JC

说明：  算法知识讲解，是一本不错的算法入门级教材的pdf版本(This is a good introduction to algorithm)

程序采用C语言，实现最小二乘辨识算法和递推最小二乘算法(Program using C language, least squares identification algorithm and recursive least squares algorithm)

这本书由 Clive G. Page(University of Leicester)先生撰写，旨在帮助正在学习fortran的人。这是本非常好的原版英文入门书。(This book written by Clive G. Page(University of Leicester) is aimed to help those who want to pursue his/her career in Fortran. It is a good start book.)

里面包含了随机矩阵理论里经典的仿真，如marcenko-pastur law,tracy-widom law等(It contains some classical simulationa in the random matrix theory，such as marcenko-pastur law,tracy-widom law)

盒维数计算和豪斯多夫维数的计算与应用研究程序(box counting)

三次样条插值函数源代码：对输入的数据采用三次样条插值处理,集成了样条,并返回积分值。(Cubic spline interpolation function source code: the input data using cubic spline interpolation processing, integrated spline, and returns integral values.)

CG共轭梯度法迭代解矩阵方程，Hhestens和Stiefel给出，(Conjugate gradient method CG iterative matrix equation solution, Hhestens and Stiefel given)

安德森/计算流体力学/fortran源代码(第九章esimple算法）(Anderson/Computational Fluid Dynamics/fortran source code (Chapter esimple algorithm))

使用Matlab编程，对教材74页例3.3.1进行编程仿真。其中，正电压A的值、噪声方差值、每个码元周期内的采样点数N自行设定（可设置为可调的变量）。噪声值可使用Matlab中产生高斯随机数的函数来进行仿真。贝叶斯检测判决式中，先验概率P(H1)=P(H0)，判断错误的代价因子设为1，判断正确的代价因子设为0. 按照上述参数的设定进行仿真，实现对仿真数据的贝叶斯检测判决；循环生成仿真数据，并进行判决结果的统计，即记录判决正确的次数，估计正确判决的概率。(The use of Matlab programming, teaching materials 74 pages of 3.3.1 programming simulation. Among them, the value of positive voltage A, the variance of noise, the number of sampling points in each symbol cycle (N) can be set by itself (adjustable variable). Noise values can be simulated using the function of generating Gauss random numbers in Matlab. In the Bias detection decision, the prior probability P (H1) =P (H0), the factor of error judgment is set to 1, and the correct cost factor is set to 0. According to the simulation parameters and realize the judgment of Bayesian detection simulation data; generating cycle simulation data, statistics and decision, which records the correct number of judgment, the estimated probability of correct decision.)

结合粒子群优化算法和和声搜索算法的优点，有效将群智能思想和随机全局搜索相融合，在几个函数优化问题中得到了验证 (Combining particle swarm optimization algorithm and harmony search algorithm advantage, effective will swarm intelligence thought and random global search phase fusion, in several function optimization problem has been proved )