检索结果-内蒙古大学图书馆

lmf algorithm Based on Hyper-Chaos for the Solving of Forward Displacement in a Parallel Robot Mechanism

INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2013年第1期10卷

作者： Luo, Youxin Liu, Qiyuan Che, Xiaoyi He, ZheMing Hunan Univ Arts & Sci Coll Mech Engn Changde Peoples R China

The forward displacement problem of the parallel robot mechanism can be converted to nonlinear equations in order to find solutions, but it is very difficult to find all solutions because of the strong coupling of the nonlinear equations. Given the problems of having only one solution and sometimes no convergence when solving the nonlinear equations with the Newton method and quasi-Newton method, a lmf algorithm based on hyper-chaos is proposed to solve the general 6-6 platform parallel mechanism, based on the combination of the hyper-chaos system and the Levenberg-Marquardt-Fletcher (abbreviated as lmf) algorithm. This method uses the hyper-chaotic system to produce the initial point of the lmf algorithm, and takes advantage of the characteristics of the chaotic sequence and the lmf algorithm to find all the real solutions. The numerical example shows that the new method has some characteristics such as that it runs in the initial value range, it has fast convergence, it finds all the real solutions that can be found, and it proves the correctness and validity. It provides a new approach to mechanism design.

关键词： Hyper-Chaos System Parallel Mechanism lmf algorithm Nonlinear Equations

来源：评论

学校读者我要写书评

暂无评论

A variable-parameter normalized mixed-norm (VPNMN) adaptive algorithm

引用

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2012年第1期2012卷 1页

作者： Zerguine, Azzedine King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

Since both the least mean-square (LMS) and least mean-fourth (lmf) algorithms suffer individually from the problem of eigenvalue spread, so will the mixed-norm LMS-lmf algorithm. Therefore, to overcome this problem for the mixed-norm LMS-lmf, we are adopting here the same technique of normalization (normalizing with the power of the input) that was successfully used with the LMS and lmf separately. Consequently a new normalized variable-parameter mixed-norm (VPNMN) adaptive algorithm is proposed in this study. This algorithm is derived by exploiting a time-varying mixing parameter in the traditional mixed-norm LMS-lmf weight update equation. The time-varying mixing parameter is adjusted according to a well-known technique used in the adaptation of the step-size parameter of the LMS algorithm. In order to study the theoretical aspects of the proposed VPNMN adaptive algorithm, our study also addresses its convergence analysis, and assesses its performance using the concept of energy conservation. Extensive simulation results corroborate our theoretical findings and show that a substantial improvement, in both convergence time and steady-state error, can be obtained with the proposed algorithm. Finally, the VPNMN algorithm proved its usefulness in a noise cancellation application where it showed its superiority over the normalized least-mean square algorithm.

关键词： LMS algorithm NLMS algorithm lmf algorithm mixed-norm algorithms normalized mixed-norm algorithms

来源：评论

学校读者我要写书评

暂无评论

Efficient Signal Conditioning Techniques for Brain Activity in Remote Health Monitoring Network

引用

IEEE SENSORS JOURNAL 2013年第9期13卷 3276-3283页

作者： Karthik, Gundlapalli Venkata Sai Fathima, Shaik Yasmin Rahman, Muhammad Zia Ur Ahamed, Shaik Rafi Lay-Ekuakille, Aime Coimbatore Inst Technol Dept Elect & Elect Engn Coimbatore 641013 Tamil Nadu India Vasireddy Venkatadri Inst Technol Dept Elect & Commun Engn Guntur 522002 India KL Univ Dept Elect & Commun Engn Guntur 522502 India Indian Inst Technol Dept Elect & Commun Engn Gauhati 781039 India Univ Salento Dept Innovating Engn I-73100 Lecce Italy

This paper proposes several efficient and less complex signal conditioning algorithms for brain signal enhancement in remote healthcare monitoring applications. In clinical environment during electroencephalogram (EEG) recording, several artifacts encounter and mask tiny features underlying brain wave activity. Especially in remote clinical monitoring, low computational complexity filters are desirable. Hence, in our paper, we propose various efficient and computationally simple adaptive noise cancelers for EEG enhancement. These schemes mostly employ simple addition and shift operations, and achieve considerable speed over the other conventional realizations. We have tested the proposed implementations on real brain waves recorded using emotive EEG system. Our experiments show that the proposed realization gives better performance compared with existing realizations in terms of signal to noise ratio, computational complexity, convergence rate, excess mean square error, misadjustment, and coherence.

