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本书第一章系统讲述 MATLAB6.5 的集成工作平台，引导读者初会MATLAB 基本用法。 第二、三章系统叙述MATLAB6.1 的四大基本数据类型（数值数组、字符串数组、元胞数组、构架数组），至于其它扩展数据类型（函数句柄、符号数据、内联函数、unit 数组、稀疏类）则另辟章节专述。此后，本书用九个独立章分述MATLAB6.5 的数值计算、符号计算、函数和数据可视、面向对象编程、GUI 交互操作界面设计、EXE 独立应用程序生成、实现不同软件平台交互的API、M-book 数据图形文字环境集成等八大通用功能。本书用专门的一章深入浅出地阐明SIMULINK 的分层建模、仿真功能、与MATLAB 交互的功能。(This book chapter describes the system integration work MATLAB6.5 platform to guide the reader will be the beginning of the basic usage of MATLAB. Second, the system described in Chapter Four MATLAB6.1 basic data types (numeric arrays, string arrays, cell arrays, structure arrays), while the other extended data types (function handle, symbol data, inline functions, unit array , sparse class) then open another chapter specifically stated. Since then, the book chapters are described by nine independent MATLAB6.5 the numerical computation, symbolic computation, functions, and data visualization, object-oriented programming, GUI interactive interface design, EXE standalone application generation software platform to achieve interaction of different API, M-book data in graphical environment for integration of the eight general-purpose language features. This book devoted a chapter in layman s language to clarify the hierarchical SIMULINK modeling, simulation capabilities, and the MATLAB int)

that the performance is improved by increasing N, and probability of detection can be improved by increasing N value even if the SNR is much lower, as long as N is large enough without noise uncertainty.

matlab dfig for ssr study

MEDFILT3 1-D, 2-D and 3-D median filtering.

GlobalPositioningSys - This diskette (version 1.0) contains demonstration programs and source codes in MATLAB (v.5.2) for algorithms listed in the textbook Global Positioning Systems, I(GlobalPositioningSys- This diskette (version 1.0) contains demonstration programs and source codes in MATLAB (v.5.2) for algorithms listed in the textbook Global Positioning Systems, I)

MATLAB s-function程序标准模板，改动即可用。(MATLAB s-function program standard template modification to use.)

compute n-dimensional histogram

Abstract There are a number of interesting variable selection methods available beside the regular forward selection and stepwise selection methods. Such approaches include LASSO (Least Absolute Shrinkage and Selection Operator), least angle regression (LARS) and elastic net (LARS-EN) regression. There also exists a method for calculating principal components with sparse loadings. This software package contains Matlab implementations of these functions. The standard implementations of these functions are available as add-on packages in S-Plus and R. Keywords LASSO, LARS, SPCA, Matlab, Elastic Net, Sparse, Sparsity, Variable selection

资料包含了matlab处理图像配准的实例代码和原图片(Information contained matlab image processing registration code examples and original pictures)

实现光伏发电系统中MPPT功能，实现PSO算法的m文件(Realize the MPPT function in the photovoltaic power generation system and realize the m file of PSO algorithm.)