MATLAB培训

MATLAB培训

MATLAB培训,MATLAB Statistical Software

Testi...Client Testimonials

MATLAB Programming

Tomasz (the trainer) was knowledgeable and friendly and made the training very interesting. He helped me learnt a lot about a subject I was very new to.

Paul Cox - Network Rail

MATLAB Programming

课堂讨论

MATLAB大纲

代码 名字 时长 概览
matlabprog MATLAB Programming 14小时 This two-day course provides a comprehensive introduction to the MATLAB® technical computing environment. The course is intended for beginner users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Working with the MATLAB user interface Entering commands and creating variables Analyzing vectors and matrices Working with data types Automating commands with scripts Writing programs with logic and flow control Writing functions Visualizing vector and matrix data Working with data files Importing data Organizing data Visualizing data
matlab2 MATLAB 基础 21小时 MATLAB软件简介 MATLAB(矩阵实验室)是MATrix LABoratory的缩写,是一款由美国The MathWorks公司出品的商业科学计算和仿真软件.MATLAB拥有一套可用于算法开发,数据可视化,数据分析以及数值计算的高级技术计算语言和交互式环境.除了矩阵运算,求解线性系统方程,绘制函数/数据图像等常用功能外,MATLAB还可以用来创建用户界面及与调用其它语言(包括C,C++,Java,Python和FORTRAN)编写的程序。 尽管MATLAB最初主要用于科学计算,但其不断增加的各种附加工具箱(到目前为止将近100个)使之适合不同领域和行业的应用,如控制系统设计与分析,生物医疗,图像处理,信号处理与通讯,金融建模和分析,汽车,航天航空等。另外还有一个基于模型化设计(MBD)的图形化仿真软件包Simulink用于系统模拟,代码生成,动态/嵌入式系统开发等方面. 培训目的  本课程将全面介绍MATLAB科学技术计算环境,旨在于使初学者迅速掌握MATLAB原理,在课程结束后可以: -> 熟悉MATLAB界面,查找帮助; -> 键入命令,进行变量,向量和矩阵的基本操作; -> 对数据进行多种可视化展示; -> 处理数据文件和不同数据类型; -> 编写脚本和函数,并在其中包含必要的逻辑和分支控制; -> 读写文本和二进制文件 课程特色 本次课程使用MATLAB2014a用于演示。本着由浅入深,注重实践,重点问题反复强调的原则,不拘泥于PPT讲义,尽量多使用实例进行示范操作.   课程大纲 1. MATLAB产品介绍 1.1 一个例子 C vs MATLAB 1.2 MATLAB产品总览 1.3 MATLAB应用领域 1.4 MATLAB能为您做些什么 1.5 MATLAB基础课程大纲 2. 使用MATLAB界面 Use MATLAB Interface 目标: 介绍MATLAB集成开发界面的主要特性和一些基本数据,文件,图形可视化操作 2.1 MATLAB界面介绍 2.2 从文件读入数据 2.3 保存和载入变量 2.4 为数据绘制图形 2.5 绘图工具 2.6 数据基础分析和拟合工具 2.7 为其他应用导出数据 3. MATLAB变量和表达式 目标: 键入MATLAB命令,强调如何创建和访问变量中的数据 3.1 输入命令 3.2 创建变量 3.3 获得帮助 3.4 访问和修改变量值 3.5 生成字符变量 4. MATLAB向量 目标: 对向量进行数学和统计计算并使其可视化. 4.1 向量计算 4.2 向量绘图 4.3 基本绘图选项 4.4 为向量做标注 5. MATLAB矩阵 目标: 使用矩阵作为数学对象或者向量的集合,理解如何在不同的应用中使用恰当的MATLAB语法 5.1矩阵尺寸和维度 5.2 矩阵计算 5.3 矩阵统计 5.4 为多个列的数据绘制图形 5.5 改变矩阵数据排列 5.6 多维矩阵 6. MATLAB脚本 目标: 将多个MATLAB命令创建成一个脚本以便反复使用. 6.1 一个建模的例子 6.2 追溯历史命令 6.3 创建脚本 6.4运行脚本 6.5注释和代码单元 6.6发布代码 7. 处理数据文件 目标: 从文件中导入数据.对于文件中不同数据格式应用细胞阵列等混合数据类型 7.1 导入数据 7.2 混合数据类型 7.3 细胞阵列 7.4 数字,字符串和细胞阵列转换 7.5 导出数据 8. 多向量绘图 目标: 为更为复杂的向量和公式绘图并利用MATLAB命令进行标注 8.1 图形结构 8.2 绘制子图形 8.3 为公式绘制图形 8.4 使用颜色 8.5 修改图形属性 9. MATLAB逻辑和流程控制 目标: 使用逻辑操作,变量和索引技巧来创建可适用于不同条件的代码. 9.1 逻辑操作和变量 9.2 按逻辑值索引 9.3 编程结构 9.4 流程控制 9.5 循环 10. MATLAB矩阵和图像可视化 目标: 可视化二维或三维图像和矩阵数据 10.1 分散数据插值 10.2 三维矩阵可视化 10.3 二维矩阵可视化 10.4 索引图像和色彩映射表 10.5 真彩色图像 11 MATLAB数据分析 目标: 在MATLAB中执行典型的数据分析任务,包括拟合理论模型. 11.1 处理丢失数据 11.2 求解相关性 11.3 平滑数据 11.4 频域分析和傅里叶变换 12 MATLAB函数 目标: 将脚本进一步编写成函数,加大执行任务的自动化程度 12.1 为何使用函数 12.2 创建函数 12.3 添加注释 12.4 调用函数和子函数 12.5 提高编程效率 12.6 MATLAB工作区 12.7 MATLAB路径和调用优先级 13 MATLAB数据类型 目标:进一步探讨MATLAB中的数据结构和数据类型转换 13.1 数据类型总览 13.2 整数 13.3 结构体 13.4 数据类型转换 14 MATLAB文件处理 目标: 如何导入,导出,和控制底层数据以及读写文本和二进制文件 14.1 打开关闭文件 14.2 读写文本文件 14.3 读写二进制文件 15 MATLAB基础教程总结 目标: 总结MATLAB基础课程,回顾MATLAB一些重要的基本操作 15.1课程总结 15.2 其他课程   请注意实际课程可能会与上述大纲有细微差别
matlabml1 MATLAB与机器学习入门 21小时 第一课:MATLAB入门基础 1、  简单介绍MATLAB的安装、版本历史与编程环境 2、  MATLAB基础操作(包括矩阵操作、逻辑与流程控制、函数与脚本文件、基本绘图等) 3、  文件导入(mat、txt、xls、csv等格式) 第二课:MATLAB进阶与提高 1、  MATLAB编程习惯与风格 2、  MATLAB调试技巧 3、  向量化编程与内存优化 4、  图形对象和句柄 第三课:BP神经网络 1、  BP神经网络的基本原理 2、  BP神经网络的MATLAB实现 3、  案例实践 4、  BP神经网络参数的优化 第四课:RBF、GRNN和PNN神经网络 1、  RBF神经网络的基本原理 2、  GRNN神经网络的基本原理 3、  PNN神经网络的基本原理 4、  案例实践 第五课:竞争神经网络与SOM神经网络 1、  竞争神经网络的基本原理 2、  自组织特征映射(SOM)神经网络的基本原理 3、  案例实践 第六课:支持向量机(Support Vector Machine, SVM) 1、  SVM分类的基本原理 2、  SVM回归拟合的基本原理 3、  SVM的常见训练算法(分块、SMO、增量学习等) 4、  案例实践 第七课:极限学习机(Extreme Learning Machine, ELM) 1、  ELM的基本原理 2、  ELM与BP神经网络的区别与联系 3、  案例实践 第八课:决策树与随机森林 1、  决策树的基本原理 2、  随机森林的基本原理 3、  案例实践 第九课:遗传算法(Genetic Algorithm, GA) 1、  遗传算法的基本原理 2、  常见遗传算法工具箱介绍 3、  案例实践 第十课:粒子群优化(Particle Swarm Optimization, PSO)算法 1、  粒子群优化算法的基本原理 2、  案例实践 第十一课:蚁群算法(Ant Colony Algorithm, ACA) 1、  粒子群优化算法的基本原理 2、  案例实践 第十二课:模拟退火算法(Simulated Annealing, SA) 1、  模拟退火算法的基本原理 2、  案例实践 第十三课:降维与特征选择 1、  主成分分析的基本原理 2、  偏最小二乘的基本原理 3、  常见的特征选择方法(优化搜索、Filter和Wrapper等)
