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雷达系统设计matlab仿真
内含雷达系统设计MATLAB仿真的pdf和代码，主要包括：雷达基础导论，雷达检测，雷达波形，雷达模糊函数，脉冲压缩，面杂波与体杂波，动目标显示和杂波抑制，相控阵，目标跟踪，电子对抗，雷达截面积，高粉笔啊率战术合成孔径雷达，信号处理等。
- 2020-12-07下载
- 积分：1
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gatbx工具箱
matlab gatbx工具箱matlab 遗传算法工具箱(GATBX)下载和安装教程,加载到toolbox,再generate即可。
- 2020-12-09下载
- 积分：1
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matlab人工地震波
人工 地震波 matlab使用三角级数法，场地指数之类的可以自定
- 2020-11-29下载
- 积分：1
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一种矩阵补全的MATLAB实现方法
一种矩阵补全的MATLAB实现方法LowRank-MatrixCompletion
- 2021-05-06下载
- 积分：1
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基于轨迹重规划的智能车辆避障模型预测控制
为了提高无人驾驶车辆的轨迹跟随性精度和实时性，针对无人驾驶车辆规避障碍物的路径规划问题，本文提出了一种基于轨迹重规划的无人车避障模型预测控制算法。利用车载传感器获得障碍物和参考路径信息，通过轨迹重规划控制器规划出规避障碍物的期望路径， 并将重规划的期望路径信息输入到模型预测控制器，控制输出前轮转角，通过方向盘主动转向实现避障的同时，实现对参考路径的跟踪；最后，利用 CarSim 和 Simulink 建立轨迹重规划和轨迹跟踪的闭环控制系统仿真平台，并以双移线工况为例，进行了仿真验证。结果表明该方法能够在不同车速下实现对障碍物可靠规避的同时，实现对参考路径的稳定跟踪。
- 2020-12-12下载
- 积分：1
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STM32F103C8T6电路原理图及PCB图
内含STM32F103C8T6电路原理图及PCB图
- 2020-06-27下载
- 积分：1
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【PDF】《Machine learning A Probabilistic Perspective》 MLAPP；by Kevin Murphy
完整版，带目录，机器学习必备经典；大部头要用力啃。Machine learning A Probabilistic PerspectiveMachine LearningA Probabilistic PerspectiveKevin P. MurphyThe mit PressCambridge, MassachusettsLondon, Englando 2012 Massachusetts Institute of TechnologyAll rights reserved. No part of this book may be reproduced in any form by any electronic or mechanicalmeans(including photocopying, recording, or information storage and retrieval)without permission inwriting from the publisherFor information about special quantity discounts, please email special_sales@mitpress. mit. eduThis book was set in the HEx programming language by the author. Printed and bound in the UnitedStates of AmLibrary of Congress Cataloging-in-Publication InformationMurphy, Kevin Png:a piobabilistctive/Kevin P. Murphyp. cm. -(Adaptive computation and machine learning series)Includes bibliographical references and indexisBn 978-0-262-01802-9 (hardcover: alk. paper1. Machine learning. 2. Probabilities. I. TitleQ325.5M872012006.31-dc232012004558109876This book is dedicated to alessandro, Michael and stefanoand to the memory of gerard Joseph murphyContentsPreactXXVII1 IntroductionMachine learning: what and why?1..1Types of machine learning1.2 Supervised learning1.2.1Classification 31.2.2 Regression 83 Unsupervised learning 91.3.11.3.2Discovering latent factors 111.3.3 Discovering graph structure 131.3.4 Matrix completion 141.4 Some basic concepts in machine learning 161.4.1Parametric vs non-parametric models 161.4.2 A simple non-parametric classifier: K-nearest neighbors 161.4.3 The curse of dimensionality 181.4.4 Parametric models for classification and regression 191.4.5Linear regression 191.4.6Logistic regression1.4.7 Overfitting 221.4.8Model selection1.4.9No free lunch theorem242 Probability2.1 Introduction 272.2 A brief review of probability theory 282. 2. 1 Discrete random variables 282. 2.2 Fundamental rules 282.2.3B292. 2. 4 Independence and conditional independence 302. 2. 