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IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Ma, Xuefei Wang, Bo Li, Yang Hu, Pengpeng Wang, Tingling Wang, Guan Harbin Engn Univ Coll Underwater Acoust Engn Harbin Peoples R China Beijing Great Wall Elect & Equipment Co Ltd Beijing Peoples R China Syst Engn Res Inst Syst Engn Innovat Ctr Beijing Peoples R China

ISBN: (纸本)9781538692981

In this paper, we propose an underwater communication method that can realize real-time communication, which can be used for communication between high-speed UUV and underwater intelligent nodes. This method of Doppler compensation of orthogonal frequency division multiplexed signals for underwater communication does not utilize the conventional channel estimation method, but uses the concept of decomposing channel variations based on a set of known functions. OFDM symbol reconstruction is performed after the T (Taylor)-FFT decomposition channel. A stochastic gradient adaptive algorithm is designed to learn the combination of detection weights. Thus, the two steps of channel matching filtering and channel balancing are implemented by one step. The validity and correctness of this method are verified by the recent experimental data (several kilometers in shallow water, 12-15 kHz band) of mobile underwater acoustic communication provide technical support for high-speed mobile object communication.

关键词： OFDM Doppler stochastic gradient algorithm Underwater communication

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A robust variable step-size LMS algorithm using error-data normalization

A robust variable step-size LMS algorithm using error-data n...

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IEEE SoutheastCon 2005

作者： Ramadan, Z Poularikas, A Univ Alabama Dept Elect & Comp Engn Huntsville AL 35899 USA

ISBN: (纸本)0780388658

This paper introduces a new variable step-size LMS algorithm in which the step-size is dependent on both data and error normalization. With an appropriate choice of the value of the fixed step-size and the ratio between error and data normalization in the proposed algorithm, a trade-off between speed of convergence and misadjustment can be achieved. The performance of the algorithm is compared with other LMS-based algorithms in several input environments. Computer simulation results demonstrate substantial improvements in the speed of convergence of the proposed algorithm in stationary environment over other algorithms with the same small level of misadjustment. In addition, the proposed algorithm shows superior tracking capability when the system is subjected to an abrupt disturbance. For nonstationary environment, the performance of the algorithm is equivalent to other time-varying step-size algorithms.

关键词： least mean squares methods convergence of numerical methods gradient methods adaptive filters stochastic gradient algorithm robust variable step-size LMS algorithm normalization dependent step-size data normalization error normalization convergence speed/misadjustment trade-off stationary environment tracking capability abrupt system disturbance nonstationary environment

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Adaptive filtering under maximum mutual information criterion

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NEUROCOMPUTING 2008年第16-18期71卷 3680-3684页

作者： Chen, Badong Hu, Jinchun Li, Hongbo Sun, Zengqi Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China

The maximum mutual information (MaxMI) criterion is used as the adaptation cost for the adaptive filtering. This criterion is robust to measure distortions, and has strong connection with traditional mean-square error (MSE) criterion. Under Gaussian assumption, the closed-form solution of the finite impulse response (FIR) filter is obtained. Further, based on the kernel density estimation, the stochastic mutual information gradient (SMIG) algorithm is derived. Simulation results emphasize the robustness of this new algorithm. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Mutual information Adaptive filtering System identification stochastic gradient algorithm

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Optimization of delay diversity for linear equalization

Optimization of delay diversity for linear equalization

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IEEE Wireless Communications and Networking Conference

作者： Yiu, S Schober, R Gerstacker, W Univ British Columbia Vancouver BC V5Z 1M9 Canada

ISBN: (纸本)0780389662

In this paper, we optimize delay diversity (DD) for the case when simple linear equalization (LE) is used at the receiver. We consider the general case of transmission over a correlated multiple-input multiple-output (MIMO) frequency-selective fading channel. The proposed optimization requires the knowledge of the statistical properties of the wireless channel at the transmitter, but channel state information is only required at the receiver side. Based on an approximation of the bit error rate for LE, we derive a stochastic gradient algorithm that allows us to optimize the DD transmit filters. Simulation results for the GSM/EDGE system show significant performance gains of the proposed optimized DD scheme over the DD schemes reported in [11 and 12], respectively, if LE is used at the receiver.