matlabfundamentalsfinance MATLAB 基础 35小时 MATLAB软件简介 MATLAB(矩阵实验室)是MATrix LABoratory的缩写,是一款由美国The MathWorks公司出品的商业科学计算和仿真软件.MATLAB拥有一套可用于算法开发,数据可视化,数据分析以及数值计算的高级技术计算语言和交互式环境.除了矩阵运算,求解线性系统方程,绘制函数/数据图像等常用功能外,MATLAB还可以用来创建用户界面及与调用其它语言(包括C,C++,Java,Python和FORTRAN)编写的程序。 尽管MATLAB最初主要用于科学计算,但其不断增加的各种附加工具箱(到目前为止将近100个)使之适合不同领域和行业的应用,如控制系统设计与分析,生物医疗,图像处理,信号处理与通讯,金融建模和分析,汽车,航天航空等。另外还有一个基于模型化设计(MBD)的图形化仿真软件包Simulink用于系统模拟,代码生成,动态/嵌入式系统开发等方面. 培训目的  本课程将全面介绍MATLAB科学技术计算环境,旨在于使初学者迅速掌握MATLAB原理,在课程结束后可以: -> 熟悉MATLAB界面,查找帮助; -> 键入命令,进行变量,向量和矩阵的基本操作; -> 对数据进行多种可视化展示; -> 处理数据文件和不同数据类型; -> 编写脚本和函数,并在其中包含必要的逻辑和分支控制; -> 读写文本和二进制文件 课程特色 本次课程使用MATLAB2014a用于演示。本着由浅入深,注重实践,重点问题反复强调的原则,不拘泥于PPT讲义,尽量多使用实例进行示范操作.   课程大纲 1. MATLAB产品介绍 1.1 一个例子 C vs MATLAB 1.2 MATLAB产品总览 1.3 MATLAB应用领域 1.4 MATLAB能为您做些什么 1.5 MATLAB基础课程大纲 2. 使用MATLAB界面 Use MATLAB Interface 目标: 介绍MATLAB集成开发界面的主要特性和一些基本数据,文件,图形可视化操作 2.1 MATLAB界面介绍 2.2 从文件读入数据 2.3 保存和载入变量 2.4 为数据绘制图形 2.5 绘图工具 2.6 数据基础分析和拟合工具 2.7 为其他应用导出数据 3. MATLAB变量和表达式 目标: 键入MATLAB命令,强调如何创建和访问变量中的数据 3.1 输入命令 3.2 创建变量 3.3 获得帮助 3.4 访问和修改变量值 3.5 生成字符变量 4. MATLAB向量 目标: 对向量进行数学和统计计算并使其可视化. 4.1 向量计算 4.2 向量绘图 4.3 基本绘图选项 4.4 为向量做标注 5. MATLAB矩阵 目标: 使用矩阵作为数学对象或者向量的集合,理解如何在不同的应用中使用恰当的MATLAB语法 5.1矩阵尺寸和维度 5.2 矩阵计算 5.3 矩阵统计 5.4 为多个列的数据绘制图形 5.5 改变矩阵数据排列 5.6 多维矩阵 6. MATLAB脚本 目标: 将多个MATLAB命令创建成一个脚本以便反复使用. 6.1 一个建模的例子 6.2 追溯历史命令 6.3 创建脚本 6.4运行脚本 6.5注释和代码单元 6.6发布代码 7. 处理数据文件 目标: 从文件中导入数据.对于文件中不同数据格式应用细胞阵列等混合数据类型 7.1 导入数据 7.2 混合数据类型 7.3 细胞阵列 7.4 数字,字符串和细胞阵列转换 7.5 导出数据 8. 多向量绘图 目标: 为更为复杂的向量和公式绘图并利用MATLAB命令进行标注 8.1 图形结构 8.2 绘制子图形 8.3 为公式绘制图形 8.4 使用颜色 8.5 修改图形属性 9. MATLAB逻辑和流程控制 目标: 使用逻辑操作,变量和索引技巧来创建可适用于不同条件的代码. 9.1 逻辑操作和变量 9.2 按逻辑值索引 9.3 编程结构 9.4 流程控制 9.5 循环 10. MATLAB矩阵和图像可视化 目标: 可视化二维或三维图像和矩阵数据 10.1 分散数据插值 10.2 三维矩阵可视化 10.3 二维矩阵可视化 10.4 索引图像和色彩映射表 10.5 真彩色图像 11 MATLAB数据分析 目标: 在MATLAB中执行典型的数据分析任务,包括拟合理论模型. 11.1 处理丢失数据 11.2 求解相关性 11.3 平滑数据 11.4 频域分析和傅里叶变换 12 MATLAB函数 目标: 将脚本进一步编写成函数,加大执行任务的自动化程度 12.1 为何使用函数 12.2 创建函数 12.3 添加注释 12.4 调用函数和子函数 12.5 提高编程效率 12.6 MATLAB工作区 12.7 MATLAB路径和调用优先级 13 MATLAB数据类型 目标:进一步探讨MATLAB中的数据结构和数据类型转换 13.1 数据类型总览 13.2 整数 13.3 结构体 13.4 数据类型转换 14 MATLAB文件处理 目标: 如何导入,导出,和控制底层数据以及读写文本和二进制文件 14.1 打开关闭文件 14.2 读写文本文件 14.3 读写二进制文件 15 MATLAB基础教程总结 目标: 总结MATLAB基础课程,回顾MATLAB一些重要的基本操作 15.1课程总结 15.2 其他课程   请注意实际课程可能会与上述大纲有细微差别
matlabfincance Matlab for Finance 14小时 MATLAB integrates computation, visualization and programming in an easy to use environment. It offers Financial Toolbox, which includes the features needed to perform mathematical and statistical analysis of financial data, then display the results with presentation-quality graphics. This instructor-led training provides an introduction to MATLAB for finance. We dive into data analysis, visualization, modeling and programming by way of hands-on exercises and plentiful in-lab practice. By the end of this training, participants will have a thorough understanding of the powerful features included in MATLAB's Financial Toolbox and will have gained the necessary practice to apply them immediately for solving real-world problems. Audience      Financial professionals with previous experience with MATLAB Format of the course     Part lecture, part discussion, heavy hands-on practice Overview of the MATLAB Financial Toolbox Objective: Learn to