关键词： Adaptive noise cancelers artifact brain wave signal conditioning remote health monitoring lmf algorithm

来源：评论

学校读者我要写书评

暂无评论

Robust FxLMS algorithms with improved convergence performance

引用

IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1998年第1期6卷 78-85页

作者： Rupp, M Sayed, AH Lucent Technol Wireless TEchnol Res Dept Holmdel NJ 07733 USA

This paper proposes two modifications of the filtered x least mean squares (FxLMS) algorithm with improved convergence behavior albeit at the same computational cost of 2M operations per time step as the original FxLMS update, The paper further introduces a generalized FxLMS recursion and establishes that the various algorithms are all of filtered-error form, A choice of the stepsize parameter that guarantees faster convergence and conditions for robustness are also derived, Several simulation results are included to illustrate the discussions.

关键词： active noise control feedback analyses lmf algorithm l(2)-stability robustness

来源：评论

学校读者我要写书评

暂无评论

Convergence and steady-state analysis of the normalized least mean fourth algorithm

引用

DIGITAL SIGNAL PROCESSING 2007年第1期17卷 17-31页

作者： Zerguine, Azzedine King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

The normalized least mean-fourth (Nlmf) algorithm is presented in this work and shown to have potentially faster convergence. Unlike the lmf algorithm, the convergence behavior of the Nlmf algorithm is independent of the input data correlation statistics. Sufficient conditions for the Nlmf algorithm convergence in the mean are obtained and an analysis of the steady-state performance is carried out with a new approach. The latter uses the concept of feedback and bypasses the need for working directly with the weight error covariance matrix. Simulation results obtained in a system identification scenario confirms the theoretical predictions on performance of the Nlmf algorithm. (c) 2006 Elsevier Inc. All rights reserved.

关键词： LMS algorithm NLMS algorithm lmf algorithm Nlmf algorithm

来源：评论

学校读者我要写书评

暂无评论

Channel equalization using simplified least mean-fourth algorithm

引用

DIGITAL SIGNAL PROCESSING 2011年第3期21卷 447-465页

作者： Otaru, Musa U. Zerguine, Azzedine Cheded, Lahouari King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia MTN Nigeria Commun Ltd Special Telecom Projects Lagos Nigeria King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran 31261 Saudi Arabia

The steady flow of new research results and developments in the field of adaptive equalization that was witnessed for at least the last four decades is clearly evidenced by the many footprints of success it left behind and shows no sign of ending. The thrust of research and implementation in this field is mainly powered by the use of the well-known mean-square cost function upon which relies the ubiquitous least-mean square (LMS) algorithm. However, such an algorithm is well-known to lead to sub-optimal solutions in the real world that is largely dominated by non-Gaussian interference signals. The use of a non-mean-square cost function would successfully tackle these types of interference signals but would invariably involve a higher computational cost. To address these important practical issues, this paper proposes a new adaptive equalization technique that combines both the least-mean-fourth (lmf) algorithm, which is governed by a non-mean-square cost function, with a power-of-two quantizer (PTQ) in the coefficient update process, which greatly reduces the computational cost involved and which therefore makes the proposed technique applicable to time-varying environments. This paper not only elaborates on the basic idea behind the proposed technique but also defines the necessary assumptions and provides a thorough statistical performance analysis (including a study of the convergence behavior) of the combined algorithm lmf-PTQ that is at the core of the proposed technique. An extensive simulation work was carried out and, showed that the theoretical predictions are very well substantiated. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Adaptive equalization lmf algorithm Power-of-two quantizer

来源：评论

学校读者我要写书评

暂无评论

New insights into the normalization of the least mean fourth algorithm

引用

SIGNAL IMAGE AND VIDEO PROCESSING 2013年第2期7卷 255-262页

作者： Eweda, Eweda Zerguine, Azzedine Ajman Univ Sci & Technol Dept Elect Engn Ajman U Arab Emirates King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