关键词： error statistics optimisation delays diversity reception equalisers radio receivers MIMO systems fading channels statistical analysis radio transmitters approximation theory gradient methods stochastic processes space-time codes filtering theory cellular radio delay diversity optimization linear equalization receiver correlated multiple-input multiple-output frequency-selective fading channel correlated MIMO fading channel frequency-selective channel channel state information bit error rate approximation BER approximation stochastic gradient algorithm transmit filters GSM/EDGE system space-time coding multiple input multiple output systems Equalizers Channel state information Receivers filtering theory space-time coding cellular radio radio receivers space-time codes gradient methods diversity reception Approximation Theory RADIO TRANSMITTERS Fading channels Markov chain

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Predicting Hepatitis B to be acute or chronic in an infected person using machine learning algorithm

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ADVANCES IN ENGINEERING SOFTWARE 2022年 172卷

作者： Vijayalakshmi, C. Mohideen, S. Pakkir BSA Crescent Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India BSA Crescent Inst Sci & Technol Dept Comp Applicat Chennai Tamil Nadu India BSA Crescent Inst Sci & Technol Chennai Tamil Nadu India

Hepatitis B is a viral infection which causes liver damage. It can lead to death. This hepatitis B along with Hepatitis C can cause hepatocellular carcinoma and liver cirrhosis. In this paper it is discussed about Hepatitis B found positive in a person's blood test is acute or chronic. This research work plans to code an endurance forecast model for the dataset which contains the boundaries or data of Hepatitis-B patients. At first the information will be pre-prepared, to improve fit for additional handling and for being in satisfactory configuration for the calculations. At that point, several calculations to indicate the forecast and draw out the precision of the model. What's more, further contrast those calculations with indicate the calculation with most adequacy. The precision is determined by contrasting the anticipated result and ongoing result of the patient. In light of thinking about different boundaries, the model will anticipate the danger of a patient of his endurance rate of acute or chronic infected person accuracy. In this paper we use stochastic gradient algorithm to find the Co-connection between boundaries of the date set, kernel approximation to finalise the resulting accuracy of the acute or choric prediction of patients and SVM method we use to clustering the kernel approximation calculation and connection analysis.

关键词： Hepatitis B Machine learning SVM stochastic gradient algorithm Dataset

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Parameter Estimation of a Class of Neural Systems with Limit Cycles

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algorithmS 2018年第11期11卷 169-169页

作者： Lou, Xuyang Cai, Xu Cui, Baotong Jiangnan Univ Inst Syst Engn 1800 Lihu Rd Wuxi 214122 Peoples R China

This work addresses parameter estimation of a class of neural systems with limit cycles. An identification model is formulated based on the discretized neural model. To estimate the parameter vector in the identification model, the recursive least-squares and stochastic gradient algorithms including their multi-innovation versions by introducing an innovation vector are proposed. The simulation results of the FitzHugh-Nagumo model indicate that the proposed algorithms perform according to the expected effectiveness.

关键词： parameter estimation neural system recursive least-squares algorithm stochastic gradient algorithm innovation

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Optimal Adaptive Control with Consistent Parameter Estimates

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IFAC Proceedings Volumes 1987年第2期20卷 353-357页

作者： Han-Fu Chen Lei Guo Institute of Systems Science Academia Sinica Beijing PRC

For the multidimensional ARMAX model the unknown matrix coefficientsare estimated by both the least squares (LSA) and the stochastic gradient algorithms (SGA) and adaptive controls are designed either for minimizing the quadratic cost or for tracking a reference signal. By using the attenuating excitation and the randomly varying truncation techniques the consistency of estimates and the minimality of the loss function are achieved simultaneously.

