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物流网络选址与路径优化问题的模型与启发式解法120交通运输工程学报2006年(i∈T)(10表1小规模问题的计算结果Tab. 1 Computation result of smalF-scaled problem(xh+xbk)-z≤1(i∈T,∈Ck∈K)(11)最优解∈TUC(j∈C问题规模目标值运行时间/s(12)NG= 3, NT=3, Nc= 83885k≤s1(≤c1121=∑体k∈(13NG=3,Nr=3,Nc=10421.221U-U+Nx≤N-1(i,∈S,k∈K)(14)NG=3,Nr=3,Nc=126850580∈TNG=3,NT=3,Nc=148741162000注:Nc为供应商数量,Nr为配送中心数量,Nc为客户数量ba≤B(g∈G)(16用启发式算法求得初期解xi∈/Q1(i,j∈T∪C,k∈K)(17)l0;∈/0,1∈T(18通过交换配送中心间的路径进行第1次解的改善∈/O,1∈T,j∈C)(19)yk∈/0,1(i∈C,k∈K)(20)通过交换同一路径中客户的位置(2-OPT法)进行第2次解的改善式中:G为供应商的集合;T为配送中心的集合;C为客户的集合;K为车辆的集合;Qk为车辆k的最通过交换不同路径中的客户进行第3次解的改善大载质量:S为C的部分集合;C为从点到点j的距离;B为供应商g的最大供应量;V为配送中心SA模块的最大货物通过量;D,为客户j的需求量;H2为配送中心i的固定费用;L为从供应商g到配送中是否满足终止条件心i的单位运输费用;Fk为车辆k的固定费用;a为解的输出与通过量有关的系数;xk为对于车辆k,如果点i以后的访问点是点j,即为1,否则为0;y为如果点j图1基于SA的混合启发式算法的货物由车辆k配送,即为1,否则为0;v;为如果FiMixed heuristic algorit hm based on sa使用配送中心i,即为1,否则为0;z为如果客户j传统启发式算法与智能启发式算法相结合的混合算由配送中心提供服务,即为1,否则为0;pa为从供法,以期在短时间内求得全局最优解应商g到配送中心的供应量。其中x、3计算分析和pg为决策变量。2问题的求解为了对SA的参数进行设定,进行了预备实验并确定参数如下:初始温度To为200冷却率α为为了验证上述数学模型的正确性,用数理规划07,与温度相关的循环次数调整参数β为.1,最商用软件 LINGO8.0对小规模问题进行了数值计大循环次数将按照问题规模的大小做适当的设定,算,结果见表1。可以看出,随着问题的规模扩大,数据采用人工生成数据,在200km×200km的区计算时间急剧增加;当客户达到14个时,计算时间域内随机生成指定个数的供应商、配送中心和客户,长达45h,显然无法满足解决现实问题的需要。为并生成距离矩阵和客户需求量;采用C语言编程,了满足解决现实问题的需要,有必要开发岀一种能计算结果见表2。从表2中的结果比较可以看出,够在合理的时间内求得准最优解的近似算法。表2最优解与近似解比较传统启发式算法能够在短时间内求得局部最优Tab 2 Comparison of optimal solution and approxi mate solution解,但往往容易陷于局部最优,而无法求得全局最优最优解近似解问题规模解。智能启发式算法能够求得全局最优解,但计算时目标值运行时间/s目标值运行时间/s%间相对较长。如果能够将两者结合起来,既可以防止N=3N=3,Nc=838533965求解过程陷于局部搜索无法跳出,保证全局解的搜3N=3NG=10+2122112100索,又可以缩短搜索时间达到在短时间内求得全局N-3-3Nc12|60s0620110最优解的目的。基于上述考虑本文提出了图1的将ublishrigfoustAingnsestrvcu,trttp/www.urrhi.rctNG=3,Nr=3,Nc=1487411620008895第3期陈松岩,等:物流网络选址与路径优化问题的模型与启发式解法121本算法求得的近似解与 LINGO80计算的最优解心,再从配送中心到客户这一典型的物流过程,涉及之间的误差很小,但计算时间却大大缩短了。依据运输与配送2个阶段和供应商、配送中心和客户3结果虽然无法判定所提岀的混合启发式算法对于大个层次,提出了多供应商、多物流中心情况下的物流规模问题的有效性,但可以看岀,对于求解小规模问路径与配送路径优化问题,给岀了问题的数学模型,题是有效和良好的。对于大规模问题,将利用实例利用传统启发式算法与模拟退火法开发了混合近似进行计算验证。解法,通过人工生成数据和实例计算验证,可以看出4应用实例所提出的数学模型可以准确地描述此类问题,具有良好的适应性,所提出的混合近似解法能够在短时在应用实例中,将港口作为供应商来考虑,以进间内求解问题并得到接近于最优解的近似解,具有口货物从港口经配送中心配送到客户过程中发生的较高的实用价值。但本模型没有考虑库存问题与供费用最小化为目的,以港口的数量和位置、配送中心应商的成本问题,无法达到物流网络中各个环节的数量和位置作为对象进行优化。实例的区域选择山整体优化,有待于今后进一步研究。东省,候选港口为天津港、烟台港、威海港、青岛港参考文献日照港和连云港等6处,候选配送中心设置于山东省除港口城市以外的13个地级市,设定客户分别位References于90个县(包括县级市)。为了分析候选港口和配1 I anen g Flpo C. Spatial de composit ion for a multI送中心的数量及位置与目标值之间的关系,在计算of Production Economics, 2000, 64(1/2/3): 177186过程中,候选港口和配送中心的数量分别从1开始(21 Melkote s, Das kin m s. a n in te grated model of facil ity loca tion增加(港口的位置为随机选择),计算结果见图2、3and transportat ion netw ork design[ J. Transport ation Research part A,2001,35(6):5155386[3] Goets chalckx M, Vidal C J Dogan K Modeling and design ofglobal logistics s yst ems: a review of integrated strat e gic an dt act ical models and design algorithm s[ J European Journal of005◇◇0◇◇◇◇◇◇◇Operat ional Research, 2002, 143 (1):118135791113[4] H wang H S Design of suppl y chain logist ics sy stem con sider-港口数量配送中心数量ing service level[ J. Computers and Industrial Engineerin g,图2港口数量与图3配送中心数量与2002,43(1/2):283297目标函数值关系目标函数值关系[5] WuT H, Low C, Bai J W. Heurist ic s ol ution s to mu ltt depotFig2 Relation of ob ject value Fig 3 Relation of ob ject valuelocatioN rou tin g pr ob lems[ J]. Computers and Operations Re-and ports numberand changing depots num besearch,2002,29(10):13931415可以看出,随着候选港口数量的增加.