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C#URController V1.7外国六自由度机器人上位机
C#URController V1.7外国六自由度机器人上位机,一个完整的C#控制机械手，工控项目，很值得下载，绝对值
- 2020-06-23下载
- 积分：1
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自适应变异的粒子群优化算法
本文提出了一种新的基于群体适应度方差自适应变异的粒子群优化算法(A ，s0)．该算法在运行过程中根据群体适应度方差以及当前最优解的大小来确定当前最佳粒子的变异概率，变异操作增强了粒子群优化算法跳出局部最优解的能力。
- 2021-05-06下载
- 积分：1
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MATLAB中图像分割迭代法求阈值
该程序实现了图像中用迭代法求阈值，迭代法是基于逼近的思想，其步骤如下：求出图像的最大灰度值和最小灰度值，根据阈值 将图像分割为前景和背景，分别求出两者的平均灰度值 ，求出新阈值
- 2020-12-11下载
- 积分：1
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OFDM调制程序 MATLAB
OFDM调制解调程序 MATLAB源代码 详细注释
- 2020-12-01下载
- 积分：1
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基于SURF算法的图像拼接
基于SURF算法的图像拼接处理，运用RANSAC算法，单向性等原理进行图像拼接。
- 2020-06-28下载
- 积分：1
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STM32F1单片机MS5611高精度气压计驱动工程
STM32F1单片机MS5611高精度气压计驱动工程，完成遵守燕骏v3.0编程规范（本人已上传到CSDN）
- 2020-12-05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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小功率光伏发电并网系统.doc
【实例简介】本系统以全桥逆变电路为功率变换核心，采用准同期并列方式通过对电网电压检测实现并网，利用数字式闭环反馈调节方式实现对并网输出功率的调节。数字电路采用单片机和FPGA结合的方式，实现对各路电参数的实时监测和反馈控制，并为并网动作和过流保护动作提供控制。系统强电与弱电相互隔离，输出电压稳定，电流THD小于5%，功率因数高达98%，效率可达到85%以上。
- 2021-12-08 00:35:30下载
- 积分：1
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matlab按帧分割视频为jpg和灰度图
利用matlab，实现按帧分割视频为jpg和灰度图。程序可用，只需要改一下input视频的名称。生成的图片在当前文件夹下。无需设置图像大小，图像帧数，均自动实现。
- 2020-12-09下载
- 积分：1
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自适应滤波器MATLAB仿真
自适应滤波器的设计以及使用MATLAB仿真实现
- 2020-11-27下载
- 积分：1
