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这是一个相关系数分析的算法代码，用matlab编写的(This is a correlation coefficient analysis of the algorithm code, prepared by matlab)

求取系统的绝对幅度响应、相对的db值幅度响应、相位响应和群延时响应的函数(The absolute magnitude of the strike system response, a function of the relative a dB value amplitude response, phase response and group delay response)

It s about smart antenna. It can help you whatever it is.

浊音的对数幅度谱，短时过零率，平均能量，基音周期(Logarithmic amplitude spectrum voiced, short zero-crossing rate, average energy, pitch period)

地震波场模拟，二维声波模拟！未加边界条件，新手学习还是可以滴！(2D seismic wave forward modelling)

EMD端点效应的处理方法，（镜像延拓发）简单易学。(Processing method end effect of EMD, (mirror extension hair) is easy to learn.)

说明：  License from matlab 2017a

步进电机失步情况的一个仿真.这是一个比较完整的方针,但是仅仅考虑了在单个脉冲下的失步情况.(Stepper motor out-of-step situation in a simulation. This is a relatively comprehensive approach, but considered only in a single pulse under the out-of-step situation.)

jpeg在matlab下的仿真实现，整个程序运行还行，共享给大家(jpeg of the Matlab Simulation, and the whole process has run OK, for everyone to share)

说明：  遗传算法（Genetic Algorithm，GA）最早是由美国的 John holland于20世纪70年代提出,该算法是根据大自然中生物体进化规律而设计提出的。是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法。该算法通过数学的方式,利用计算机仿真运算,将问题的求解过程转换成类似生物进化中的染色体基因的交叉、变异等过程。在求解较为复杂的组合优化问题时,相对一些常规的优化算法,通常能够较快地获得较好的优化结果。遗传算法已被人们广泛地应用于组合优化、机器学习、信号处理、自适应控制和人工生命等领域。(Genetic algorithm (GA) was first proposed by John Holland of the United States in the 1970s. It is designed and proposed according to the evolution law of organisms in nature. It is a computational model of biological evolution process simulating natural selection and genetic mechanism of Darwin's biological evolution theory. It is a method to search the optimal solution by simulating natural evolution process. The algorithm transforms the solving process of the problem into a process similar to the crossover and mutation of chromosome genes in biological evolution by means of mathematical method and computer simulation. When solving complex combinatorial optimization problems, compared with some conventional optimization algorithms, it usually can get better optimization results quickly. Genetic algorithm has been widely used in combinatorial optimization, machine learning, signal processing, adaptive control and artificial life.)