神经网络、遗传算法、支持向量机、退火算法等算法matlab源码、工具包及数据集

本项目着手实现了一个基于卷积神经网络的人脸识别签到系统，该系统能够进行人脸的采集，并将不同人脸对应的学号（工号）姓名信息存储于数据库，利用CNN卷积神经网络对人脸进行训练；人脸签到模块能实时识别当前人脸，识别成功会语音播报某学号(工号)某同学(员工)签到成功，并在系统界面输出显示签到信息同时自动更改当前对象的签到状态；缺勤模块可以查看当前未签到成员信息，可以重置所有成员的签到状态。项目特点：1、基于神经网络，系统具有学习能力，理论上给它喂的数据越多，它就可以识别越多的人而且准确度会不断提高。 2、利用多线程将ui界面与功能代码分开，在显示界面的同时还能进行后台的运算，防止卡顿提升使用体验，

详细介绍了状态观测器及其在控制系统中的应用。Observers inControl SystemsA Practical guideGeorge ellisDanaher corporation4ACADEMIC PRESSAn imprint of elsevier ScienceAmsterdam Boston London New York Oxford ParisSan Diego San Francisco Singapore Sydney TokyoThis book is printed on acid-free paper ooCopyright 2002, Elsevier Science (USA)All rightsNo part of this publication may be reproduced or transmitted in any form or by anymeans, electronic or mechanical, including photocopy, recording, or any informationstorage and retrieval system, without permission in writing from the publisher. Requestsfor permission to make copies of any part of the work should be mailed to thefollowing address: Permissions Department, Harcourt, Inc, 6277 Sea Harbor DriveOrlando. Florida. 32887-6777.ACADEMIC PRESSAn imprint of Elsevier Science525 B Street, Suite 1900, San Diego, CA 92101-4495, USAhttp://www.academicpress.comAcademic pr84 Theobalds Road. London WCIX 8RR. UKhttp://www.academicpress.comLibrary of congress control Number: 2002104256International Standard book Number: 0-12-237472-XPrinted in the United States of america020304050607MB987654321TO Lee Ann, my loving wife, and our daughter Gretchen, who makes us both proud.Observers in Control Systems■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■Acknowledgments■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■Safety■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■1 Control Systems and the role of observers■■■■■■■■■■■■■■■■1.1 Overviewaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa1.2 Preview of observers21.3 Summary of the book2 Control-System Background52.1 Control-System Structures52.2 Goals of control systems132.3 Visual Model Simulation Environment2. 4 Software Experiments: Introduction to Visual ModelQ182.5 Exercises393 Review of the Frequency Domain.■■■■■■■■■■■■■■■■■■■■■■■■■■■■■3. 1 Overview of the s-domain413.2 Overview of the z-Domain543.3 The Open-Loop Method593.4 A Zone-Based Tuning Procedure623.5 Exercises664 The Luenberger observer: Correcting SensorProblems674. 1 What Is a luenberger observer?674.2 Experiments 4A-4C: Enhancing Stability with an Observer724.3 Predictor-Corrector Form of the Luenberger Observer774. 4 Filter Form of the luenberger observer. ..................................784.5 Designing a Luenberger observer824.6 Introduction to Tuning an observer compensator9047 Exercises955 The Luenberger Observer and Model Inaccuracy... 975.1 Model Inaccuracy.........….……,975.2 Effects of Model Inaccuracy .............................................1005.3 Experimental Evaluation1025.4 Exercises1146 The Luenberger observer and disturbances1156.1 Disturbances1156.2 Disturbance Response1236.3 Disturbance DecouplingIB..-.81296.4 Exercises1387 Noise in the Luenberger Observer…,,,…,,…,…,1417.1 Noise in Control Systems1417.2 Sensor noise and the luenberger observer1457.3 Noise Sensitivity when Using Disturbance Decoupling1567. 4 Reducing Noise Susceptibility in Observer-Based Systems1617.5 Exercises1708 Using the Luenberger Observer in Motion Control1738.1 The Luenberger observers in motion Systems1738.2 Observing Velocity to Reduce Phase Lag1858.3 Using observers to Improve Disturbance Response..... 2028.4 Exercises212Referencesn213A Observer-Based resolver conversion in industrialServo Systems1■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■217B Cures for mechanical resonance in IndustrialServo Systems227Introductionaaa日aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa日aaaaa.aaaaaaaaaaaaaaa日aa227TWo-Part Transfer function228LOW-Frequency Resonance229Velocity Control Law…...8....230Methods of Correction Applied to Low-Frequency Resonance231Conc| usion.…235Acknowledgments235References235C European Symbols for Block Diagrams237Part Linear functions237Part l: nonlinear functions238D Development of the bilinear transformation241Bilinear Transformation241Prewarning242Factoring polynomials243Phase Advancing………………………,243Solutions toChapter 2245Chapter 3245Chapter 4246Chapter 5重口m口m246Chapter 6247Chapter7.….B..8..8..8...248Chapter 8249Index251AcknowledgmentsWriting a book is a large task and requires support from numerous people, and thosepeople deserve thanks. First, I thank LeeAnn, my devoted wife of more than 20 yearsShe has been an unflagging fan, a counselor, and a demanding editor. She taught memuch of what I have managed to learn about how to express a thought in ink. Thanksto my mother who was sure I would grow into someone in whom she would be proudwhen facts should have dissuaded her Thanks also to my father for his insistence thatI obtain a college education; that privilege was denied to him, an intelligent man borninto a family of modest meansI am grateful for the education provided by Virginia Tech Go Hokies. The basicsof electrical engineering imparted to me over my years at school allowed me to graspthe concepts I apply regularly today. I am grateful to Mr. Emory Pace, a toughprofessor who led me through numerous calculus courses and, in doing so, gaveme the confidence on which I would rely throughout my college career and beyondI am especially grateful to Dr Charles Nunnally; having arrived at university froma successful career in industry, he provided my earliest exposure to the practicalapplication of the material I strove to learn. I also thank Dr robert lorenz of theUniversity of Wisconsin at Madison, who introduced me to observers some years agoHis instruction has been enlightening and practical. Several of his university coursesare available in video format and are recommended for those who would like toextend their knowledge of controls. In particular, readers should consider ME 746which presents observers and numerous other subjectsI thank those who reviewed the manuscript for this book. Special thanks goes toDan Carlson for his contributions to almost every chapter contained herein Thanksalso to Eric Berg for his numerous insights. Thanks to the people of KollmorgenCorporation(now, Danaher Corporation), my long-time employer, for their continuing support in writing this book. Finally, thanks to Academic Press, especially to JoelClaypool, my editor, for the opportunity to write this edition and for editing, printing, distributing, and performing the myriad other tasks required to publish a bookX1

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Shamir 的(k,n)秘密共享算法将秘密S 分为n个子秘密，任意k 个子秘密都可以恢复出S ，而任意k 1个子秘密无法恢复出S 。