matlabcode

仿真对象如下： 其中， v( k )为服从N (0,1) 分布的白噪声。输入信号u ( k) 采用M 序列，幅度为 1。M 序列由 9 级移位寄存器产生，x(i)=x(i-4)⊕x(i-9)。 选择如下辨识模型： 加权阵取Λ = I。 衰减因子β = 0.98，数据长度 L = 402。 辨识结果与理论值比较，基本相同。辨识结果可信 (he simulation object is as follows: among them, v (k) to obey N (0, 1) distribution of white noise. The input signal u (k) using M sequence, amplitude is 1. M sequence by 9 level shift register generation, x (I) = x (I- 4) ⊕ x (I- 9). Choose the identification model: weighted array take Λ = I. Attenuation factor β = 0.98, the data length L = 402. Identify the theoretical calculation and comparison, basically the same. Identification results are reliable)

scm modulation technique

在系统辨识前，对输入输出数据进行平滑处理，列举了移动平均法、loess、sgolay等几种平滑方法进行分析比较(System identification, data input and output smoothing, moving average method, loess, sgolay cited several smoothing methods were analyzed and compared)

使用matlab调试C文件，mex相关操作。附带说明和源程序。(This code use matlab to debug the code which is writed by c.)

Model-Based_Predictive_Control_A practical approach，pdf图书和图书配套的matlab仿真代码(Model-Based_Predictive_Control_A practical approach, book and matlab code for this book)

随机介质的基本知识和模型建立的相关算法以及流程图(Correlation algorithm and flowchart showing the basic knowledge and random medium model established)

强化学习算法，通过Q学习算法解决房间路径规划问题，matlab(Reinforcement learning algorithm and Q learning algorithm to solve the problem of room path planning, matlab)

BEMD对图像进行分解，包络构造采用Delaunay三角剖分和三次插值，得到分解的结果。 输入：灰度bmp图像 输出：imf1 imf2 imf3 残差(BEMD image decomposition, the envelope structure using Delaunay triangulation and cubic interpolation, the result of decomposition. Input: greyscale bmp image output: imf1 imf2 imf3 residuals))

Linear regression code implementation in matlab

说明：  Harris corner detection function