The available normalization of the least mean fourth algorithm is investigated. It is shown that that normalization does not protect the algorithm from divergence when the input power of the adaptive filter increases. The reason of this drawback is that the normalization is done by dividing the weight vector update term by the squared norm of the regressor, while the update term is a fourth order polynomial in the regressor. The paper presents a normalized lmf algorithm that is based on dividing the weight vector update term by the fourth power of the norm of the regressor. This normalization protects the algorithm from divergence when the input power increases. An approximate stability step-size bound of the proposed algorithm is derived. The step-size bound depends on the weight initialization, while it does not depend on the input power of the adaptive filter for non-small signal-to-noise ratio. Simulation results support the analytical results of the paper.

关键词： Adaptive filtering lmf algorithm Normalized lmf algorithm

来源：评论

学校读者我要写书评

暂无评论

A Unified Performance Analysis of the Family of Normalized Least Mean algorithms

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2014年第10期39卷 7145-7157页

作者： Moinuddin, Muhammad Zerguine, Azzedine King Abdulaziz Univ Elect & Comp Engn Dept Jeddah 21859 Saudi Arabia King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst CEIES Jeddah 21859 Saudi Arabia King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

This work presents a unified performance analysis of the family of normalized least mean (NLM) algorithms under very weak assumptions. The key feature of the analysis is based on a recently proposed performance measure called effective weight deviation vector. The name is so given as it is the only component that contributes to the excess estimation error of the adaptive filter. Using this novel concept, both the steady-state analysis and the tracking analysis are presented with a unified framework for the whole family of the NLM algorithms. Thus, the derived results are valid for any normalized stochastic gradient algorithm minimizing 2pth power of the error where p is an integer value. The novelty of the analysis resides in the fact that it does not impose restrictions on the dependence between input successive regressors, the dependence among input regressor elements, the length of the adaptive filter, the distribution of noise, and filter's input. Moreover, this approach is not limited to only small step-size value, and therefore, the analysis is valid for all the values of the step size in the stable range of the NLM algorithms. Furthermore, in this analysis, both stationary and non-stationary input signal and plant scenarios are considered. Consequently, asymptotic time-averaged convergence for the mean-squared effective weight deviation, mean absolute excess estimation error, and the mean-square excess estimation error for the NLM algorithm are established for both constant and time-varying plants. Finally, a number of simulation results are carried out to corroborate the theoretical findings.

关键词： LMS algorithm lmf algorithm NLMS algorithm Nlmf algorithm

来源：评论

学校读者我要写书评

暂无评论

Comments on "Efficient Signal Conditioning Techniques for Brain Activity in Remote Health Monitoring Network" Authors' Reply

引用

IEEE SENSORS JOURNAL 2015年第9期15卷 5351-5351页

作者： Karthik, Gundllapalli Venkata Sai Fathima, Shaik Yasmin Rahman, Muhammad Zia Ur Shaik, Rafi Ahamed Lay-Ekuakille, Aime Honeywell Technol Solut Lab Pvt Ltd Hyderabad 500034 Andhra Pradesh India RVR & JC Coll Engn Dept Elect & Commun Engn Guntur 522019 India KL Univ Dept Elect & Commun Engn Guntur 522502 India Indian Inst Technol Guwahati Dept Elect & Commun Engn Gauhati 781039 India Univ Salento Dept Innovating Engn I-73100 Lecce Italy

In this paper, we are concerned with the last mean fourth (lmf) algorithm and its variants. Using lmf variants, we have implemented several adaptive noise cancelers (ANCs) to enhance EEG signals those suitable for remote health care monitoring applications. There are no misconceptions in this paper. In this paper, we only concentrated on lmf-based algorithms. Based on several variations, we have implemented various ANCs for EEG signal enhancements. However, when we are applying signum function to error signal, qualitatively there is no difference between sign least mean square, sign sign least mean square and sign least mean fourth, sign sign least mean fourth, respectively. As the analyzed entity is lmf based algorithms, we do not compare the implementations with LMS counter parts.

关键词： Adaptive noise cancelers artifact brain wave signal conditioning remote health monitoring lmf algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：