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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Yan, Zhengtong Zhang, Qunfei Shi, Wentao Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Shaanxi Peoples R China

ISBN: (纸本)9781538631423

In this paper, a variable step size LIVES algorithm was introduced to eliminate the coupling interference of torpedo active electromagnetic fuze system. The variable step size will increase or decrease according to the residual error, which can achieve a faster convergence rate, a smaller steady-state error and a better adaptability to the signal-to-interference ratio (SIR) than the fixed step size. The suppression performance of the step size function with different parameters is analyzed. Numerical results are presented by comparing the performance of the variable step size LMS algorithm with the fixed step size LMS algorithm, to demonstrate the effectiveness and robustness of the variable step size LMS algorithm.

关键词： Electromagnetic fuze coupling interference adaptive cancellation variable step size least mean square algorithm

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Diffusion adaptation through free space optical (FSO) wireless communication channels 4

Diffusion adaptation through free space optical (FSO) wirele...

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IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI)

作者： Aminfar, Amir Ghobadi, Changiz Amirani, Mehdi Chehel Urmia Univ Dept Elect Engn Orumiyeh Iran

ISBN: (纸本)9781538626405

free space optical (FSO) communication systems have gathered a lot of attention due to their irresistible features including faster data rates, cheap and easy implementation. Implementing distributed adaptive networks using this technology will increase the efficiency and speed of these networks. In this paper, we investigate one of the hindering problems of this application namely atmospheric turbulence. We model this turbulence with Log-normal and Gamma-Gamma distributions and study the effect of both of these models on the performance of diffusion adaptation strategies over adaptive networks. We analyze the effect of atmospheric turbulence along with noisy links among nodes for both Adapt then Combine (ATC) and Combine then adapt (CTA) diffusion algorithms and present the results in order to compare their impact. We present our simulation results with the mean square deviation (MSD) criteria. The results show that atmospheric turbulence degrade the performance of adaptive network severely and in the worst cases may avoid the convergence of the network.

关键词： Adaptive networks diffusion adaptation optical channels least mean square algorithm Gamma-Gamma and Log-normal distribution models

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RSSI Overboard Localization System for Safe Evacuation of Large Passengers Ships 13

RSSI Overboard Localization System for Safe Evacuation of La...

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13th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME) / 14th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Application to Circuit Design (SMACD)

作者： El Agroudy, Naglaa Georgiades, George Joram, Niko Ellinger, Frank Tech Univ Dresden Chair Circuit Design & Network Theory D-01062 Dresden Germany GGDedalos Technol Serv Ltd Nicosia Cyprus

ISBN: (纸本)9781509065080

This work presents an overboard localization system based on measuring the received signal strength indicator (RSSI) between smart lifejacket tags and one interrogator station mounted inside an unmanned aerial vehicle (UAV). Localization is based on weighted least mean squares (LMS) algorithm. Simulations that study the effect of the UAV search path on the localization accuracy are presented and it shows that the parallel tracks search path gives better localization results compared to other search paths. Measurements are carried out in a search area of 500 m x 350 m, where tags are localized with a mean error of 37.5 m. The measurement results show better localization accuracy compared to other RSSI based localization algorithms.

关键词： RSSI least mean square algorithm wireless sensor networks

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Research on Linear Evaluation Function Based on LMS algorithm 29

Research on Linear Evaluation Function Based on LMS Algorith...

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第29届中国控制与决策会议

作者： Haoran Zhang Shuqin Li Meng Ding Beijing Information Science & Technology University Computer School

ISBN: (纸本)9781509046584

Evaluation function is one of the core components of the computer game *** traditional way to build evaluation function is usually to use a number of features on the board extracted by human,according to experience and game rules,which gives a set of parameters,and design a static linear evaluation function to calculate the evaluation value of current *** paper proposes a method to dynamically adjust and optimize the parameters in the evaluation function by least mean squares,and implements a dynamic evaluation *** based on Surakarta chess show that the method of dynamic evaluation is effective.

