程序

卡尔曼滤波 现代滤波器的实现,matlab源码(Kalman filter to achieve a modern filter, matlab source)

燃料电池电堆的温度控制，采用预测控制算法实现(PEMFC Stack Temperature Control Study)

本演示展示FireMonkey 3D组件的某些功能。运行演示，了解各种影响：揃ACK插值，揂utoreverse，揈LASTIC插值或3D文字旋转或形状动画下列路径（pathanimation如SVG或XAML） (Some features of this demo shows FireMonkey 3D components. Run the demo, to understand the various effects: CIRCUIT DIAGRAM ACK interpolation, You utoreverse, Hong LASTIC interpolation or rotating 3D text animation following the path or shape (pathanimation such as SVG or XAML) )

MATLAB通信仿真及应用实例详解 不用多说，很经典的书(simulink)

Simple show in 3D model of Single-slit diffraction.

An Adapted Firefly Algorithm for Product Development Project Scheduling with Fuzzy Activity Duration

 结合理论课讲解基于Matlab建立不同信道模型：高斯信道、Rayleigh信道，Recian信道及多径衰落信道模型；  给出QAM/GMSK调制方式下在上述信道模型下的误码率性能分析，分析比较两种调制方式的优缺点；  分析信道参数、信噪比对误码率性能的影响； (Based on Matlab, we construct different channel models: Gaussian channel, Rayleigh channel, Recian channel and multipath fading channel model. The error rate performance analysis of QAM/GMSK modulation mode under the above channel model is given, and the advantages and disadvantages of the two modulation modes are analyzed and compared.  Analysis of channel parameters, signal to noise ratio on the impact of bit error rate performance )

本文使用Matlab及Simulink软件源码包对cdma20000的前向业务信道进行时间流与数据流仿真，并对仿真结果进行了对比分析。 (This article uses the Matlab and Simulink software source code packages on cdma20000 the first time flow and data flow simulation, and simulation results were compared to the traffic channel.)

客观现实的另一类力学模型是弹性体(也称连续系统或分布参数系统)，它的物理参数是分布型的，具有无限多个自由度，且运动规律由偏微分方程来确定(Another kind of objective reality mechanical model is elastomer (also called the continuous system or distributed parameter systems), its physical parameter is the distribution model, with an infinite multiple degrees of freedom, and movement rules by partial differential equations to determine )

用matlab实现RLS滤波，最小二乘估计算法是以最小误差平方和为优化目标的。(RLS filter using matlab, least squares estimation algorithm is the smallest sum of squared errors for the optimization objectives.)