apply the various features included in the MATLAB Financial Toolbox to perform quantitative analysis for the financial industry. Gain the knowledge and practice needed to efficiently develop real-world applications involving financial data. Asset Allocation and Portfolio Optimization Risk Analysis and Investment Performance Fixed-Income Analysis and Option Pricing Financial Time Series Analysis Regression and Estimation with Missing Data Technical Indicators and Financial Charts Monte Carlo Simulation of SDE Models Asset Allocation and Portfolio Optimization Objective: perform capital allocation, asset allocation, and risk assessment. Estimating asset return and total return moments from price or return data Computing portfolio-level statistics, such as mean, variance, value at risk (VaR), and conditional value at risk (CVaR) Performing constrained mean-variance portfolio optimization and analysis Examining the time evolution of efficient portfolio allocations Performing capital allocation Accounting for turnover and transaction costs in portfolio optimization problems Risk Analysis and Investment Performance Objective: Define and solve portfolio optimization problems. Specifying a portfolio name, the number of assets in an asset universe, and asset identifiers. Defining an initial portfolio allocation. Fixed-Income Analysis and Option Pricing Objective: Perform fixed-income analysis and option pricing. Analyzing cash flow Performing SIA-Compliant fixed-income security analysis Performing basic Black-Scholes, Black, and binomial option-pricing Financial Time Series Analysis Objective: analyze time series data in financial markets. Performing data math Transforming and analyzing data Technical analysis Charting and graphics Regression and Estimation with Missing Data Objective: Perform multivariate normal regression with or without missing data. Performing common regressions Estimating log-likelihood function and standard errors for hypothesis testing Completing calculations when data is missing Technical Indicators and Financial Charts Objective: Practice using performance metrics and specialized plots. Moving averages Oscillators, stochastics, indexes, and indicators Maximum drawdown and expected maximum drawdown Charts, including Bollinger bands, candlestick plots, and moving averages Monte Carlo Simulation of SDE Models Objective: Create simulations and apply SDE models Brownian Motion (BM) Geometric Brownian Motion (GBM) Constant Elasticity of Variance (CEV) Cox-Ingersoll-Ross (CIR) Hull-White/Vasicek (HWV) Heston Conclusion  
matlabdsandreporting MATLAB Fundamentals, Data Science & Report Generation 126小时 In the first part of this training, we cover the fundamentals of MATLAB and its function as both a language and a platform.  Included in this discussion is an introduction to MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles. In the second part, we demonstrate how to use MATLAB for data mining, machine learning and predictive analytics. To provide participants with a clear and practical perspective of MATLAB's approach and power, we draw comparisons between using MATLAB and using other tools such as spreadsheets, C, C++, and Visual Basic. In the third part of the training, participants learn how to streamline their work by automating their data processing and report generation. Throughout the course, participants will put into practice the ideas learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough grasp of MATLAB' capabilities and will be able to employ it for solving real-world data science problems as well as for streamlining their work through automation. Assessments will be conducted throughout the course to guage