关键词： Computer Game Surakarta Chess linear evaluation function LMS algorithm least mean square algorithm

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On implementation of adaptive bilinear filters for nonlinear active noise control

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APPLIED ACOUSTICS 2016年 106卷 122-128页

作者： Tan, Li Dong, Chen Du, Sidan Purdue Univ North Cent Dept Engn 1401 South US Highway 421 Westville IN 46391 USA Nanjing Univ Sch Elect Sci & Engn Nanjing 210046 Jiangsu Peoples R China

In this technical note, the simplified diagonal-structure bilinear filtered-X least mean square (SDBFXLMS) and channel-reduced diagonal-structure bilinear filtered-X least mean square (CRDBFXLMS) algorithms are proposed. Computational complexity for each proposed algorithm is analyzed to show the significant computational reduction in comparison with the diagonal-structure bilinear FXLMS (DBFXLMS) algorithm. For L = 15 (memory length of the bilinear filter), P = 2 (the corresponding number of the diagonal channels for the SDBFXLMS algorithm is L + 2P = 19 and the corresponding number of the diagonal channels for the CRDBFXLMS algorithm is 2P = 4), and M = 64 (memory length of the secondary path estimate), the SDBFXLMS algorithm achieves 45% and 40% reduction of multiplications and additions, respectively, while the CRDBFXLMS algorithm acquires 78% reduction of multiplications and 76% reduction of additions. Computer simulations validate the satisfied control performances of the proposed algorithms. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Active noise control (ANC) Adaptive filter Adaptive bilinear filter Diagonal structure least mean square algorithm

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Dynamic Error Analysis Method for Vibration Shape Reconstruction of Smart FBG Plate Structure

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SHOCK AND VIBRATION 2016年第Pt.1期2016卷 1-14页

作者： Zhang, Hesheng Zhu, Xiaojin Gao, Zhiyuan Liu, Kaining Li, Zhaowen Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China CASC Inst 804 Shanghai Acad Space Flight Technol Shanghai 201109 Peoples R China

Shape reconstruction of aerospace plate structure is an important issue for safe operation of aerospace vehicles. One way to achieve such reconstruction is by constructing smart fiber Bragg grating (FBG) plate structure with discrete distributed FBG sensor arrays using reconstruction algorithms in which error analysis of reconstruction algorithm is a key link. Considering that traditional error analysis methods can only deal with static data, a new dynamic data error analysis method are proposed based on LMS algorithm for shape reconstruction of smart FBG plate structure. Firstly, smart FBG structure and orthogonal curved network based reconstruction method is introduced. Then, a dynamic error analysis model is proposed for dynamic reconstruction error analysis. Thirdly, the parameter identification is done for the proposed dynamic error analysis model based on least mean square (LMS) algorithm. Finally, an experimental verification platform is constructed and experimental dynamic reconstruction analysis is done. Experimental results show that the dynamic characteristics of the reconstruction performance for plate structure can be obtained accurately based on the proposed dynamic error analysis method. The proposed method can also be used for other data acquisition systems and data processing systems as a general error analysis method.

关键词： Reconstruction algorithm Sensor arrays Parameter identification Error analysis dynamic error Safe handling shape reconfiguration Fiber Bragg gratings least mean square algorithm DATA ACQUISITION SYSTEMS plate structure

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A Hybrid least mean square/Sample Matrix Inversion algorithm Using Microstrip Antenna Array

A Hybrid Least Mean Square/Sample Matrix Inversion Algorithm...

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Science and Information Conference (SAI)

作者： Ali, Wael A. E. Hassan, Adel H. G. Arab Acad Sci & Technol Elect & Commun Engn Dept Alexandria Egypt

ISBN: (纸本)9780989319317

Adaptive beamforming algorithms is the kind of smart antenna algorithms that have the ability to direct the main beam toward the desired signal and to place a null in the direction of the interference signal. Those algorithms achieve the desired direction by adjusting weights "amplitude and phase" of the array pattern. The proposed hybrid algorithm is presented in this paper that combines between two algorithms to improve the performance of them. This is achieved by utilizing merits of two algorithms and avoiding demerits of them. Those two algorithms are least mean square algorithm and sample matrix inversion algorithm. A hybrid algorithm of least mean square and sample matrix inversion (least mean square/sample matrix inversion) is applied on rectangular microstrip antenna to indicate the performance of the three algorithms. The microstrip antenna covers the range between 1700-1950MHz with resonant frequency 1850MHz. This frequency range is used in many applications such as mobile communication in GSM, IS-95 (CDMA), 3G, and 4G. the conventional algorithms and the hybrid algorithm are compared and verified using MATLAB.

