pujianfa_1

使用MATLAB编写的一个凸轮的曲线运动规律(Using the MATLAB curve prepared by a law of motion cam)

Many testfunctions for global optimizers

Kalman滤波程序,在一维的数据上，观看滤波后估计值与预测值之间的差距。(Kalman filtering)

一份关于单个目标跟踪和多个目标跟踪的KALMAN滤波资料,详细的解释个多种算法 (A report on the single target tracking and multiple target tracking KALMAN filtering information, a detailed explanation of a multiple target )

计算静气动弹性的程序，主要功能包括计算q，和alpha 静稳定性以及表面诱导阻力(Static aeroelastic calculation procedures, the main features include computing q, and alpha-induced static stability and surface resistance)

matlab ods exercices

PTS MIMO STBC OFDM PAPR BER

D-S证据理论数据融合，微分方程组数值解方法，关于超声波倒车雷达测距的。( D-S evidence theory data fusion, Numerical solution of differential equations method, About ultrasonic parking radar ranging.)

说明：  使用matlab来提取nastran生成的pch文件中的刚度矩阵与质量矩阵(Extraction of stiffness matrix and mass matrix)

The optimization behavior of the self-adaptation (SA) evolution strategy (ES) with intermediate multirecombination (the (=I )-SA-ES) using isotropic mutations is investigated on the general elliptic objective function. An asymptotically exact quadratic progress rate formula is derived. This is used to model the dynamical ES system by a set of difference equations. The solutions of this system are used to analytically calculate the optimal learning parameter  . The theoretical results are compared and validated by comparison with real (=I )-SA-ES runs on typical elliptic test model cases. The theoretical results clearly indicate that using a model-independent learning parameter  leads to suboptimal performance of the (=I )-SA-ES on objective functions with changing local condition numbers as often encountered in practical problems with complex fitness landscapes.