The Precompression Processing of LMS Algorithm in Noise Elimination

In this study, the authors propose a novel precompression processing (PCP) of the least mean squares (LMS) algorithm based on a regulator factor. The novelty of the PCP algorithm is that the compressed input signals vary from each other on different components at each iteration. The input signal of the improved LMS algorithm is precompressed based on the regulator factor. The precompressed input signal is not only related to the regulator factor α and the current value of the input signal at each iteration but also related to the amplitude of the input signal before this iteration. The improved algorithm can eliminate the influence of input signal mutation on the filter performance. In the numerical simulations, we compare the improved LMS algorithm and NLMS algorithm in the cases of normal input signal and input signal with mutation and the influence of different regulator factors on the noise elimination. Results show that the PCP algorithm has good noise elimination effect when the input signal changes abruptly and the regulator factor α = 0.01 can meet the requirements.

Download Full-text

A Novel Adaptive LMS Algorithm with Genetic Search Capabilities for System Identification of Adaptive FIR and IIR Filters

Information ◽

10.3390/info10050176 ◽

2019 ◽

Vol 10 (5) ◽

pp. 176 ◽

Cited By ~ 1

Author(s):

Amjad J. Humaidi ◽

Ibraheem Kasim Ibraheem ◽

Ahmed R. Ajel

Keyword(s):

System Identification ◽

Input Signal ◽

Digital Filters ◽

Learning Algorithm ◽

Search Space ◽

Lms Algorithm ◽

Adaptation Algorithm ◽

Genetic Search ◽

Mutation Operators ◽

Input Signals

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.

Download Full-text

Pulse-Width Modulated Amplifier for DC Servo System and Its Matlab Simulation

The Open Electrical & Electronic Engineering Journal ◽

10.2174/1874129001509010625 ◽

2015 ◽

Vol 9 (1) ◽

pp. 625-631

Author(s):

Ma Xiaocheng ◽

Zhang Haotian ◽

Cheng Yiqing ◽

Zhu Lina ◽

Wu Dan

Keyword(s):

Mathematical Model ◽

Transfer Function ◽

Input Signal ◽

Pulse Width ◽

Rotation Speed ◽

Servo Motor ◽

Matlab Simulation ◽

Pulse Width Modulated ◽

Signal Changes ◽

The Relationship

This paper introduces a mathematical model for Pulse-Width Modulated Amplifier for DC Servo Motor. The relationship between pulse-width modulated (PWM) signal and reference rotation speed is specified, and a general model of motor represented by transfer function is also put forward. When the input signal changes, the rotation speed of the servo motor will change accordingly. By changing zeros and poles, transient performance of this system is discussed in detail, and optimal ranges of the parameters is recommended at the end of discussion.

Download Full-text

Adaptive method to predict and track unknown system behaviors using RLS and LMS algorithms

Facta universitatis - series Electronics and Energetics ◽

10.2298/fuee2101133t ◽

2021 ◽

Vol 34 (1) ◽

pp. 133-140

Author(s):

Teimour Tajdari

Keyword(s):

Input Signal ◽

Output Signal ◽

Adaptive Filters ◽

Adaptive Method ◽

Lms Algorithm ◽

Recursive Least Squares ◽

Least Mean Square ◽

Filter Output ◽

System Output ◽

Unknown System

This study investigates the ability of recursive least squares (RLS) and least mean square (LMS) adaptive filtering algorithms to predict and quickly track unknown systems. Tracking unknown system behavior is important if there are other parallel systems that must follow exactly the same behavior at the same time. The adaptive algorithm can correct the filter coefficients according to changes in unknown system parameters to minimize errors between the filter output and the system output for the same input signal. The RLS and LMS algorithms were designed and then examined separately, giving them a similar input signal that was given to the unknown system. The difference between the system output signal and the adaptive filter output signal showed the performance of each filter when identifying an unknown system. The two adaptive filters were able to track the behavior of the system, but each showed certain advantages over the other. The RLS algorithm had the advantage of faster convergence and fewer steady-state errors than the LMS algorithm, but the LMS algorithm had the advantage of less computational complexity.

Download Full-text

The Application of Grey Prediction on the Fast Control Power Supply in EAST

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1739 ◽

2013 ◽

Vol 427-429 ◽

pp. 1739-1742

Author(s):

Hai Hong Huang ◽

Jia Miao ◽

Hai Xin Wang ◽

Feng Feng Wang

Keyword(s):

Input Signal ◽

Power Supply ◽

Grey Theory ◽

Grey Prediction ◽

Grey System ◽

Control Power ◽

Input Signals ◽

Simulation And Experiment ◽

One Step ◽

Fast Control

Based on the grey theory, a novel model is built to predict the input signal of fast control power supply used in Experimental Advanced Superconducting Tokamak (EAST). The model can be used as online metabolic grey filtering and one-step prediction of different input signals. Results of simulation and experiment show that the predicting algorithm based on the grey system model can predict the input signal primarily.

