Practical_Quiz_3

说明：  function [ret,x0,str,ts,xts]=s4(t,x,u,flag) s4 is the M-file description of the SIMULINK system named s4. The block-diagram can be displayed by typing: s4. SYS=s4(T,X,U,FLAG) returns depending on FLAG certain system values given time point, T, current state vector, X, and input vector, U. FLAG is used to indicate the type of output to be returned in SYS.

classification possiblity fuzzy map

normal fit遗传算法，MATLBA编程。。。。。。。。。。。。(normal fit algorithm, MATLBA programming. . . . . . . . . . . .)

在matlab下的，牛顿拉夫逊法潮流算法的程序(In matlab, the Newton Raphson power flow algorithm program)

永磁电机矢量控制matlab仿真，适合电驱动研究者(Matlab simulation of permanent magnet motor vector control for electrically driven researcher)

包括主成分分析、因子分析、贝叶斯分析，有详细的注释，BP神经网络用于函数拟合与模式识别。( Including principal component analysis, factor analysis, Bayesian analysis, There are detailed notes, BP neural network function fitting and pattern recognition.)

说明：  使用matlab代码来计算复杂网络的拓扑特征——聚类系数(Calculating complex network topological characteristics clustering coefficient)

it is a program to identify the persons using his own iris

共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数(Conjugate Gradient method (Conjugate Gradient) is between the steepest descent method between Newton method and a method, it only USES a derivative information, but overcome the steepest descent method slow convergence of weakness, but also avoid the Newton law needs to storage and computing Hesse inverse matrix and shortcomings, Conjugate Gradient method is not only solve linear equations with most of the large method, and also one of the most effective solution large nonlinear optimization of one of the algorithm. In all kinds of optimization algorithm, the conjugate gradient method is very important. Its advantage is the storage capacity needed, it has small step convergence, high stability, and doesn t require any exotic parameters numerical experiment, this is the modern scientific computing of the answer above problem sets)

扇形传感器区域覆盖优化的虚拟力导向粒子群算法(Fan-shaped area coverage sensor-driven power optimization of the virtual particle swarm optimization)