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原创 Anomaly Detection and Recommender Systems

Anomaly Detection and Recommender SystemsAnomaly Detection1.1 Gaussian (Normal) distribution1.2 Program: Estimate Gaussian1.3 Selecting the threshold, ϵ\epsilonϵselectThreshold: select the "best" ϵ\epsilonϵ based on the F1 scoreRecommender System2.1 Colla

2020-08-30 21:45:15 342

转载 Windows 使用 Clion 编译 (Python 调用 C++)方法总结

@[TOC](Windows 使用 Clion 编译 (Python 调用 C++)方法总结) 环境 Windows 10 Clion GCC 8.2.0 (MinGW.org GCC-8.2.0-5) Boost 1.17.0 参考阅读： Windows 安装 Boost Python 并使用 Visual Studio 2019 或 Clion 编译 (Python 调用 C++) Boost 安装安装 GCC (MinGW) 参考文章： Windows环境下MinGW/gcc安装和环境配置

2020-08-18 16:18:12 1614

原创【K-Means Clustering and Principal Component Analysis】

K-Means Clustering总结 a method to automatically cluster similar data examples together （Unsupervised） given a training set${x^{(1)},\ldots x^{(m)}} $where ), and want to group the data into a few cohesive ‘clusters’.

2020-08-16 11:31:07 436

原创 [SVM支持向量机]：Hyperparameter “C, sigma“ selection

Andrew Ng 机器学习笔记 SVM 公式总结 Cost function for SVM Simularity function (Gaussian Kernal) MatLab 代码： Gridsearch，通过计算最小validation error来选择C和sigma function [C, sigma] = dataset3Params(X, y, Xval, yval) %DATASET3PARAMS returns your choice of C and sigma for P

2020-08-07 15:17:17 721

原创 [Support Vector Machine (SVM)]: Gaussian Kernel Matlab Implementation

SVM Gaussian Kernal function sim = gaussianKernel(x1, x2, sigma) %RBFKERNEL returns a radial basis function kernel between x1 and x2 % sim = gaussianKernel(x1, x2) returns a gaussian kernel between x1 and x2 % and returns the value in sim % Ensure tha

2020-08-07 14:36:37 600

原创多线程UI更新时间小程序 Python Qt

多线程更新UI数据（在两个线程中传递数据） Signal（）和 Slot（）进行连接： pyqtSignal 通过emit()方法触发信号 pyqtSignal通过connect()方法与目标function进行连接

2020-08-03 16:38:25 312

转载 Event 模块在python threading中的使用方法

threading.Event()的使用方法：包括set()， clear()， wait()等等 Event object is one of the simplest mechanisms for communication between threads: one thread signals an event and other threads wait for it We’re using multiple threads to spin separate operations off to r

2020-08-03 16:24:27 399

原创 Python Threading Timer的使用方法

介绍python 多线程的Timer使用方法

2020-08-03 13:51:14 1405

原创 PC-Arduino Serial communication using python

PC-Arduino Serial communication介绍PC: python serial 程序Arduino代码介绍记录最简单的用python和Arduino实现异步通信的方法，通过使用python serial 模块让Arduino 的build-in LED实现开关

2020-07-28 14:10:28 294

原创记录几个学习各种编程语言/开发的资源和网址

记录python，/C/C++/嵌入式，QT，Machine learning等优质学习资源和网站1. 嵌入式学习 (Embedded system)2. Python学习(1) Python 基础和进阶(2) PyQT, PySide2图形界面GUI设计(3) Python 机器学习，深度学习(4) Python socket 编程(5) Python 多线程编程3. Linux4. Web 开发 1. 嵌入式学习 (Embedded system) 以下选自知乎嵌入式AI从入门到放肆树莓派（以及各

2020-07-26 22:06:50 349

原创 ROS Melodic中同时使用python2和python3方法

ROS Melodic中同时使用python2和python3方法问题引入解决方案注意事项问题引入 ROS Melodic的默认环境是python2, 最新发布的ROS noetic才完全支持python3。因此对用ROS Melodic开发项目，同时需要使用python3编写程序的使用者造成了一定的困扰。希望这篇文章可以有所帮助目标：在对原有python2代码不造成损害的前提下，开发用python3编写额外的ROS node. 在同一个ROS workspace下保证python2和pytho

2020-07-13 23:15:05 9520 9

Model Free Adaptive Control Theory and Applications

by Hou, Zhongsheng Jin, Shangtai Introduction ...........................................................................................1 1.1 Model-Based Control ........................................................................1 1.1.1 Modeling and Identification .................................................1 1.1.2 Model-Based Controller Design ...........................................3 1.2 Data-Driven Control .........................................................................5 1.2.1 Definition and Motivation of Data-Driven Control .............6 1.2.2 Object of Data-Driven Control Methods..............................7 1.2.3 Necessity of Data-Driven Control Theory and Methods ........................................................................8 1.2.4 Brief Survey on Data-Driven Control Methods..................10 1.2.5 Summary of Data-Driven Control Methods.......................15 1.3 Preview of the Book.........................................................................16 2 Recursive Parameter Estimation for Discrete-Time Systems................19 2.1 Introduction ....................................................................................19 2.2 Parameter Estimation Algorithm for Linearly Parameterized Systems.....................................................................20 2.2.1 Projection Algorithm..........................................................21 2.2.2 Least-Squares Algorithm ....................................................22 2.3 Parameter Estimation Algorithm for Nonlinearly Parameterized Systems.....................................................................27 2.3.1 Projection Algorithm and Its Modified Form for Nonlinearly Parameterized Systems...............................27vi ◾ Contents 2.3.2 Least-Squares Algorithm and Its Modified Form for Nonlinearly Parameterized Systems...............................32 2.4 Conclusions.................................................................................... 44 3 Dynamic Linearization Approach of Discrete-Time Nonlinear Systems................................................................................45 3.1 Introduction ....................................................................................45 3.2 SISO Discrete-Time Nonlinear Systems ..........................................47 3.2.1 Compact Form Dynamic Linearization..............................47 3.2.2 Partial Form Dynamic Linearization..................................53 3.2.3 Full Form Dynamic Linearization......................................59 3.3 MIMO Discrete-Time Nonlinear Systems...................................... 64 3.3.1 Compact Form Dynamic Linearization............................. 64 3.3.2 Partial Form Dynamic Linearization................................. 66 3.3.3 Full Form Dynamic Linearization......................................69 3.4 Conclusions.....................................................................................71 4 Model-Free Adaptive Control of SISO Discrete-Time Nonlinear Systems................................................................................75 4.1 Introduction ....................................................................................75 4.2 CFDL Data Model Based MFAC ................................................... 77 4.2.1 Control System Design...................................................... 77 4.2.2 Stability Analysis ................................................................80 4.2.3 Simulation Results..............................................................87 4.3 PFDL Data Model Based MFAC.....................................................93 4.3.1 Control System Design.......................................................93 4.3.2 Stability Analysis ................................................................96 4.3.3 Simulation Results............................................................104 4.4 FFDL Data Model Based MFAC...................................................108 4.4.1 Control System Design.....................................................108 4.4.2 Simulation Results............................................................113 4.5 Conclusions................................................................................... 118 5 Model-Free Adaptive Control of MIMO Discrete-Time Nonlinear Systems..............................................................................119 5.1 Introduction .................................................................................. 119 5.2 CFDL Data Model Based MFAC ..................................................120 5.2.1 Control System Design.....................................................120 5.2.2 Stability Analysis ..............................................................124 5.2.3 Simulation Results............................................................132 5.3 PFDL Data Model Based MFAC..................................................

2020-07-26

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