Download Full-text

On a Certain Class of Expanding Systems

Metrology and Measurement Systems ◽

10.2478/v10178-010-0026-4 ◽

2010 ◽

Vol 17 (2) ◽

pp. 299-306

Author(s):

Adam Żuchowski

Keyword(s):

Transfer Function ◽

Input Signal ◽

Initial Conditions ◽

System Optimization ◽

Gain Coefficient ◽

Time Intervals ◽

System Parameters ◽

Linear Dynamic ◽

Subsequent Time ◽

Input Signals

On a Certain Class of Expanding Systems The interesting properties of a class of expanding systems are discussed. The operation of the considered systems can be described as follows: the input signal is processed by a linear dynamic converter in subsequent time intervals, each of them is equal to Ti. Processing starts at the moments n · Ti, always after zeroing of converter initial conditions. For smooth input signals and a given transfer function of the converter one can suitably choose Ti and the gain coefficient in order to realize the postulated linear operations on input signals, which is quite different comparing it to the operation realized by the converter. The errors of postulated operations are mainly caused by non-smooth components of the input signal. The principles for choice of system parameters and rules for system optimization are presented in the paper. The referring examples are attached too.

Download Full-text

Application of Dynamic Input Shaping for a Dual-Mirror System

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4053188 ◽

2021 ◽

Author(s):

Alicia Dautt-Silva ◽

Raymond de Callafon

Keyword(s):

Input Signal ◽

Inverse Kinematic ◽

Open Loop ◽

Mirror System ◽

Image Motion ◽

Step Response ◽

Input Shaping ◽

Input Signals ◽

Compensation Device ◽

Operating Constraints

Abstract The task of trajectory planning for a dual-mirror optical pointing system greatly benefits from carefully designed dynamic input signals. This paper summarizes the application of multivariable input shaping (IS) for a dual-mirror system, starting from initial open-loop step-response data. The optical pointing system presented consists of two Fast Steering Mirrors (FSM) for which dynamically coupled input signals are designed, while adhering to mechanical and input signal constraints. For the solution, the planned trajectories for the dual-mirrors are determined via (inverse) kinematic analysis. A linear program (LP) problem is used to compute the dynamic input signal for each of the FSMs, with one of the mirrors acting as an image motion compensation device that guarantees tracking of a planned trajectory within a specified accuracy and the operating constraints of the FSMs.

Download Full-text

Optimal preconditioned regularization of least mean squares algorithm for robust online learning1

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-191728 ◽

2020 ◽

Vol 39 (3) ◽

pp. 3375-3385

Author(s):

Shazia Javed ◽

Noor Atinah Ahmad

Keyword(s):

Computational Cost ◽

Lms Algorithm ◽

Step Size ◽

State Behavior ◽

Autocorrelation Matrix ◽

Input Signals ◽

Unknown System ◽

Least Mean Squares Algorithm ◽

Adaptive Step ◽

Low Computational Cost

Despite its low computational cost, and steady state behavior, some well known drawbacks of the least means squares (LMS) algorithm are: slow rate of convergence and unstable behaviour for ill conditioned autocorrelation matrices of input signals. Several modified algorithms have been presented with better convergence speed, however most of these algorithms are expensive in terms of computational cost and time, and sometimes deviate from optimal Wiener solution that results in a biased solution of online estimation problem. In this paper, the inverse Cholesky factor of the input autocorrelation matrix is optimized to pre-whiten input signals and improve the robustness of the LMS algorithm. Furthermore, in order to have an unbiased solution, mean squares deviation (MSD) is minimized by improving convergence in misalignment. This is done by regularizing step-size adaptively in each iteration that helps in developing a highly efficient optimal preconditioned regularized LMS (OPRLMS) algorithm with adaptive step-size. Comparison of OPRLMS algorithm with other LMS based algorithms is given for unknown system identification and noise cancelation from ECG signal, that results in preference of the proposed algorithm over the other variants of LMS algorithm.

Download Full-text

Study on Anti-Noise Materials with Multi-Level Filter Based on the Adaptive Noise Cancellation System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.886.390 ◽

2014 ◽

Vol 886 ◽

pp. 390-393

Author(s):

Jing Mo ◽

Wei He ◽

Dan Su ◽

Jing Wei Wu

Keyword(s):

Noise Cancellation ◽

Noise Signal ◽

Lms Algorithm ◽

Variable Step Size ◽

Adaptive Noise Cancellation ◽

Step Size ◽

Filter Performance ◽

Multi Level ◽

Adaptive Noise ◽

Filtering Effect

It presents the Multi-level filters idea of the adaptive noise cancellation system based on the fact that the adaptive noise cancellation system cant filter out noise signal completely. According to the linear combination and the variable step-size LMS algorithm, it analyzes the effects of the two level filters. Theory analyzing and simulation results prove that the multi-level filter can get a better the filtering effect than the one-filter, which improves the filter performance in terms of the fast convergence speed, tracking speed and the low maladjustment error. And the anti-noise materials with multi-level filter based on the adaptive noise cancellation system has the good de-noising ability of noisy signals.

Download Full-text