progress. Format of the course Course includes theoretical and practical exercises, including case discussions, sample code inspection, and hands-on implementation. Note Practice sessions will based on pre-arranged sample data report templates. If you have specific requirements, please contact us to arrange Introduction MATLAB for data science and reporting   Part 01: MATLAB fundamentals Overview     MATLAB for data analysis, visualization, modeling, and programming. Working with the MATLAB user interface Overview of MATLAB syntax Entering commands     Using the command line interface Creating variables     Numeric vs character data Analyzing vectors and matrices     Creating and manipulating     Performing calculations Visualizing vector and matrix data Working with data files     Importing data from Excel spreadsheets Working with data types     Working with table data Automating commands with scripts     Creating and running scripts     Organizing and publishing your scripts Writing programs with branching and loops     User interaction and flow control Writing functions     Creating and calling functions     Debugging with MATLAB Editor Applying object-oriented programming principles to your programs   Part 02: MATLAB for data science Overview     MATLAB for data mining, machine learning and predictive analytics Accessing data     Obtaining data from files, spreadsheets, and databases     Obtaining data from test equipment and hardware     Obtaining data from software and the Web Exploring data     Identifying trends, testing hypotheses, and estimating uncertainty Creating customized algorithms Creating visualizations Creating models Publishing customized reports Sharing analysis tools     As MATLAB code     As standalone desktop or Web applications Using the Statistics and Machine Learning Toolbox Using the Neural Network Toolbox   Part 03: Report generation Overview     Presenting results from MATLAB programs, applications, and sample data     Generating Microsoft Word, PowerPoint®, PDF, and HTML reports.     Templated reports     Tailor-made reports         Using organization’s templates and standards Creating reports interactively vs programmatically     Using the Report Explorer     Using the DOM (Document Object Model) API Creating reports interactively using Report Explorer     Report Explorer Examples         Magic Squares Report Explorer Example     Creating reports         Using Report Explorer to create report setup file, define report structure and content     Formatting reports         Specifying default report style and format for Report Explorer reports     Generating reports         Configuring Report Explorer for processing and running report     Managing report conversion templates         Copying and managing Microsoft Word , PDF, and HTML conversion templates for Report Explorer reports     Customizing Report Conversion templates         Customizing the style and format of Microsoft Word and HTML conversion templates for Report Explorer reports     Customizing components and style sheets         Customizing report components, define layout style sheets Creating reports programmatically in MATLAB     Template-Based Report Object (DOM) API Examples         Functional report         Object-oriented report         Programmatic report formatting     Creating report content         Using the Document Object Model (DOM) API     Report format basics         Specifying format for report content     Creating form-based reports         Using the DOM API to fill in the blanks in a report form     Creating object-oriented reports         Deriving classes to simplify report creation and maintenance     Creating and formatting report objects         Lists, tables, and images     Creating DOM Reports from HTML         Appending HTML string or file to a Microsoft® Word, PDF, or HTML report generated by Document Object Model (DOM) API     Creating report templates         Creating templates to use with programmatic reports     Formatting page layouts         Formatting pages in Microsoft Word and PDF reports Summary and closing remarks