目标值呈[6 Syam ss. A model and met hodologies for the locat ion p rob lem下降趋势,说明可供选择的港口越多,求得最优解的with logist ical components[ J]. Computers and Operations Re机会越大,但本例中,当候选港口的数量增加到5个se arch,200)2,29(9):l173-1193时,目标值达到最小(实际被选中的港口为3个);候[7 Amiri A. Designing a distribution network in a suppl y chainsystem[ J]. European Jou rnal of O perat ional Research, 2004选配送中心数量的变化也呈相同的趋势,当候选配171(2):567576送中心数量达到9个时,目标值达到最小(实际被选8 G ena m, Syarif a. Hybrid genetic algorit hm for mult+ time pe-中的配送中心为8个);本实例的计算时间都在8sriod production/ distribution planning[ J1. Computers and Ir以内,虽然无法判断所求解为最优解,但从计算结果dustrial Engineering, 2005, 48(4): 799809来看,基本接近最优,因此可以认为本算法对于求解9工丰元,潘福全张丽霞等基于交通限制的路网最优路径算大规模问题也是有效和良好的法J.交通运输工程学报,2005,5(1):9295Wang Feng yuan, Pan Fuquan, Zhang Li xia, et al. Opti mal5结语path algorithm of road netw ork with traffic rest riction[ JIJourn al of Traffic and Trans port ation Engin eering, 2005, 5(1)本文将研究范围界定在商品从供应商到配送中9295.(in Ch ineseo1994-2012ChinaacAdemicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net

ZYNQ-ZC7020硬件原理图和PCB图，亲测可用ZYNQ-ZC7020硬件原理图和PCB图，亲测可用ZYNQ-ZC7020硬件原理图和PCB图，亲测可用

MATLAB在卡尔曼滤波器中应用的理论与实践KalmanKALMAN FILTERINGTheory and Practice Using MATLABThird editionMOHINDER S GREWALCalifornia State University at FullertonANGUS P. ANDREWSRockwell Science Center (retired)WILEYA JOHN WILEY & SONS, INC. PUBLICATIONCopyright 2008 by John Wiley sons, Inc. All rights reservedPublished by John Wiley sons, InC, Hoboken, New JerseyPublished simultaneously in CanadaNo part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or byany means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permittedunder Section 107 or 108 of the 1976 United States Copyright Act, without either the prior writtenpermission of the Publisher, or authorization through payment of the appropriate per-copy fee to theCopyright Clearance Center, Inc, 222 Rosewood Drive, Danvers, MA 01923,(978)750-8400, fax(978)750-4470,oronthewebatwww.copyright.com.RequeststothePublisherforpermissionshouldbe addressed to the Permissions Department, John Wiley Sons, Inc, lll River Street, Hoboken, NJ07030,(201)748-6011,fax(201)748-6008,oronlineathttp://www.wiley.com/go/permissionimit of liability Disclaimer of Warranty: While the publisher and author have used their best efforts inpreparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability orfitness for a particular purpose. No warranty may be created or extended by sales representatives orwritten sales materials. The advice and strategies contained herein may not be suitable for your situationYou should consult with a professional where appropriate. Neither the publisher nor author shall be liablefor any loss of profit or any other commercial damages, including but not limited to special, incidentalconsequential, or other damagesFor general information on our other products and services or for technical support, please contact ourCustomer Care Department within the United States at(800)762-2974, outside the United States at(317)572-3993 or fax(317)572-4002Wiley also publishes its books in a variety of electronic formats. Some content that appears in print maynot be available in electronic format. For more information about wiley products, visit our web site atwww.wiley.comLibrary of Congress Cataloging- in-Publication DataGrewal. Mohinder sKalman filtering: theory and practice using MATLAB/Mohinder S. GrewalAngus p. andrews. 3rd edIncludes bibliographical references and indexISBN978-0-470-17366-4( cloth)1. Kalman filtering. 2. MATLAB. I. Andrews, Angus P. II. TitleQA402.3.G69520086298312—dc22200803733Printed in the United States of america10987654321CONTENTSPrefaceAcknowledgmentsXIIIList of abbreviationsXV1 General Information1.1 On Kalman Filtering1.2 On Optimal Estimation Methods, 51. 3 On the notation Used In This book 231. 4 Summary, 25Problems. 262 Linear Dvnamic Systems2. 