water_fill

本代码为基于模糊RBF辨识的自适应PID控制，比较常规PID具有很明显优势控制效果。(The code for identification based on fuzzy adaptive PID control of RBF, compared with the obvious advantage of conventional PID control performance.)

给出了FIR滤波器设计的MATLAB语言程序，可以根据设计要求，灵活地调整参数，得到需要的滤波器参数。 (given FIR filter design in MATLAB procedures, according to design requirements, flexibility to adjust parameters and the need to filter parameters.)

lecture series of matlab

code FOR SIMULATE WSN PROBLEM OF SECURITY

A basic example of piano in Matlab

炮记录加道头，野外地震数据转换，dat转su或者segy(add head to seismic data ,data to su or segy)

用最速下降法求解多维函数的极值 function [x,minf]=minFD(f,x0,var,eps) 目标函数：f 初始点：x0 自变量向量：var 精度：eps 目标函数取最小值时的自变量值：x 目标函数的最小值：minf( With the steepest descent method for solving the multi-dimensional function of the extreme value function [x, minf] = minFD (f, x0, var, eps) target function: f Initial Point: x0 argument vector: var Accuracy: eps target function takes the minimum value of the independent variable values: x minimum target function: minf)

说明：  使用卡尔曼滤波器对信号进行滤波，不局限于工频干扰的频率，简单方便(Kalman filter using the signal filtering, frequency interference is not limited to frequency, simple)

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)

A PID controller coded in Matlab