ipmat1 Introduction to Image Processing using Matlab 28小时 This four day course provides image processing foundations using Matlab. You will practise how to change and enhance images and even extract patterns from the images. You will also learn how to build 2D filters and apply them on the images. Examples and exercises demonstrate the use of appropriate Matlab and Image Processing Toolbox functionality throughout the analysis process. Day 1: Loading images Dealing with RGB components of the image Saving the new images Gray scale images Binary images Masks Day 2: Analyzing images interactively Removing noise Aligning images and creating a panoramic scene Detecting lines and circles in an image Day 3: Image histogram Creating and applying 2D filters Segmenting object edges Segmenting objects based on their color and texture Day 4 Performing batch analysis over sets of images Segmenting objects based on their shape using morphological operations Measuring shape properties
smlk Simulink® for Automotive System Design 28小时 Objective: This training is meant for software Engineers who are working with MBD technology,the training will cover Modelling techniques for Automotive systems, Automotive standards ,Auto-code generation and Model test harness building and verification Audience: Software developper for automotive supplierFundamentals & Basics Using the MATLAB® environment Essential Mathematics for control systems using MATLAB® Graphics and Visualization Programming using MATLAB® GUI Programming using MATLAB®(optional) Introduction to Control systems and Mathematical Modeling using MATLAB® Control Theory using MATLAB® Introduction to systems modeling using SIMULINK® Simulink® internals (signals, systems, subsystems, simulation Parameters,…etc) Stateflow for automotive systems(Automotive Body Controller application) Introduction to MAAB( Mathworks® Automotive Advisory Board) Introduction to AUTOSAR AUTOSAR SWCs modeling using Simulink® Simulink Tool boxes for Automotive systems Hydraulic Cylinder Simulation Introduction to SimDrivelin (Clutch Models ,Gera Models)(Optional) Modeling ABS (Optional ) Modeling for Automatic Code Generation Model Verification Techniques
simulinkadv Simulink® for Automotive System Design Advanced Level 14小时 Fundamentals Using the MATLAB® environment Essential Mathematics for control systems using MATLAB® Graphics and Visualization Programming using MATLAB® GUI Programming using MATLAB®(optional) Introduction to Control systems and Mathematical Modeling using MATLAB® Control Theory using MATLAB® Introduction to systems modeling using SIMULINK® Model Driven Development in Automotive Model Based versus Model-less Development Test Harness for Automotive Software System Tests Model in the Loop, Software in the Loop, Hardware in the Loop Tools for Model Based Development and Testing in Automotive Matelo Tool Example Reactis Tool Example Simulink/Stateflow Models Verifiers and SystemTest Tool Example Simulink® internals (signals, systems, subsystems, simulation Parameters,…etc)-Examples Conditionally executed subsystems Enabled subsystems Triggered subsystems Input validation model Stateflow for automotive systems(Automotive Body Controller application)-Examples Creating and Simulating a Model Create a simple Simulink model, simulate it, and