extended_euclid.m

FDTD with ABC boundary done by me

可行方向法，用于无约束优化的作业所制作，请大家指教(Possible direction method, used for unconstrained optimization of assignments)

说明：  在前面1维，2维的基础上继续推出3维的FDTD程序，可以帮助解决复杂的3维电磁散射问题，适用范围更广。这个程序给出计算要求的参数即可运行。(in front of one-dimensional, two-dimensional basis continue to offer three-dimensional FDTD procedures, which can help to solve the complex three-dimensional electromagnetic scattering problems, which have wider applicability. This procedure gives the parameters computing requirements can run.)

hht_toolbox工具箱用 时频分析必备 大家共同学习(HHT toolbox for HHT time-frequency analysis )

Data cursor support for multiple axes in a fugure(Data cursor support for multiple axes in a f ugure)

In this paper, an attractive approach for teaching genetic algorithm (GA) is pre-sented. This approach is based primarily on using MATLAB in implementing the genetic operators: crossover, mutation and selection. A detailed illustrative example is presented to demonstrate that GA is capable of finding global or near-global optimum solutions of multi-modal functions. An application of GA in designing a robust controller for uncertain control systems is also given to show its potential in designing engineering intelligent systems

图像加噪，平滑使质量下降；图像的空域增强；中值滤波器；(Image denoising, smooth added that quality drop Image airspace strengthened Median filter)

edfa edfrnrt utnuh u h u yhu ub(unènn unu(nu(

SRM ,Dynamic model of a swithce reluctanc emotor.

The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order (The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order)