An Improved LMS_Newton Algorithm and its Analysis

2011 ◽  
Vol 130-134 ◽  
pp. 458-462 ◽  
Author(s):  
Rui Mei Lian
Keyword(s):  
Computer Simulation ◽  
Steady State ◽  
Theoretical Analysis ◽  
Functional Relation ◽  
Convergence Speed ◽  
Error Signal ◽  
Nonlinear Functional ◽  
Newton Algorithm ◽  
Step Factor ◽  
Better Than

An amended LMS Newton algorithm is proposed which create the nonlinear functional relation of the step factor μ and the error signal e (n). And the next we discuss the factors in the amended algorithm and the performance in the different IP environment. The algorithm is simple and easy to be implemented, Theoretical analysis and computer simulation show that the properties of the algorithm such as convergence speed, and steady state error ate better than those of SVSLMS algorithm.

A New Variable Step Size LMS Adaptive Filtering Algorithm and its Simulations

2014 ◽  
Vol 513-517 ◽  
pp. 3736-3739 ◽  
Author(s):  
Xue Li Wu ◽  
Zi Zhong Tan ◽  
Liang Gao
Keyword(s):  
Steady State ◽  
Adaptive Filtering ◽  
Functional Relationship ◽  
Convergence Speed ◽  
Variable Step Size ◽  
Error Signal ◽  
Step Size ◽  
Variable Step ◽  
The Absolute ◽  
State Error

. Aiming at the disadvantage of the variable step size LMS adaptive filtering algorithms' convergence speed contradicting its steady-state error, a novel non-liner functional relationship between μ (n) and error signal e (n) was established. On the basis of the functional relationship, a new algorithm of variable step size LMS adaptive filtering was presented. The step size factor of the new algorithm is adjusted by the absolute value of the product of the current and former errors. It also uses the absolute estimation error compensation terms disturbance to speed up the convergence of adaptive filter tap weight vector. At the same time, the algorithm considers the relationship between step length of the last iteration and the former M error signal. As a result the algorithm has higher convergence characteristic and small steady state error. The theoretical analysis and simulation results show that the new algorithm has faster convergence speed, lower steady state error and better performance of noise suppression, also show the overall performance of this algorithm exceeds some others condition.

Active control for vehicle interior noise using the improved iterative variable step-size and variable tap-length LMS algorithms

2019 ◽  
Vol 67 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Ningning Liu ◽  
Yuedong Sun ◽  
Yansong Wang ◽  
Hui Guo ◽  
Bin Gao ◽  
...  
Keyword(s):  
Steady State ◽  
Noise Control ◽  
Convergence Speed ◽  
Interior Noise ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Vehicle Interior ◽  
Vehicle Interior Noise ◽  
Variable Step

Active noise control (ANC) is used to reduce undesirable noise, particularly at low frequencies. There are many algorithms based on the least mean square (LMS) algorithm, such as the filtered-x LMS (FxLMS) algorithm, which have been widely used for ANC systems. However, the LMS algorithm cannot balance convergence speed and steady-state error due to the fixed step size and tap length. Accordingly, in this article, two improved LMS algorithms, namely, the iterative variable step-size LMS (IVS-LMS) and the variable tap-length LMS (VT-LMS), are proposed for active vehicle interior noise control. The interior noises of a sample vehicle are measured and thereby their frequency characteristics. Results show that the sound energy of noise is concentrated within a low-frequency range below 1000 Hz. The classical LMS, IVS-LMS and VT-LMS algorithms are applied to the measured noise signals. Results further suggest that the IVS-LMS and VT-LMS algorithms can better improve algorithmic performance for convergence speed and steady-state error compared with the classical LMS. The proposed algorithms could potentially be incorporated into other LMS-based algorithms (like the FxLMS) used in ANC systems for improving the ride comfort of a vehicle.

Chaos Glowworm Swarm Optimization Algorithm Based on Cloud Model for Face Recognition

2020 ◽  
Vol 34 (12) ◽  
pp. 2056009
Author(s):  
Guangyu Zhou ◽  
Aijia Ouyang ◽  
Yuming Xu
Keyword(s):  
Face Recognition ◽  
Cloud Model ◽  
Cuckoo Search ◽  
Global Optimum ◽  
Convergence Speed ◽  
Invasive Weed ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Better Than

To overcome the shortcomings of the basic glowworm swarm optimization (GSO) algorithm, such as low accuracy, slow convergence speed and easy to fall into local minima, chaos algorithm and cloud model algorithm are introduced to optimize the evolution mechanism of GSO, and a chaos GSO algorithm based on cloud model (CMCGSO) is proposed in the paper. The simulation results of benchmark function of global optimization show that the CMCGSO algorithm performs better than the cuckoo search (CS), invasive weed optimization (IWO), hybrid particle swarm optimization (HPSO), and chaos glowworm swarm optimization (CGSO) algorithm, and CMCGSO has the advantages of high accuracy, fast convergence speed and strong robustness to find the global optimum. Finally, the CMCGSO algorithm is used to solve the problem of face recognition, and the results are better than the methods from literatures.

