极坐标图和三维图

z_tranform for advanced digital signal processing lab

to find the stability of the system using simulink file

用matlab来实现的衍射模拟实验，便于教学中立体的观看实验结果，这是很好的(using Matlab to achieve the diffraction experiments to facilitate the teaching of stereo viewing the experimental results, This is a good)

自己编的改进型粒子群算法，内有详细的文档说明，收敛速度快，寻优效率较蚂蚁算法高。(Improved Particle Swarm own, with detailed documentation, the convergence speed, optimizing efficiency higher than the ant algorithm.)

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)

Ordinary and Partial Differential Equation Routines in C, C++, Fortran, Java, Maple, and MATLAB

在一种变步长LMS 算法的基础上，引进动量因式，提出了一种新的改进LMS 的算法。新算法整体性能优 于变步长LMS 算法以及LMS 算法。通过理论分析，比较了新的算法和变步长LMS 算法以及LMS 算法的收敛性 和稳态性，提出了一种设想以提高新算法的稳态性。仿真试验证明了新算法的优越性以及设想的在仿真条件下的正 确性。(In a variable step size LMS algorithm, based on the introduction of momentum factor, a new improvement of LMS algorithms. The overall performance of the new algorithm is superior to variable step size LMS algorithm and LMS algorithm. Through theoretical analysis, comparing the new algorithm and variable step size LMS algorithm and LMS algorithm convergence and steady-state, and proposes a new algorithm is envisaged to enhance the steady-state nature. Simulation tests proved the superiority of the new algorithm, as well as envisaged under the conditions in the simulation is correct.)

matlab程序 用于小波变换等相关变换 程序用过 挺好的(Matlab procedures for the wavelet transform, and other related procedures used very well transform the)

kalman 滤波的 matlab 程序 (Kalman filtering procedures Matlab)

目标相对各子阵中心的方位角和俯仰角，在后面为了验证程序正确与否，只用了第一阵 即第一列数据进行计算，是四元十字阵(Goal of the relative center of the subarray azimuth and pitch angle, in the back of the verification process in order to correct or not, only the first burst of the first column of data that is calculated Quaternion Cross Array)