npc1

Simple dynamic ode model describes a nutirition food web in a lake.

说明：  matlab中最优化函数fmincon 求解非线性规划问题(fmincon used to solve nonlinear programming problem)

vc和mat混合编程，利用matlab的com组件来实现mat和vc的混合编程(vc and mat mixed programming, the use of matlab' s com vc components to achieve mat and Hybrid Programming)

根据输入值的大小和输出值得大小，绘制相应的曲线，功率谱图(The size of the input and output values ​ ​ worthy size, draw the curve of the power spectrum)

雷达数据处理对于观测的目标位置数据（称为点迹）进行滤波处理，自动形成航迹，并且可以对下一个目标的位置进行估计预测。利用最小二乘算法（LMS）进行滤波处理，得到了比较稳定的航迹估计。 最小二乘方法是参数估计中一种较为常见的参数估计方法。对于雷达观测数据，在我们假定已经精确知道航迹的运动学模型之后，通过建立观测方程，即可以对于模型的参数，如初速度，初始位置和加速度等进行最小均方误差估计。采用序贯最小二乘的方法可以对观测数据进行实时处理，已对待估计参数进行实时更新，然后利用观测模型可以对 数据进行滤波计算。(Radar data processing for target position data observed (known as trace points) filter processing, automatic track, and can be estimated to predict the position of the next target. Using the least squares algorithm (LMS) filtering process, have been relatively stable track estimates. Least-squares parameter estimation method is a more common parameter estimation method. For radar data, we assume that the kinematic model has been accurately track after, through the establishment of observation equations, which can be the parameters of the model, as ever speed, acceleration, etc. initial position and minimum mean square error estimation. Sequential least squares method using the observed data can be processed in real time, has been to treat the estimated parameters are updated in real time, and then use observational data can be filtered model calculations.)

MATLAB仿真机器人避障实验,其中机器人系统通过模型实现，障碍物由点构成(MATLAB simulation robot obstacle avoidance test)

用Matlab编写的求解高斯线型随机差分方程的离散扩展卡尔曼滤波程序。(Matlab prepared for the linear Gaussian stochastic differential equation expansion of discrete Kalman filter procedures.)

Algorithm for Generating Random Number with Multi-dimensional Discrete Distribution

数字识别，通过matlab对0-9几各简单的数字进行识别(Digital identification)

非常完美的OFDM程序，已经在MATLAB验证过，完全能用！！！(Perfect OFDM program has been validated in MATLAB, can be used completely! ! !)