ycsf

说明：  进行基于最大最小方法和K均值结合的分类方法 并画出具体的分类情况，对二维数组具有鲁棒性。(Based on max-min method and combination of K-means classification method and draw a specific classification of two-dimensional array with robustness.)

code of simulink for gpc controller

This code is calculating K-means in matlab programming.

属性分类的粒子群算法Particle swarm optimization (pso) algorithm to the classification of the attribute(Particle swarm optimization (pso) algorithm to the classification of the attribute)

利用田纳西伊斯曼过程数据 分析得到误差的特征统计量( 允许输入拉丁字符的拼音 中文(简体) 英语 日语 Tennessee Eastman process of data analysis to get the characteristics of the error statistics 允许输入拉丁字符的拼音 中文(简体) 英语 日语 Tennessee Eastman process of data analysis to get the characteristics of the error statistics)

说明：  matlab 程序 是自己写的，太简单科别笑我 希望有人支持(matlab program is written in their own, too simple divisions laugh, I hope there is support for)

Given an output function f(x) and a cost function J(x) (and gradients), optimal inverse functions are found which produce the input x* with the lowest cost based on the current desired output. Output and cost functions produce a scalar. The method is demonstrated on 2D problems (for convenient visualization). With 2D plots removed (all but Figure 41), the code can run higher dimension systems also.

基于HNLF的全光逻辑与门主程序，光纤中的四波混频过程的matlab数值模拟仿真代码。(HNLF-based all-optical logic gates main program, fiber four-wave mixing process matlab numerical simulation codes.)

关于随机分布控制理论中最基础的思想，主要是基于平方根B样条建模。(About randomly distributed control theory the most basic idea is mainly based on the square root of the B-spline modeling.)

《系统辨识与MATLAB仿真.pdf》 侯媛彬 汪梅 王立琦 本书共分8章，第1、2章为辨识的基本概念、理论基础和古典辨识方法；第3至6章为现代辨识内容，其中第3章是最小二乘参数辨识，第4章是梯度校正参数辨识，第5章是极大似然法的参数辨识方法，第6章是自适应参数辨识；第7、8章为复杂的非线性系统的智能辨识和混沌辨识，其中第7章是非线性系统的神经网络辨识；第8章是Volterra辨识方法、复杂系统的混沌现象及其辨识。从第2至7章，各章均包含开发的相应程序及其程序剖析。 (" System Identification and MATLAB simulation. Pdf" Hou Yuanbin Wang Mei Wang Liqi book is divided into eight chapters, Chapters 1 and 2 for the identification of the basic concepts, theoretical foundation and classical identification methods first 3-6 chapters of modern identification content, Chapter 3 is the least squares parameter identification, Chapter 4 is the gradient correction parameter identification, Chapter 5 is the maximum likelihood parameter identification method, Chapter 6 is an adaptive parameter identification Chapters 7 and 8 for complex Intelligent identification of nonlinear systems and chaos identification, which is in Chapter 7 of the neural network nonlinear system identification Chapter 8 is Volterra identification method, complex systems and chaos identification. From the first 2-7 chapters, each chapter contains procedures for the development of appropriate programs and their analysis.)