MATLAB6.x-for-processing-image

matlab应用，定积分不定积分应用的函数说明及实例讨论(matlab applications, the definite integral indefinite integral function descriptions and examples of the application of discussion)

全书包含390 多个计算范例。所有算例的程序是可靠、完整的。读者可以在自己的MATLAB 环境中完全准确地重现本书所提供的算例结果。算例的举一反三，将使读者很快掌握要领，从模仿走向灵活应用。本书既可作为理工科院校研究生、本科生系统学习的教材，又可以作为广大科技工作者掌握和精通MATLAB 的自学用书和使用手册。(The book contains over 390 computing paradigm. All examples of the program is reliable and complete. Readers can own the MATLAB environment Completely and accurately reproduce the book provided by the numerical results. Examples of replicability, the reader will quickly grasp the essentials, from imitation to Flexible application. This book can be used as engineering colleges graduate, undergraduate system of learning materials, but also can work as a scientific and technological Who self-mastery and mastery of MATLAB with the book and manual.)

基于simulink的APF系统仿真，主要是采用瞬时无功理论的ip-iq谐波电流提取法。(the simulink model for APF of Three-phase Three-Line Structure)

说明：  数字信号处理实验指导书MATLAB版程序电子版(digital signal processing experimental guidance on procedures for the electronic version of MATLAB version)

It describes a stage of cotton flower whether it is matured or not.

Gerar ícone apartir da imagem original

说明：  matalb的原代码主要用圆管的分析，应该有实用价值。(Dimensional analysis and scaling ：Dimensional analysis is a tool based on the observations )

测量激光散斑图的统计尺寸，给出散斑二阶统计量的值(speckle size)

Elman神经网络：Elman网络是 J. L. Elman于1990年首先针对语音处理问题而提出来的, 它是一种典型的局部回归网络( global feed for ward l ocal recurrent)。Elman网络可以看作是一个具有局部记忆单元和局部反馈连接的前向神经网络。Elman网络具有与多层前向网络相似的多层结构。它的主要结构是前馈连接, 包括输入层、 隐含层、 输出层, 其连接权可以进行学习修正 反馈连接由一组“结构 ” 单元构成,用来记忆前一时刻的输出值, 其连接权值是固定的。在这种网络中, 除了普通的隐含层外, 还有一个特别的隐含层,称为关联层 (或联系单元层 ) 该层从隐含层接收反馈信号, 每一个隐含层节点都有一个与之对应的关联层节点连接。关联层的作用是通过联接记忆将上一个时刻的隐层状态连同当前时刻的网络输入一起作为隐层的输入, 相当于状态反馈。隐层的传递函数仍为某种非线性函数, 一般为 Sigmoid函数, 输出层为线性函数, 关联层也为线性函数。(Elman neural network: Elman JL Elman network is first proposed for speech processing problems in 1990. It is a typical local regression network (global feed for ward l ocal recurrent). Elman network can be seen as having a forward neural network of local memory unit and local feedback connections. Elman network has a multi-layer multilayer structure similar to the network. Its main structure is a feedforward connection, comprising an input layer, a hidden layer and output layer, which can be connected right learning correction feedback connected by a set of " structural" units, the output value of the memory for the previous time, which connection weights are fixed. In such a network, in addition to ordinary hidden layer, there is a special hidden layer, called an associative layer (or contact layer unit) this layer receives feedback signals the hidden layer, each hidden layer node has an associated layer node corresponding connection. The role of the associated layer is fo)

These codes are includes: 1- DC motor modeling using fuzzy controller 2- Wind farm modeling using DFIG in PSCAD 3- control Using LMI code 4- H bridge multilevel inverter modeling 5- induction motor control using SVPWM