吴恩达机器学习coursera作业题目+答案

matlab m文件编写的DB小波分解与重构算法，直接可运行，思路清晰。

地震处理软件SAC格式数据国际通用地震格式

这是继上次上传的资源“基于simulink的并网逆变器PQ控制仿真”的逆变器另外一种典型的控制方式。VF控制与PQ控制以及Droop控制共同形成逆变器的控制策略。这份资源对于研究出储能双向逆变器（PCS）、并网逆变器、光伏逆变器、三相逆变器、单相逆变器、微网控制、微源控制等都具有重要参考作用。

制作ppt所需的一些图标

基于glut，读取点云文件 并进行绘制 包括用鼠标加键盘进行旋转、缩放、平移操作使用VS2012编写

【实例简介】STM32+按键调控PWM输出+串口输出占空比.pdf )

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

fpga实现的伺服电机控制器代码，含有梯形跟S曲线速度控制，可以参考

用可变步长的自然梯度算法解决盲信源分离问题。

我室(南京悦能科技工作室)推出的低压有源滤波器（APF）和静止无功发生器（SVG）产品为三电平结构模块化设计，完全自主研发、性能优异、价格合理。 我是做技术的，我们的有源滤波器采用目前最先进的控制技术（自我独创），实现产品的高性能高稳定性，在多个工程中已有应用。我室刚开展业务，诚心寻找合作伙伴，合作方式多样，以深入的技术合作为主，也可代理销售本工作室授权公司生产的APF、 SVG成品。若贵公司有这方面的业务需求欢迎联系参观合作。:负载建模Three load棖据实际负载情况设置负载参数,下图分别为三相三线制和三相四线制仿真的负载连接C三相三线值负载为:三相整流负载。Three LoadI模块展开「mm「三相四线值负载为:单相整流负载。四:有源滤波器主回路建模如下图,各AP主回路模块接口图。注意图中有以下区别1,三柱三线制AFF的强电输入为A,B、C三根电源线:三四线制APF的强电输入为A、B、C、N四根电源线2,APF_2 level_3p3的调旨系数为3路,产生驱动脉冲6谘PWM信号APF2 level_3p4L的阗亐系数为4路,产生驱动脉冲8路PWM信号;APF3 level3p3L和APF3 level3p4L的凋节系数都为3路,产生驱动脉冲12路PWM信号;3,在AP回路,四种APF都需婁采集3路APF电流信号;但是APF2 leyel3p3L和APF2 elevel3p4L直流母线电压只有一路,只采集一路,APF3levl3p3L和APF3 dlevel_3puL直流母线电压有两路,采集两路yd- char geCnangevdc lpwmInVuuVdr m出gAl changhnresA onAFF2|乳Lapf leve 4LFF3Eve孔以下匹图,分别为APF2 level_3p3L、APP2leel3p4L、APF3 level3p3LAPF3lvel3p4L的拓扑结构」APF2eve3p3主拓扑逆APF2eve3p4L宇拓扑本本4本本本本本本本本TadTAPF3evel3p3主拓扑本本x本小本本本本小本CrrtiCYF本本山本本时本La 2 LeI」APF_3leve3p4L主拓扑APF2 level3p3主叫路建模如下图,APF2leve3p仅多一路线IGBT和进线电抗PMM領电流检测的直流突波容甲容滤波回路主电抗模拟杂数电感APF_2leve1_3p3L主回路建模AFF3evl建模如下图,APF3evel3p3和APF3 level3p4L网别在于装置N点是否连接电网N线。电流检测PW_an主电抗0.0013·1均压电阻宰牛C=9220uF和幸+∞苧滤波板GB逆变回路0直流电压检测APF3leve1主回路建模五:电压、电流检测回路建模电压、电流信号釆集电路实昒在数据釆集板或滮波板(50A模块)上,包括3路电源电压釆集SV、直流母线电压CV釆集(两电平1路,三电平2路)、3路AP电流AI采集3路负载电流II采集。1,电网电压SvV采样处理如下图,采用原副边电压比为300707380V系统的电压互感器。PT changESv changer07300res n2,直流母线电压〔V釆样处理如下图,额定电压375V(三电平,两路路)或750V(两电平,一路)的直流母线电压,通过R1电阻网络转化为75mA左右的电流信号,通过L25P(原副边电流比为15)转换,再通过主控板上的200欧接收电阻R转化为电压信号(此电玉信号为375V左右,若追求更精准可调节主控板上的运放电阻,使其进入AD转换芯片时变为7V左右的信号)。-E SenT1c2751150002三宅平直流母线采样两路vocc ant日=2HaT〓n11两电平直流母线采样-路3,APF电流AI釆样处理如下图,50A模块釆用原副边电流比为10001)的电流传感器IP-128:控制板上接收电阻为50欧。Rcang=ChangCXchange50re-311090(50A only)4,负载电流I釆样处理如下图, CTl change为负载现场安装的互感器图中为100045A的,CT2 change为滤波板模块釆用原副边电流比为10004)的电流传感器IP-128:滤波板上通过接收2000欧电阻转为电压信号CHange2000n2.I0arno六:主控板控制部分建模61概述妇下图,APF内部控制主要包括模块 APF RIFI(主要作月是得AFF给定电流信号、APF反缋电流信号、电网电压信号)、闭环控制模块ⅣI,两电平三相四线制还多一个N线调节系数产生模块APF RIF 2记rY ntLrGsAl ntAPF21eve1_3p3L和APF31eve1内部控制框图N通可LLI intad res川finAl inAPF_21evel_3p4L内部控制框图62:主控板控制部分模块 APF RIF作用模块 APF RIFI作用如下1,对所有集量进行量化处理;2,对采样到得AFF电流信号进行合适的数字滤波、限幅等处理,获得AF反馈电流信号 Al res;对采样到得电网电压信号进行合适的数字滤波、限塥等处理,获电网