关键词： smart antenna least mean square algorithm sample matrix inversion algorithm beamforming

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A novel adaptive structure for SOA system effort estimation

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2016年第8期27卷 1115-1127页

作者： Mishra, Siba Kumar, Chiranjeev ISM Dept Comp Sci & Engn Dhanbad 826004 Jharkhand India

This paper addresses a novel adaptive structure to solve the problem of service-oriented architecture (SOA) system effort estimation. The objective of our work is to combine the insights of signals theory with empirical research to find a recommendation scheme for the problem of SOA system effort estimation. The main motivation for using this structure is to enhance the principle of self-adaptability to the situation at hand. The proposed structure consists of an adaptive filter composed of linear combiner of filter weights, input and desired values. Additionally, a gradient steepest descent method is used to adjust the filter parameters (training process) using the least mean square algorithm. Furthermore, an experimental analysis is conducted with the proposed structure using the data of 10 past SOA system industrial applications, and in the empirical analysis, two performance measurement metrics and an evaluation function are used to assess the performance in terms of predictive accuracy. The experimental analysis and comparison study are helpful to demonstrate the effectiveness of an estimation technique. The obtained results indicate that an improved predictive accuracy for the problem of SOA system effort estimation has been achieved using the proposed structure when compared with support vector machines, linear, stepwise and ordinary least square regression techniques. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： Estimation Techniques empirical approach adaptive filters filter parameters proposed structure least mean square algorithm

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Comments on "Design of fractional adaptive strategy for input nonlinear Box-Jenkins systems"

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SIGNAL PROCESSING 2016年 119卷 169-173页

作者： Aslam, Muhammad Saeed Pakistan Inst Engn & Appl Sci Islamabad Pakistan

This note points out some errors in the identification model and implementation issues of adaptive strategies presented by Chaudhary and Raja for estimation of input nonlinear Box-Jenkins (INBJ) systems (Chaudhary and Raja, 2015 [1]). This note proves that identification model presented in above mentioned paper is incorrect and in stepwise manner suggests some suitable corrections. Authors claim to propose two algorithms for INBJ systems in above mentioned paper: fractional least mean square (F-LMS) algorithm and auxiliary model fractional least mean square (AM-FLMS) algorithm. AM-FLMS algorithm is not presented in this paper, but results of this algorithm are provided. The adaptive algorithms presented by authors are not implementable due to the presence of unknown noise terms in information vector. Authors have ignored these critical issues. Finally, corrections on the basis of established techniques are also mentioned that solve the problems in the above mentioned paper. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Signal processing Parameter estimation Adaptive filtering Input nonlinear Box-Jenkins systems least mean square algorithm

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Comparative study of balancing methods: case of imbalanced medical data

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INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2016年第3期21卷 247-263页

作者： Belarouci, Sara Bouchikhi, Sarra Chikh, Mohammed Amine Tlemcen Univ Biomed Engn Lab Tilimsen Algeria

Imbalanced learning problems contain unequal distribution of data samples among different classes, where most of the samples belong to some classes and the rest to the other classes. Learning from the imbalanced data is of utmost important to the research community as it is present in many vital real-world classification problems, such as medical diagnosis. There have been many works dealing with classification of imbalanced data sets. In medical data classification, we often face the imbalanced number of data samples where at least one of the classes constitutes only a very small minority of the data. In this paper, we proposed a learning method based on a cost-sensitive extension of least mean square (LMS) algorithm that penalises errors of different samples with different weights and some rules of thumb to determine those weights. After the balancing phase, we apply different classifiers (Support Vector Machine [SVM], k-Nearest Neighbour [k-NN] and Multilayer Perceptron [MLP]) for the new balanced data set. We have also compared the results obtained by the LMS algorithm with the results obtained by the sampling techniques (under-sampling, oversampling and Synthetic Minority Oversampling Technique (SMOTE)).

关键词： imbalanced medical database least mean square algorithm undersampling oversampling SMOTE multilayer perceptron k-nearest neighbour support vector machine

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