analyze the results. Define the potentiometer system Explore the Simulink environment interface Create a Simulink model of the potentiometer system Simulate the model and analyze results Modeling Programming Constructs Objective: Model and simulate basic programming constructs in Simulink Comparisons and decision statements Zero crossings MATLAB Function block Modeling Discrete Systems Objective: Model and simulate discrete systems in Simulink. Define discrete states Create a model of a PI controller Model discrete transfer functions and state space systems Model multirate discrete systems Modeling Continuous Systems: Model and simulate continuous systems in Simulink. Create a model of a throttle system Define continuous states Run simulations and analyze results Model impact dynamics Solver Selection: Select a solver that is appropriate for a given Simulink model. Solver behavior System dynamics Discontinuities Algebraic loops Introduction to MAAB ( Mathworks® Automotive Advisory Board) -Examples Introduction to AUTOSAR AUTOSAR SWCs modeling using Simulink® Simulink Tool boxes for Automotive systems Hydraulic cylinder Simulation-Examples Introduction to SimDrivelin (Clutch Models, Gera Models) (Optional) -Examples Modeling ABS (Optional ) -Examples Modeling for Automatic Code Generation -Examples Model Verification Techniques -Examples Engine Model (Practical Simulink Model) Anti-Lock Braking System (Practical Simulink Model) Engagement Model (Practical Simulink Model) Suspension System (Practical Simulink Model) Hydraulic Systems (Practical Simulink Model) Advanced System Models in Simulink with Stateflow Enhancements Fault-Tolerant Fuel Control System (Practical Simulink Model)  Automatic Transmission Control (Practical Simulink Model) Electrohydraulic Servo Control (Practical Simulink Model) Modeling Stick-Slip Friction (Practical Simulink Model)
octnp Octave not only for programmers 21小时 Course is dedicated for those who would like to know an alternative program to the commercial MATLAB package. The three-day training provides comprehensive information on moving around the environment and performing the OCTAVE package for data analysis and engineering calculations. The training recipients are beginners but also those who know the program and would like to systematize their knowledge and improve their skills. Knowledge of other programming languages is not required, but it will greatly facilitate the learners' acquisition of knowledge. The course will show you how to use the program in many practical examples. Introduction Simple calculations Starting Octave, Octave as a calculator, built-in functions The Octave environment Named variables, numbers and formatting, number representation and accuracy, loading and saving data  Arrays and vectors Extracting elements from a vector, vector maths Plotting graphs Improving the presentation, multiple graphs and figures, saving and printing figures Octave programming I: Script files Creating and editing a script, running and debugging scripts, Control statements If else, switch, for, while Octave programming II: Functions Matrices and vectors Matrix, the transpose operator, matrix creation functions, building composite matrices, matrices as tables, extracting bits of matrices, basic matrix functions Linear and Nonlinear Equations More graphs Putting several graphs in one window, 3D plots, changing the viewpoint, plotting surfaces, images and movies,  Eigenvectors and the Singular Value Decomposition  Complex numbers Plotting complex numbers,  Statistics and data processing  GUI Development
