MatlabMoments

说明：  几种前向神经网络的应用实例，用matlab编的源代码(Several prior to the application of neural network, using matlab made the source code)

用局部参数最优化方法设计一个模型参考自适应系统,可调增益的初值Kc(0)=0.2，给定值r(t)为单位阶跃信号，即r(t)=A×1(t)。 要求： 1把连续系统离散化(采样时间可取0.1)。 2编制并运行这个系统的计算机程序(注意调整B值，使系统获得较好的自适应特性)。 3记录ym、yp的曲线 记录kp×kc的曲线 记录广义输出误差e的变化曲线。 4在参数收敛后，让Kp=2变为Kp=1，重新观察Kp×Kc及e的变化曲线。 5找出在确定的B值下，使系统不稳定的A值(阶跃信号的幅值)，并与用劳斯稳定判据计算的结果比较。 (With local parameter optimization method to design a model reference adaptive system, adjustable gain initial Kc (0) = 0.2, for a given value of r (t) for the unit step signal that r (t) = A × 1 ( t). Requirements: a continuous system discretization (sampling time of 0.1 preferred). 2 compiled and run the system computer program (Note B to adjust the value of the system to obtain better adaptive characteristics). 3 records ym, yp curve record kp × kc curve records generalized output error e curves. 4 parameter convergence, let Kp = 2 into the Kp = 1, re-observed Kp × Kc and e curves. B-5 to find value in determining the next, making the system unstable A value (step signal of amplitude), and using Routh stability criterion and the comparison of the results calculated.)

Reconocimiento de patrones de voz.

generate lfm waveform

通过对卡尔曼滤波器的介绍，使用matlab编程，实现卡尔曼滤波(Through the introduction of the Kalman filter using matlab programming, Kalman filtering)

MATLAB数值分析与应算习题代码2 3节，用于学习交流，不可用商业用途。(MATLAB Numerical Analysis and Problem code 23 should be considered section for learning exchanges, can not be used for commercial purposes.)

创建一个n维的稀疏数组对象，n是任意值。 定义N可能是大于2的一类n维稀疏阵列。然而，它应该被认为是一种起动方式与普通的MATLAB稀疏矩阵和重塑它有N维度。换句话说，稀疏的数据，首先必须能够作为一个普通的2D MATLAB稀疏矩阵被前n维。事实上，如果目标数组的尺寸mxnxp……yxz，然后将它存储在内部类是一个普通的二维稀疏阵列的尺寸（M×N×P×……×Y）XZ。这导致了某些内存株时使用大量的尺寸。我发现有用的类主要用于中等尺寸像三维图像边缘检测，你经常要举行一个稀疏的3D的边缘地图。(Creates an N-dimensional sparse array object, for arbitrary N.This submission defines a class of N-dimensional sparse arrays for N possibly greater than 2. However, it should really be thought of as a way of starting with an ordinary MATLAB sparse matrix and reshaping it to have N dimensions. In other words, the sparse data must first be able to exist as an ordinary 2D MATLAB sparse matrix before being made N-dimensional. In fact, if the intended array has dimensions MxNxP...YxZ, then the class will store it internally as an ordinary 2D sparse array of dimensions (M*N*P*...*Y)xZ. This leads to certain memory strains when using large numbers of dimensions. I find the class useful mainly for moderate dimensional things like edge detection in 3D imaging, where you often want to hold a sparse 3D edge map. )

libsvm-3.16工具箱，支持多分类向量机，能载入matlab并运行，已经通过验证的程序，很好(libsvm-3.16 toolbox, support multi-classification vector machine, can be loaded the matlab and running, has been validated procedures, very good)

车牌号码识别检测程序，能快速有效检测车辆号码(License plate number recognition testing procedures, fast and effective detection of vehicle numbers)

speckle noise in image processing