Synthesis Design and Analysis of a Hybrid Controller for Robotic Arms

2016 ◽  
Author(s):  
Dan Zhang ◽  
Bin Wei
Keyword(s):  
Pid Controller ◽  
Adaptive Controller ◽  
Convergence Speed ◽  
Model Reference ◽  
Robotic Arms ◽  
Hybrid Controller ◽  
Comparison Results ◽  
Convergence Performance ◽  
Model Reference Adaptive ◽  
Better Than

In this paper, a hybrid controller for robotic arms is proposed and designed by combining a proportional-integral-derivative controller (PID) and a model reference adaptive controller (MRAC) in order to further improve the accuracy and joint convergence speed performance. The convergence performance of the PID controller, the model reference adaptive controller and the PID+MRAC hybrid controller for 1-DOF and 2-DOF manipulators are compared. The comparison results show that the convergence speed and its performance for the MRAC and the PID+MRAC controllers are better than that of the PID controller, and the convergence performance for the hybrid control is better than that of the MRAC control.

IMAGE PROCESSING VIA CNN GENES WITH VON NEUMANN NEIGHBORHOODS

2005 ◽  
Vol 15 (12) ◽  
pp. 3999-4006 ◽  
Author(s):  
FENG-JUAN CHEN ◽  
FANG-YUE CHEN ◽  
GUO-LONG HE
Keyword(s):  
Image Processing ◽  
Computer Simulation ◽  
Edge Detection ◽  
Vertical Line ◽  
Corner Detection ◽  
Line Detection ◽  
Von Neumann ◽  
Center Point ◽  
Better Than

Some image processing research are restudied via CNN genes with five variables, and this include edge detection, corner detection, center point extraction and horizontal-vertical line detection. Although they were implemented with nine variables, the results of computer simulation show that the effect with five variables is identical to or better than that with nine variables.

Dynamic Stability of an Impact System Connected With Rock Drilling

1969 ◽  
Vol 36 (4) ◽  
pp. 743-749 ◽  
Author(s):  
C. C. Fu
Keyword(s):  
Computer Simulation ◽  
Exact Solution ◽  
Steady State ◽  
Asymptotic Stability ◽  
Piecewise Linear ◽  
Steady State Solution ◽  
External Excitation ◽  
Rock Drilling ◽  
Impact System ◽  
Number Of Cycles

This paper deals with asymptotic stability of an analytically derived, synchronous as well as nonsynchronous, steady-state solution of an impact system which exhibits piecewise linear characteristics connected with rock drilling. The exact solution, which assumes one impact for a given number of cycles of the external excitation, is derived, its asymptotic stability is examined, and ranges of parameters are determined for which asymptotic stability is assured. The theoretically predicted stability or instability is verified by a digital computer simulation.

Design, analysis and modelling of a hybrid controller for serial robotic manipulators

Robotica ◽  
2016 ◽  
Vol 35 (9) ◽  
pp. 1888-1905 ◽  
Author(s):  
Dan Zhang ◽  
Bin Wei
Keyword(s):  
Pid Controller ◽  
Robotic Manipulators ◽  
Convergence Speed ◽  
Model Reference ◽  
Control Approach ◽  
Hybrid Controller ◽  
Comparison Results ◽  
Convergence Performance ◽  
Model Reference Adaptive ◽  
Better Than

SUMMARYWhen the end-effector of a robotic arm grasps different payload masses, the output of joint motion will vary. By using a model reference adaptive control approach, the payload variation effect can be solved. This paper describes the design for a hybrid controller for serial robotic manipulators by combining a PID controller and a model reference adaptive controller (MRAC) in order to further improve the accuracy and joint convergence speed performance. The convergence performance of the PID controller, the MRAC and the PID+MRAC hybrid controller for 1-DOF, 2-DOF and subsequently 3-DOF manipulators is compared. The comparison results show that the convergence speed and its performance for the MRAC and the PID+ MRAC controllers is better than that of the PID controller, and the convergence performance for the hybrid control is better than that of the MRAC control.

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