MATLAB5[1].X-induction-and--enhance

1、系统仅处于训练状态。训练序列为2cos(2π×f1×n/fs)， 干扰为sin(2π×f2×n/fs)；序列长度为100个样点。 其中n=0…99，f1=1kHz，f2=2kHz，fs=8kHz。 2、采用LMS算法，均衡器输入为训练序列加上干扰，均衡器阶数为21阶，步长因子β=0.01。 3、在Matlab中编写浮点LMS算法，绘制均衡器的输出波形，并与训练序列进行对比。 (1, the system only in the training status. The training sequence is 2cos (2πf1n/fs), interference sin (2πf2n/fs) sequence length of 100 samples. Where n = 0 ... 99, f1 = 1kHz, f2 = 2kHz, fs = 8kHz. 2, using LMS algorithm, the input to the equalizer training sequence plus interference equalizer order is 21 bands, step factor β = 0.01. 3, the preparation of the floating-point LMS algorithm in Matlab, draw the output waveform equalizer, and with the training sequence comparison.)

此实验是关于详细的采样定理，有设计与实现及MATLAB程序代码。(It is useful.)

matlab在信号处理中傅里叶变换的应用，大家可以参考一下(matlab Fourier transform in signal processing applications, we can refer to)

很好的关于风机的资料,希望对大家有所帮组。(the material of Wind turbines )

含有噪声信号的功率谱 该算法是一种经典算法，可移植。(Contains a noise signal power spectrum of the algorithm is a classical algorithm, can be transplanted.)

Principle Component Analysis

说明：  信号带宽30MHz,输入采样率96MHz,输出采样率32MHz，抽取倍数为3的数字下变频DDC的Matlab仿真程序(Signal bandwidth of 30MHz, the input sample rate of 96MHz, the output sampling rate of 32MHz, extracted 3 times DDC DDC in the Matlab simulation program)

一级倒立摆系统采用lqr方法控制，使其达到平衡状态(Inverted pendulum system using lqr to control, to reach the equilibrium state)

plot the pskrates .Useful for the beginners.

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.)