matlab-ruanjisheji

matlab求解reynolds方程程序(matlab program for solving Reynolds equation)

利用矩阵自定义乘法，实现网络连通性的邻接矩阵(Matrix multiplication using custom, network connectivity adjacency matrix)

对一随机信号进行AR模型的谐波估计,包括不同采样点数(Harmonics of a random signal AR model estimates, including different sampling points)

MFDFA 方法的多重分形 MATLAB程序，可用于股市分析，材料学、医学、岩石物理等领域。(The multifractal MATLAB program of the MFDFA method can be used in the field of stock market analysis, materials science, medicine, rock physics and so on.)

在一无人机机身安装共形阵，将机身视为一的规则圆柱体，对此共形阵进行波束扫描 (At one UAV airframe installation conformal array, will be regarded as one of the rules of airframe cylinder, for which conformal array for beam scanning)

时延角度联合估计JADE 算法，估计多径时延及到达角离开角(JADE algorithm for joint estimation of delay angle)

《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.)

Quad Tree image processing

支持向量机的回归拟合——混凝土抗压强度预测,介绍SVM回归拟合的基本思想和原理，并以实例的形式阐述其在混凝土抗压强度预测中的应用。(Support vector machine regression- concrete compressive strength prediction, SVM regression introduce the basic ideas and principles, and examples in the form set forth in the concrete compressive strength prediction.)

这个函数用来通过蒙特卡罗求积分， f 要求的函数； fail1 积分下限 fail2 积分上限 a，b 积分区域内x的积分上下限 c,d 积分区域内y的最大和最小值,c,d不是必须的参数，但是有之计算速度更快 n 求解迭代次数( This function is used to find the integral Monte Carlo, function f requirements fail1 integral limit fail2 integral upper limit a, regional integration within b limit c on x points, d- the maximum and minimum value of the integration region y, c, d is not required parameters, but the calculation speed is faster n Iteration number)