matlabpredanalytics Matlab for Predictive Analytics 21小时 Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events. In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data. By the end of this training, participants will be able to: Create predictive models to analyze patterns in historical and transactional data Use predictive modeling to identify risks and opportunities Build mathematical models that capture important trends Use data to from devices and business systems to reduce waste, save time, or cut costs Audience Developers Engineers Domain experts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Introduction     Predictive analytics in finance, healthcare, pharmaceuticals, automotive, aerospace, and manufacturing Overview of Big Data concepts Capturing data from disparate sources What are data-driven predictive models? Overview of statistical and machine learning techniques Case study: predictive maintenance and resource planning Applying algorithms to large data sets with Hadoop and Spark Predictive Analytics Workflow Accessing and exploring data Preprocessing the data Developing a predictive model Training, testing and validating a data set Applying different machine learning approaches ( time-series regression, linear regression, etc.) Integrating the model into existing web applications, mobile devices, embedded systems, etc. Matlab and Simulink integration with embedded systems and enterprise IT workflows Creating portable C and C++ code from MATLAB code Deploying predictive applications to large-scale production systems, clusters, and clouds Acting on the results of your analysis Next steps: Automatically responding to findings using Prescriptive Analytics Closing remarks
matlabdl Matlab for Deep Learning 14小时 In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition. By the end of this training, participants will be able to: Build a deep learning model Automate data labeling Work with models from Caffe and TensorFlow-Keras Train data using multiple GPUs, the cloud, or clusters Audience Developers Engineers Domain experts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice To request a customized course outline for this training, please contact us.
matlabprescriptive Matlab for Prescriptive Analytics 14小时 Prescriptive analytics is a branch of business analytics, together with descriptive and predictive analytics. It uses predictive models to suggest actions to take for optimal outcomes, relying on optimization and rules-based techniques as a basis for decision making. In this instructor-led, live training, participants will learn how to use Matlab to carry out prescriptive analytics on a set of sample data. By the end of this training, participants will be able to: Understand the key concepts and frameworks used in prescriptive analytics Use MATLAB and its toolboxes to acquire, clean and explore data Use rules-based techniques including inference engines, scorecards, and decision trees to make decisions based on different business scenarios Use Monte Carlo simulation to analyze uncertainties and ensure sound decision making Deploy predictive and prescriptive models to enterprise systems Audience Business analysts Operations planners Decision makers Functional managers BI team members Format of the course Part lecture, part discussion, exercises and heavy hands-on practice To request a customized course outline for this training, please contact us.

近期课程

课程日期价格【远程 / 传统课堂】
Introduction to Machine Learning with MATLAB - 北京 - 创而新大厦星期一, 2017-12-04 09:30¥30350 / ¥35750

其它地区

MATLAB,培训,课程,培训课程, MATLAB训练,小组MATLAB课程,MATLAB课程,学习MATLAB ,MATLAB远程教育,MATLAB讲师,MATLAB周末培训,企业MATLAB培训,MATLAB晚上培训,MATLAB培训师,一对一MATLAB课程,MATLABs辅导,MATLAB辅导班,MATLAB教程,短期MATLAB培训,MATLAB私教,学MATLAB班

促销课程

订阅促销课程

为尊重您的隐私,我公司不会把您的邮箱地址提供给任何人。您可以享有优先权和随时取消订阅的权利。

我们的客户