关键词： stochastic systems least squares algorithm stochastic gradient algorithm adaptive control consistent estimate

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Using Computer Algebra to Certify the Global Convergence of a Numerical Optimization Process

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MATHEMATICS IN COMPUTER SCIENCE 2007年第2期1卷 291-304页

作者： Gu, Nong Lazard, Daniel Rouillier, Fabrice Xiang, Yong Deakin Univ Sch Engn & Informat Technol Geelong Vic 3217 Australia INRIA Paris Rocquencourt SALSA Project F-75016 Paris France Univ Paris VI F-75016 Paris France CNRS F-75016 Paris France

The basic objective of blind signal separation is to recover a set of source signals from a set of observations that are mixtures of the sources with no, or very limited knowledge about the mixture structure and source signals. To extract the original sources, many algorithms have been proposed;among them, the cross-correlation and constant modulus algorithm (CC-CMA) appears to be the algorithm of choice due to its computational simplicity. An important issue in CC-CMA algorithm is the global convergence analysis, because the cost function is not quadratic nor convex and contains undesirable stationary points. If these undesirable points are local minimums, the convergence of the algorithm may not be guaranteed and the CC-CMA would fail to separate source signals. The main result of this paper is to complete the classification of these stationary points and to prove that they are not local minimums unless if the mixing parameter is equal to 1. This is obtained by using the theory of discriminant varieties to determine the stationnary points as a function of the parameter and then to show that the Hessian matrix of the cost function is not positive semidefinite at these stationnay points, unless if the mixing parameter is 1.

关键词： Blind signal separation MIMO systems global convergence analysis stochastic gradient algorithm discriminant variety parametric polynomial system

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A unified convergence analysis for shuffling-type gradient methods

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 9397-9440页

作者： Lam M. Nguyen Quoc Tran-Dinh Dzung T. Phan Phuong Ha Nguyen Marten Van Dijk IBM Research Thomas J. Watson Research Center Yorktown Heights NY Department of Statistics and Operations Research The University of North Carolina at Chapel Hill Chapel Hill NC eBay Inc. San Jose CA Centrum Wiskunde & Informatica Amsterdam Netherlands

In this paper, we propose a unified convergence analysis for a class of generic shuffling-type gradient methods for solving finite-sum optimization problems. Our analysis works with any sampling without replacement strategy and covers many known variants such as randomized reshuffling, deterministic or randomized single permutation, and cyclic and incremental gradient schemes. We focus on two different settings: strongly convex and nonconvex problems, but also discuss the non-strongly convex case. Our main contribution consists of new non-asymptotic and asymptotic convergence rates for a wide class of shuffling-type gradient methods in both nonconvex and convex settings. We also study uniformly randomized shuffling variants with different learning rates and model assumptions. While our rate in the nonconvex case is new and significantly improved over existing works under standard assumptions, the rate on the strongly convex one matches the existing best-known rates prior to this paper up to a constant factor without imposing a bounded gradient condition. Finally, we empirically illustrate our theoretical results via two numerical examples: nonconvex logistic regression and neural network training examples. As byproducts, our results suggest some appropriate choices for diminishing learning rates in certain shuffling variants.

关键词： stochastic gradient algorithm shuffling-type gradient scheme sampling without replacement non-convex finite-sum minimization strongly convex minimization

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IDENTIFICATION OF HAMMERSTEIN OUTPUT-ERROR SYSTEMS WITH TWO-SEGMENT NONLINEARITIES: algorithm AND APPLICATIONS

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CONTROL AND INTELLIGENT SYSTEMS 2010年第4期38卷 194-201页

作者： Yu, Bo Fang, Huazhen Lin, Yang Shi, Yang Department of Mechanical Engineering University of Saskatchewan Saskatoon SK S7N 5A9 57 Campus Drive Canada Department of Mechanical Engineering University of Victoria Victoria BC V8W 3P6 P.O. Box 3055 Canada

This paper studies the parameter identification problem of Hammerstein output-error systems with two-segment nonlinearities. New switching sequences are proposed to reduce the number of parameters to be estimated. A stochastic gradient identification algorithm together with the convergence analysis is further developed. A varying forgetting factor scheme is used to make a tradeoff between the convergence rate and estimation accuracy. Both numerical simulation results and applications to distillation columns are provided to show the effectiveness of the proposed method.

关键词： System identification Hammerstein systems with two-segment non-linearities stochastic gradient algorithm distillation columns

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