fft-sample

一维Logistic映射的OGY控制（稳定至不动点）(One-dimensional Logistic map of OGY control (stability to the fixed point))

matlab 同步电机 abc 模型 适合大家学习(matlab synchronous motor abc model for everyone to learn)

说明：  利用EM算法对状态空间模型中的参数进行辨识，效果良好(The EM algorithm is used to identify the parameters in the state space model, and the result is good.)

MALAB GUI 学习资料， 总共10章 对初学者很有帮助。。。。。(MALAB GUI )

对连续的单一频率离散序列 ，即k=8。 按采样频率 采样，截取长度N分别选N =20和N =16，观察其DFT结果的幅度谱。 (Discrete to continuous sequence of single frequency, ie k = 8. According to the sampling frequency sampling, truncation length N = 20 N, respectively, and selected N = 16, observed the results of its DFT magnitude spectrum.)

《MATLAB神经网络30个案例分析》中的第11个例子，案例11 连续Hopfield神经网络的优化-旅行商问题优化计算。希望对大家有一定的帮助！(The MATLAB neural network analysis of 30 cases of 11 examples, case 11 continuous Hopfield neural network optimization- traveling salesman problem optimization calculation. Hope to have certain help to everybody! )

说明：  用于功率预测的CNN代码，以及一系列相关函数文件(CNN Code for Power Prediction)

routine to take the Fourier transform of a signal and plot both the time and frequency domain representations. Typical use: >> n = 100 number of data points >> dt = 0.01 spacing between samples >> t = 0:dt:(n-1)*dt time vector >> y = sin(2*pi*10*t) 10 Hz sin wave >> fft_plot(y,dt) plot of signal in time and frequency domain

vc调用matlab中自定义的.m函数，对于混编简单的例子(Vc call custom in matlab. M function，For simple example mixed)

1 chaos_draw_GCM.m 基于最大标准差绘制的大域耦合相图（GCM）；2 chaos_GCM.m 特定参数下的大域耦合状态变量的值的时域及频域图； 3 chaos_simple.m 混沌对初始条件敏感的实验； 4 ICAS.m ICAS系统的评价数据处理； 5 julia.m 朱莉娅集的绘制； 6 logistic.m 逻辑斯蒂克集的稳定点关于参数u的分布(1 chaos_draw_GCM.m drawn based on the maximum standard deviation of the big field coupling phase diagram (GCM) big field coupling state variable 2 chaos_GCM.m special parameter values ​ ​ in the time domain and frequency domain 3 chaos_simple.m Chaos sensitive to initial conditions experimental evaluation data processing 4 ICAS.m ICAS system draw 5 julia.m Julia sets stable point 6 logistic.m logic Si Dike set of parameter distributions u)