1 Chapter focus, 312.2 Dynamic System Models, 362. 3 Continuous Linear Systems and Their Solutions, 402.4 Discrete Linear Systems and Their Solutions, 532.5 Observability of Linear Dynamic System Models, 552.6 Summary, 61Problems. 643 Random Processes and Stochastic Systems3.1 Chapter Focus, 673.2 Probability and random Variables (rvs), 703.3 Statistical Properties of RVS, 78CONTEN3.4 Statistical Properties of Random Processes(RPs),803.5 Linear rp models. 883.6 Shaping Filters and State Augmentation, 953.7 Mean and Covariance propagation, 993.8 Relationships between Model Parameters, 1053.9 Orthogonality principle 1143.10 Summary, 118Problems. 1214 Linear Optimal Filters and Predictors1314.1 Chapter Focus, 1314.2 Kalman Filter. 1334.3 Kalman-Bucy filter, 1444.4 Optimal Linear Predictors, 1464.5 Correlated noise Sources 1474.6 Relationships between Kalman-Bucy and wiener Filters, 1484.7 Quadratic Loss Functions, 1494.8 Matrix Riccati Differential Equation. 1514.9 Matrix Riccati Equation In Discrete Time, 1654.10 Model equations for Transformed State Variables, 1704.11 Application of Kalman Filters, 1724.12 Summary, 177Problems. 1795 Optimal Smoothers5.1 Chapter Focus, 1835.2 Fixed-Interval Smoothing, 1895.3 Fixed-Lag Smoothing, 2005.4 Fixed-Point Smoothing, 2135.5 Summary, 220Problems. 226 Implementation Methods2256. 1 Chapter Focus, 2256.2 Computer Roundoff, 2276.3 Effects of roundoff errors on Kalman filters 2326.4 Factorization Methods for Square-Root Filtering, 2386. 5 Square-Root and UD Filters, 2616.6 Other Implementation Methods, 2756.7 Summary, 288Problems. 2897 Nonlinear Filtering2937.1 Chapter Focus, 2937.2 Quasilinear Filtering, 296CONTENTS7.3 Sampling Methods for Nonlinear Filtering, 3307.4 Summary, 345Problems. 3508 Practical Considerations3558.1 Chapter Focus. 3558.2 Detecting and Correcting Anomalous behavior, 3568.3 Prefiltering and Data Rejection Methods, 3798.4 Stability of Kalman Filters, 3828. 5 Suboptimal and reduced- Order Filters, 3838.6 Schmidt-Kalman Filtering, 3938.7 Memory, Throughput, and wordlength Requirements, 4038.8 Ways to Reduce Computational requirements 4098.9 Error Budgets and Sensitivity Analysis, 4148.10 Optimizing Measurement Selection Policies, 4198.11 Innovations analysis, 4248.12 Summary, 425Problems. 4269 Applications to Navigation4279.1 Chapter focus, 4279.2 Host vehicle dynamics, 4319.3 Inertial Navigation Systems(INS), 4359. 4 Global Navigation Satellite Systems(GNSS), 4659.5 Kalman Filters for GNSS. 4709.6 Loosely Coupled GNSS/INS Integration, 4889.7 Tightly Coupled GNSS /INS Integration, 4919. 8 Summary, 507Problems. 508Appendix A MATLAB Software511A 1 Notice. 511A 2 General System Requirements, 511A 3 CD Directory Structure, 512A 4 MATLAB Software for Chapter 2, 512A. 5 MATLAB Software for Chapter 3, 512A6 MATLAB Software for Chapter 4, 512A. 7 MATLAB Software for Chapter 5, 513A 8 MATLAB Software for Chapter 6, 513A 9 MATLAB Software for Chapter 7, 514A10 MATLAB Software for Chapter 8, 515A 11 MATLAB Software for Chapter 9, 515A 12 Other Sources of software 516CONTENAppendix b A Matrix Refresher519B. 1 Matrix Forms. 519B 2 Matrix Operations, 523B 3 Block matrix Formulas. 527B 4 Functions of Square Matrices, 531B 5 Norms. 538B6 Cholesky decomposition, 541B7 Orthogonal Decompositions of Matrices, 543B 8 Quadratic Forms, 545B 9 Derivatives of matrices. 