相干光通信的matlab离线处理代码 DSP

[x,f]= L01p_e(c,A,b,N)用枚举法求解下列 0-1线性规划问题min f=c *x, s.t. A*x<=b,x的分量全为整数0或1, 其中N表示约束条件 Ax ≤ b中的前N个是等式，N= 0时可以省略。 返回结果x是最优解，f是最优解处的函数值。 ([X, f] = L01p_e (c, A, b, N) with the following enumeration method for solving linear programming problems 0-1 min f = c ' * x, st A* x < = b, x is an integer of all components 0 or 1, where N represents the constraints Ax ≤ b in the equation of the first N, N = 0 can be omitted. Return result x is the optimal solution, f is the function value at the optimal solution.)

uic513标准的matlab算法。振动，分垂横纵3个方向，输入，经matlab算法得出振动舒适度的指标(uic513 matlab)

精通matlab图像处理，书籍配套源码资料(Proficient in matlab image processing, books supporting source data)

带matlab界面的FIR滤波器窗化设计，应用fft实现序列通过数字滤波器。(With matlab interface window of FIR filter design, FFT is applied to implement sequence through the digital filter.)

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用matlab设计的一个界面，能够自主添加按钮(Matlab interface design, able to independently add button)

Implementation of fulkerson alghoritm, graph theory. Full source code in C++.

MATLAB仿真在通信与电子工程中的应用（第二版）实例(Examples of MATLAB simulation applications in communications and electronic engineering (second edition))

本人自编的一个基于matlab平台的定时摄像的GUI界面程序，在定时方面，考虑了程序运行的时间，即严格按照定时间隔进行拍摄图片，并可设置任意路径(A good matlab program, written by myself, is used for capturing images at a fixed interval of time.)

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)