5 Continuous random variable32CONTENTS2.2.6 Quantiles 332.2.7 Mean and variance 332.3 Some common discrete distributions 342.3.1The binomial and bernoulli distributions 342.3.2 The multinomial and multinoulli distributions 352. 3.3 The Poisson distribution 372.3.4 The empirical distribution 372.4 Some common continuous distributions 382.4.1 Gaussian (normal) distribution 382.4.2Dte pdf 392.4.3 The Laplace distribution 412.4.4 The gamma distribution 412.4.5 The beta distribution 422.4.6 Pareto distribution2.5 Joint probability distributions 442.5.1Covariance and correlation442.5.2 The multivariate gaussian2.5.3 Multivariate Student t distribution 462.5.4 Dirichlet distribution 472.6 Transformations of random variables 492. 6. 1 Linear transformations 492.6.2 General transformations 502.6.3 Central limit theorem 512.7 Monte Carlo approximation 522.7.1 Example: change of variables, the MC way 532.7.2 Example: estimating T by Monte Carlo integration2.7.3 Accuracy of Monte Carlo approximation 542.8 Information theory562.8.1Entropy2.8.2 KL dive572.8.3 Mutual information 593 Generative models for discrete data 653.1 Introducti653.2 Bayesian concept learning 653.2.1Likelihood673.2.2 Prior 673.2.3P683.2.4Postedictive distribution3.2.5 A more complex prior 723.3 The beta-binomial model 723.3.1 Likelihood 733.3.2Prior743.3.3 Poster3.3.4Posterior predictive distributionCONTENTS3.4 The Dirichlet-multinomial model 783. 4. 1 Likelihood 793.4.2 Prior 793.4.3 Posterior 793.4.4Posterior predictive813.5 Naive Bayes classifiers 823.5.1 Model fitting 833.5.2 Using the model for prediction 853.5.3 The log-sum-exp trick 803.5.4 Feature selection using mutual information 863.5.5 Classifying documents using bag of words 84 Gaussian models4.1 Introduction974.1.1Notation974. 1.2 Basics 974. 1.3 MlE for an mvn 994.1.4 Maximum entropy derivation of the gaussian 1014.2 Gaussian discriminant analysis 1014.2.1 Quadratic discriminant analysis(QDA) 1024.2.2 Linear discriminant analysis (LDA) 1034.2.3 Two-claSs LDA 1044.2.4 MLE for discriminant analysis 1064.2.5 Strategies for preventing overfitting 1064.2.6 Regularized LDA* 104.2.7 Diagonal LDA4.2.8 Nearest shrunken centroids classifier1094.3 Inference in jointly Gaussian distributions 1104.3.1Statement of the result 1114.3.2 Examples4.3.3 Information form 1154.3.4 Proof of the result 1164.4 Linear Gaussian systems 1194.4.1Statement of the result 1194.4.2 Examples 1204.4.3 Proof of the result1244.5 Digression: The Wishart distribution4.5. 1 Inverse Wishart distribution 1264.5.2 Visualizing the wishart distribution* 1274.6 Inferring the parameters of an MVn 1274.6.1 Posterior distribution of u 1284.6.2 Posterior distribution of e1284.6.3 Posterior distribution of u and 2* 1324.6.4 Sensor fusion with unknown precisions 138
- 2020-12-10下载
- 积分：1
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matlab实现FFT频谱分析仪(GUI)以及滤波作用
自己的课设程序，要求用matlab实现读取音频文件和声卡采集，并FFT分析频谱，提供IIR、FIR两种滤波方式（高通、低通、带通），含有打包好的exe文件，不过如果你的电脑上不是matlab2013a版的可能运行不了，可以在自己电脑上打包exe,内含txt说明文档
- 2020-12-01下载
- 积分：1
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JSP博客系统源代码及数据库文件
JSP博客系统源代码及数据库文件 值得一用
- 2020-12-01下载
- 积分：1
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万能网盘搜索神器
万能网盘搜索神器，搜索你相要的百度云资源，非常强大和实用
- 2020-11-28下载
- 积分：1