546Bibliography549Index565PREFACEThis book is designed to provide familiarity with both the theoretical and practicalaspects of Kalman filtering by including real-world problems in practice as illustrativeexamples. The material includes the essential technical background for Kalman filter-ing and the more practical aspects of implementation: how to represent the problem ina mathematical model, analyze the performance of the estimator as a function ofsystem design parameters, implement the mechanization equations in numericallystable algorithms, assess its computational requirements, test the validity of resultsitor the filteThetant attributes ofthe subject that are often overlooked in theoretical treatments but are necessary forapplication of the theory to real-world problemsIn this third edition, we have included important developments in the implemen-tation and application of Kalman filtering over the past several years, including adaptations for nonlinear filtering, more robust smoothing methods, and develelopingapplications in navigationWe have also incorporated many helpful corrections and suggefrom ourreaders, reviewers, colleagues, and students over the past several years for theoverall improvement of the textbookAll software has been provided in MatLab so that users can take advantage ofits excellent graphing capabilities and a programming interface that is very close tothe mathematical equations used for defining Kalman filtering and its applicationsSee Appendix a for more information on MATLAB softwareThe inclusion of the software is practically a matter of necessity because Kalmanfiltering would not be very useful without computers to implement it. It provides aMATLAB is a registered trademark of The Mathworks, IncEFACEbetter learning experience for the student to discover how the Kalman filter works byobserving it in actionThe implementation of Kalman filtering on computers also illuminates some of thepractical considerations of finite-wordlength arithmetic and the need for alternativealgorithms to preserve the accuracy of the results. If the student wishes to applywhat she or he learns, then it is essential that she or he experience its workingsand failings--and learn to recognize the differenceThe book is organized as a text for an introductory course in stochastic processes atthe senior level and as a first-year graduate-level course in Kalman filtering theory andapplicationIt can also be used for self-instruction or for purposes of review by practi-cing engineers and scientists who are not intimately familiar with the subject. Theorganization of the material is illustrated by the following chapter-level dependencygraph, which shows how the subject of each chapter depends upon material in otherchapters. The arrows in the figure indicate the recommended order of study. Boxesabove another box and connected by arrows indicate that the material represented bythe upper boxes is background material for the subject in the lower boxAPPENDIX B: A MATRIX REFRESHERGENERAL INFORMATION2. LINEAR DYNAMIC SYSTEMSRANDOM PROCESSES AND STOCHASTIC SYSTEMS4. OPTIMAL LINEAR FILTERS AND PREDICTORS5. OPTIMAL SMOOTHERS6. IMPLEMENTATIONMETHODS7. NONLINEAR8. PRACTICAL9. APPLICATIONSFILTERINGCONSIDERATIONSTO NAVIGATIONAPPENDIX A: MATLAB SOFTWAREChapter l provides an informal introduction to the general subject matter by wayof its history of development and application. Chapters 2 and 3 and Appendix b coverthe essential background material on linear systems, probability, stochastic processesand modeling. These chapters could be covered in a senior-level course in electricalcomputer, and systems engineeringChapter 4 covers linear optimal filters and predictors, with detailed examples ofapplications. Chapter 5 is a new tutorial-level treatment of optimal smoothing

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一个小的均值漂移算法的MATLAB程序，供大家参考一下