Gauss-Legendre

mimo的相关书籍，主要对mimo技术所涉及的一些基础理论进行概括和总结。方便读者阅读。(mimo' s books, mainly on mimo technology involved some of the basic theory Summary and Conclusion. The convenience of readers.)

用于蒙特卡洛monte calro仿真方法的模拟(For the Monte Carlo simulation method simulation monte calro)

单位冲击响应 单位脉冲响应 单位斜坡响应及稳态误差的计算(Unit impulse response unit impulse response unit ramp response the calculation of the steady-state error)

function dg=gplus(x,y,z,x1,x2,y1,y2,z1,z2,p) G=6.67e-11 t222=myfun(x,y,z,x2,y2,z2) t122=myfun(x,y,z,x1,y2,z2) t212=myfun(x,y,z,x2,y1,z2) t221=myfun(x,y,z,x2,y2,z1) t211=myfun(x,y,z,x2,y1,z1) t121=myfun(x,y,z,x1,y2,z1) t112=myfun(x,y,z,x1,y1,z2) t111=myfun(x,y,z,x1,y1,z1) dg=-G*p*1e6*(t222-t122-t212-t221+t211+t121+t112-t111) 转化为g.u. function y=myfun(x,y,z,xi,eta,zeta) R=sqrt((xi-x).^2+(eta-y).^2+(zeta-z).^2) temp1=(xi-x).*log((eta-y)+R) temp2=(eta-y).*log((xi-x)+R) temp3=(zeta-z).*atan((zeta-z).*R/((xi-x).*(eta-y))) 书中所给公式 temp3=-(zeta-z).*atan((xi-x).*(eta-y)./(zeta-z)./R) 上式变换 y=temp1+temp2+temp3 (function dg = gplus (x, y, z, x1, x2, y1, y2, z1, z2, p) G = 6.67e-11 t222 = myfun (x, y, z, x2, y2, z2) t122 = myfun (x, y, z, x1, y2, z2) t212 = myfun (x, y, z, x2, y1, z2) t221 = myfun (x, y, z, x2, y2, z1) t211 = myfun (x, y, z, x2, y1, z1) t121 = myfun (x, y, z, x1, y2, z1) t112 = myfun (x, y, z, x1, y1, z2) t111 = myfun (x, y, z, x1, y1, z1) dg =-G* p* 1e6* (t222-t122-t212-t221+t211+t121+t112-t111) converted the Gu function y = myfun, (x y, z, xi, eta, zeta) R = sqrt ((xi-x). ^ 2+ (eta-y). ^ 2+ (zeta-z). ^ 2) temp1 = (xi-x).* log ((eta-y)+R) temp2 = (eta-y).* log ((xi-x)+R) temp3 = (zeta-z).* atan ((zeta-z).* R/( (xi-x).* (eta-y))) book given formula Temp3 =- (Zeta-z)* ATAN ((Xi-X)* (ETA-y)/(Zeta- Z)/R) the above formula transform Y = temp1+temp2+temp3 )

基于单神经元网络方法的等温模锻过程PID控制算法。(PID control algorithm based on isothermal forging process of a single neural network approach.)

Title: "Modeling Lung Cancer Diagnosis Using Bayesian Network Inference" This demo illustrates a simple Bayesian Network example for exact probabilistic inference using Pearl s message-passing algorithm. Introduction: Bayesian networks (or belief networks) are probabilistic graphical models representing a set of variables and their dependencies. The graphical nature of Bayesian networks and the ability of describing uncertainty of complex relationships in a compact manner provide a method for modelling almost any type of data.

说明：  遗传算法优化支持向量机算法,从而更好的提高识别率和预测率(GA-SVM implemented by MATLAB)

有关矩阵特征值的ＭＡＴＬＡＢ源程序，解决数学建模问题中矩阵特征值的求解(Eigenvalues of the matrix MATLAB source code to solve the problem in mathematical modeling for solving matrix eigenvalue)

This m-file is written in MATLAB. It instroduces a simple method to create a PID controller

多维数据的多维偏最小二乘法的数据拟合方法，能有效地建立各类共线性数据的模型。()