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matlab改进人工势场法模拟机器人路径规划,避障
在论坛上搜了一些matlab人工势场法程序,但很多都有一些问题,比如目标不可达,角度计算错误等等。在这些的基础上,我进行了改进,使得能够更好地到达目标点,减少了震荡,但是依旧会出现几次震荡,愿高手继续完善。程序可以用C写出来,做机器人路径规划或者避障的同学,该文档有很高的参考性。
- 2020-06-14下载
- 积分:1
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北邮通信原理 第三版 周炯槃 课后习题答案详解
北邮通信原理 第三版 周炯槃 课后习题答案详解 主要是1-6章节习题答案,可供作业和复习使用,考试很多原题的
- 2020-12-12下载
- 积分:1
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用Matlab绘制发动机万有特性曲线
用Matlab绘制发动机万有特性曲线程序代码
- 2020-12-03下载
- 积分:1
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扩展卡尔曼滤波器的matlab代码
扩展卡尔曼滤波器的matlab仿真。希望能对大家有所帮助。
- 2020-12-01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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全息谱Matlab程序
西安交大屈梁生院士所著全息诊断原理书中的自带程序
- 2020-12-11下载
- 积分:1
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plc水位控制系统 plc程序 流程图
plc水位控制系统 plc程序 流程图
- 2020-12-03下载
- 积分:1
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基于DTW算法的语音识别原理与Matlab实现〔论文说明+源代码〕
以一个能识别数字0~9的语音识别系统的实现过程为例,阐述了基于DTW算法的特定人孤立词语音识别的基本原理和关键技术。其中包括对语音端点检测方法、特征参数计算方法和DTW算法实现的详细讨论,最后给出了在Matlab下的编程方法和实验结果。
- 2020-12-12下载
- 积分:1
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超声波测距PCB图
【实例简介】原创,基于单片机的超声波测距PCB图,本人自制,硬件已经经过测试,可以直接拿去PCB工厂印制,系统工作正常,上传PCB图供大家参考
- 2021-11-01 00:34:00下载
- 积分:1
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基于verilog的电风扇控制器代码
【实例简介】此设计是采用VERILOG代码设计,可用于任何FPGA开发板,此程序为西安邮电学院课程设计报告,已经过严格测试。
- 2021-11-16 00:41:14下载
- 积分:1