A_Tutorial_on_Principal_Component_Analysis

Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but (sometimes) poorly understood. The goal of this paper is to dispel the magic behind this black box. This manuscript focuses on building a solid intuition for how and why principal component analysis works. This manuscript crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA. This tutorial does not shy away from explaining the ideas informally, nor does it shy away from the mathematics. The hope is that by addressing both aspects, readers of all levels will be able to gain a better understanding of PCA as well as the when, the how and the why of applying this technique.

C++ for Neural-Networks-and fuzzy logic

在实际开发项目中，使用MATLAB的神经网络工具箱和模糊逻辑工具箱设计算法然后用Coder将算法转换为高级语言。然后使用过Coder的用户就知道，一定有部分应用场景需要直接用高级语言编写算法。这本书就是为此目的编写。 书是以网页形式组织的，其中ewtoc.htm是目录。主要章节如下。 ------------------------------------------------------------- • Chapter 1 gives you an overview of neural network terminology and nomenclature. You discover that neural nets are capable of solving complex problems with parallel computational architectures. The Hopfield network and feedforward network are introduced in this chapter. • Chapter 2 introduces C++ and object orientation. You learn the benefits of object-oriented programming and its basic concepts. • Chapter 3 introduces fuzzy logic, a technology that is fairly synergistic with neural network problem solving. You learn about math with fuzzy sets as well as how you can build a simple fuzzifier in C++. • Chapter 4 introduces you to two of the simplest, yet very representative, models of: the Hopfield network, the Perceptron network, and their C++ implementations. • Chapter 5 is a survey of neural network models. This chapter describes the features of several models, describes threshold functions, and develops concepts in neural networks. • Chapter 6 focuses on learning and training paradigms. It introduces the concepts of supervised and unsupervised learning, self-organization and topics including backpropagation of errors, radial basis function networks, and conjugate gradient methods. • Chapter 7 goes through the construction of a backpropagation simulator. You will find this simulator useful in later chapters also. C++ classes and features are detailed in this chapter. • Chapter 8 covers the Bidirectional Associative memories for associating pairs of patterns. • Chapter 9 introduces Fuzzy Associative memories for associating pairs of fuzzy sets. • Chapter 10 covers the Adaptive Resonance Theory of Grossberg. You will have a chance to experiment with a program that illustrates the working of this theory. • Chapters 11 and 12 discuss the Self-Organizing map of Teuvo Kohonen and its application to pattern recognition. • Chapter 13 continues the discussion of the backpropagation simulator, with enhancements made to the simulator to include momentum and noise during training. • Chapter 14 applies backpropagation to the problem of financial forecasting, discusses setting up a backpropagation network with 15 input variables and 200 test cases to run a simulation. The problem is approached via a systematic 12-step approach for preprocessing data and setting up the problem. You will find a number of examples of financial forecasting highlighted from the literature. A resource guide for neural networks in finance is included for people who would like more information about this area. • Chapter 15 deals with nonlinear optimization with a thorough discussion of the Traveling Salesperson problem. You learn the formulation by Hopfield and the approach of Kohonen. • Chapter 16 treats two application areas of fuzzy logic: fuzzy control systems and fuzzy databases. This chapter also expands on fuzzy relations and fuzzy set theory with several examples. • Chapter 17 discusses some of the latest applications using neural networks and fuzzy logic.

MATLAB Coder User Guide 2015

MATLAB Coder User Guide 2015（官方文档） 在MATLAB中开发完算法后，往往需要把算法转换成高级语言的源程序，之后对转化的源程序再进行工程化开发。Coder可以帮助开发人员自动化的完成大部分转化功能。尽管一些与矩阵运算的功能仍然要手动优化，但是框架代码的转化可以自动化实现。

MATLAB 2014b 安装序列号

MATLAB2014b; 安装序列号（测试环境ubuntu 14.10， 64位）

MATLAB 2014b 许可协议文件

MATLAB 2014b 许可协议文件（Linux，64位，Ubuntu14.10已测试）

MATLAB 2014b 破解文件

MATLAB 2014b Linux破解文件, libmwservices.so

MATLAB 2014b 安装镜像修改文件

MATLAB 2014b 安装镜像修改文件，用于替换镜像中原有install.jar，否则产品列表中只有有限的几项可以选择。

Nerual Network Design(神经网络设计, Hagan)

Nerual Network Design (2nd Edition) Content Ch 2 Neuron Model and Network Architectures Ch 3 An Illustrative Example Ch 4 Perceptron Learning Rule Ch 5 Signal and Weight Vector Spaces Ch 6 Linear Transformations for Neural Networks Ch 7 Supervised Hebbian Learning Ch 8 Performance Surfaces and Optimum Points Ch 9 Performance Optimization Ch 10 Widrow-Hoff Learning Ch 11 Backpropagation Ch 12 Variations on Backpropagation Ch 13 Generalization Ch 14 Dynamic Networks Ch 15 Associative Learning Ch 16 Competitive Networks Ch 17 Radial Basis Networks Ch 18 Grossberg Network Ch 19 Adaptive Resonance Theory Ch 20 Stability Ch 21 Hopfield Network Ch 22 Practical Training Issues Ch 23 Case Study 1:Function Approximation Ch 24 Case Study 2:Probability Estimation Ch 25 Case Study 3:Pattern Recognition Ch 26 Case Study 4: Clustering Ch 27 Case Study 5: Prediction

Neural Network Toolbox Getting Started Guide

神经网络现在又重新回到很多人关注的焦点中，所以特地把MATLAB 2014A中有关神经网络的工具箱使用资料分享给大家。 以下是目录摘要： ----------------------------------------------------- Automatic Script Generation . . . . 1-6 Neural Network Toolbox Applications . 1-7 Neural Network Design Steps . . . . . 1-9 Fit Data with a Neural Network . . . 1-10 Defining a Problem. . . . . . . . . . 1-10 Using the Neural Network Fitting Tool . . 1-12 Using Command-Line Functions . . . . . . . 1-23 Classify Patterns with a Neural Network . . 1-33 Defining a Problem . . . . . . . . . . .. . 1-33 Using the Neural Network Pattern Recognition Tool .. 1-35 Using Command-Line Functions . . . . . . . . . . . . 1-47 Cluster Data with a Self-Organizing Map . . . . . . 1-56 Defining a Problem . . . . . . . . . . . . . . . . . 1-56 vii Using the Neural Network Clustering Tool ... . . 1-57 Using Command-Line Functions . . . . . . . . .. . . 1-67 Neural Network Time Series Prediction and Modeling . . . . . . . . . . . . . . . . . . . 1-75 Defining a Problem . . . . . . . . . . . . . . 1-75 Using the Neural Network Time Series Tool . . . . . 1-76 Using Command-Line Functions . . . . . . . . . . . . 1-88 Parallel Computing on CPUs and GPUs . . . . . . . . .1-99 Parallel Computing Toolbox . . . . . . . . . . . . . 1-99 Parallel CPU Workers . . . . . . . . . . . . . . . . 1-99 GPU Computing . . . .. . . . . . . . . . . . . . . . 1-100 Multiple GPU/CPU Computing . . . . . . . . . . . . . 1-100 Cluster Computing with MATLAB Distributed Computing Server . . . . . . . . . . . . . . . . . . . . . . . 1-101 Load Balancing, Large Problems, and Beyond . . . . . 1-101 Neural Network Toolbox Sample Data Sets . . . . . . 1-103

Windows System Programming 勘误表和注释

这是Windows System Programming的勘误表，供参考。