulation of system identification Based on LMS algorithm by matlab and simulation. It can be seen that the adaptive FIR filter can simulation the unknown system well. Thus it can be got the system function of the unknown system through the parameters of the adaptive FIR filter and It can be carried out the function of the same hardware reconfiguration of the unknown system.
Comparative Analysis of various Adaptive Filtering Algorithms for Adaptive System Identification
System identification is one of the most interesting applications for adaptive filters, for this dissertation provides a comparison of LMS, VSSLMS,NLMS and TDLMS adaptive algorithms. This process provided the best suitable algorithm for usage in adaptive filters for system identification. This technique Based on the error signal, where filter's coefficients are updated and corrected, in order to adapt, so the output signal has the same values as the reference signal. Its applications include echo cancellation, channel equalization, interference cancellation, and so forth. Simulation results show that the proposed algorithms outperform the standard NLMS and TDLMS algorithms in both convergence rate and steady-state performance for sparse systems identification.
Optimization of LMS Algorithm for System Identification
ArXiv, 2017
An adaptive filter is defined as a digital filter that has the capability of self adjusting its transfer function under the control of some optimizing algorithms. Most common optimizing algorithms are Least Mean Square (LMS) and Recursive Least Square (RLS). Although RLS algorithm perform superior to LMS algorithm, it has very high computational complexity so not useful in most of the practical scenario. So most feasible choice of the adaptive filtering algorithm is the LMS algorithm including its various variants. The LMS algorithm uses transversal FIR filter as underlying digital filter. This paper is based on implementation and optimization of LMS algorithm for the application of unknown system identification. Keywords- Adaptive Filtering, LMS Algorithm, Optimization, System Identification, MATLAB
Unknown System Identification using LMS Algorithm
2016
An adaptive filter is a digital filter that self adjusts its transfer function according to an optimizing algorithm which is most frequently Least Mean Square (LMS) algorithm. Due to the complexity of adaptive filtering most digital filters are FIR filter. There are numerous applications of adaptive filters like noise cancellations, echo cancellation, system modelling and identification, inverse system modelling, adaptive beam-forming etc. In this research article, adaptive LMS algorithm has been used for unknown system identification. The system identification is a category of adaptive filtering which find its numerous applications in diverse field like communication, image processing, speech processing etc.
Adaptive system identification using the normalized least mean fourth algorithm
1998
In this work we propose a novel scheme for adaptive system identication. This scheme is based on a normalized version of the leastmean fourth (LMF) algorithm. In contrast to the LMF algorithm, this new normalized version of the LMF algorithm is found to be independent of the input sequence autocorrelation matrix. It is also found that it converges faster than the normalized least mean square (NLMS) algorithm for the lowest steady-state error reached by the NLMS algorithm. Simulation results conrm the superior performance of the new algorithm.
A square root normalized LMS algorithm for adaptive identification with non-stationary inputs
Journal of Communications and Networks, 2007
The conventional normalized least mean square (NLMS) algorithm is the most widely used for adaptive identification within a non-stationary input context. The convergence of the NLMS algorithm is independent of environmental changes. However, its steady state performance is impaired during input sequences with low dynamics. In this paper, we propose a new NLMS algorithm which is, in the steady state, insensitive to the time variations of the input dynamics. The square soot (SR)-NLMS algorithm is based on a normalization of the LMS adaptive filter input by the Euclidean norm of the tap-input. The tap-input power of the SR-NLMS adaptive filter is then equal to one even during sequences with low dynamics. Therefore, the amplification of the observation noise power by the tap-input power is cancelled in the misadjustment time evolution. The harmful effect of the low dynamics input sequences, on the steady state performance of the LMS adaptive filter are then reduced. In addition, the square root normalized input is more stationary than the base input. Therefore, the robustness of LMS adaptive filter with respect to the input non stationarity is enhanced. A performance analysis of the first-and the second-order statistic behavior of the proposed SR-NLMS adaptive filter is carried out. In particular, an analytical expression of the step size ensuring stability and mean convergence is derived. In addition, the results of an experimental study demonstrating the good performance of the SR-NLMS algorithm are given. A comparison of these results with those obtained from a standard NLMS algorithm, is performed. It is shown that, within a nonstationary input context, the SR-NLMS algorithm exhibits better performance than the NLMS algorithm.
System Identification through RLS Adaptive Filters
Ijca Proceedings on National Conference on Innovative Paradigms in Engineering and Technology, 2012
System Identification is one of the most interesting applications for adaptive filters, especially for the Least Mean Square algorithm, due to its robustness and calculus simplicity. Based on the error signal, the filter's coefficients are updated and corrected, in order to adapt, so the output signal has the same values as the reference signal. The application enables remarkable developments and research, creating an opportunity for automation and prediction. In this paper we focus on parameters of system identification by changing design parameters such as forgetting factor, filter length, initial value of filter weight and input variance of filter through MATLAB/SIMULINK Software.
American Journal of Engineering and Applied Sciences
Problem statement: This study introduced a variable step-size Least Mean-Square (LMS) algorithm in which the step-size is dependent on the Euclidian vector norm of the system output error. The error vector includes the last L values of the error, where L is a parameter to be chosen properly together with other parameters in the proposed algorithm to achieve a trade-off between speed of convergence and misadjustment. Approach: The performance of the algorithm was analyzed, simulated and compared to the Normalized LMS (NLMS) algorithm in several input environments. Results: Computer simulation results demonstrated substantial improvements in the speed of convergence of the proposed algorithms over other algorithms in stationary environments for the same small level of misadjustment. In addition, the proposed algorithm shows superior tracking capability when the system is subjected to an abrupt disturbance. Conclusion: For nonstationary environments, the algorithm performs as well NLMS and other variable step-size algorithms.
Optimized LMS algorithm for system identification and noise cancellation
Journal of Intelligent Systems, 2021
Optimization by definition is the action of making most effective or the best use of a resource or situation and that is required almost in every field of engineering. In this work, the optimization of Least Mean square (LMS) algorithm is carried out with the help of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Efforts have been made to find out the advantages and disadvantages of combining gradient based (LMS) algorithm with Swarm Intelligence SI (ACO, PSO). This optimization of LMS algorithm will help us in further extending the uses of adaptive filtering to the system having multi-model error surface that is still a gray area of adaptive filtering. Because the available version of LMS algorithm that plays an important role in adaptive filtering is a gradient based algorithm, that get stuck at the local minima of system with multi-model error surface considering it global minima, resulting in an non-optimized convergence. By virtue of the proposed method we...
Robust least mean square adaptive FIR filter algorithm
Iee Proceedings-vision Image and Signal Processing, 2001
The authors propose a new robust adaptive FIR filter algorithm for system identification applications based on a statistical approach named the M estimation. The proposed robust least mean square algorithm differs from the conventional one by the insertion of a suitably chosen nonlinear transformation of the prediction residuals. The effect of nonlinearity is to assign less weight to a small portion of large residuals so that the impulsive noise in the desired filter response will not greatly influence the final parameter estimates. The convergence of the parameter estimates is established theoretically using the ordinary differential equation approach. The feasibility of the approach is demonstrated with simulations.