mudulation

模拟实现非线性的时候 ，机器人定位与构图的情况。(SLAm the code)

Perfect for the beginner, this demo illustrates simple object detection

实现简单的MATLAB矩阵生成，作为初学MATLAB的初级接触。(Simple MATLAB matrix generation)

说明：  BP神经网络识别手写体数字，BP(back propagation)神经网络是1986年由Rumelhart和McClelland为首的科学家提出的概念，是一种按照误差逆向传播算法训练的多层前馈神经网络，是应用最广泛的神经网络。(Recognition of handwritten digits by BP neural network)

对于matlab进行白塞尔打的主题结算的正反算都进行了详细的编程，希望能够有用！(For matlab for white s theme of the settlement is playing positive and negative, the paper makes a detailed programming, hope to useful! )

北京航空航天大学出版社出版，江志红编著的,深入浅出数字信号处理,的pdf文件扫描版，及书中Matlab程序(Beijing University of Aeronautics and Astronautics Press, Jiang Zhihong edited version layman pdf file scanning digital signal processing, and the book Matlab procedures)

A Binary Particle Swarm Optimization for Optimal Placement and Sizing of Capacitor Banks in Radial Distribution Feeders with Distorted Substation Voltages

说明：  移动通信信道建模与仿真 1．验证实验原理中所述的相关程序，CLARK, Jakes(Rayleigh.m) 信道模型。 2．验证QPSK调制信号经根升余弦滚降成形滤波之后在瑞利衰落信道下系统误比特和误符号性能，实现参考程序ex11main.m，ex11.mdl。(Modeling and Simulation of mobile communication channel)

This the program for Generalized synchronization of fractional order Hyperchaotic chen system

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)