KonghuPanda

Jon Bentley的《编程珠玑》 本书针对程序设计人员探讨了一系列的实际问题，这些问题是对现实中常见问题的归纳总结。作者虽然没有给出解决这些问题的具体代码，但始终非常富有洞察力和创造力地围绕着这些折磨程序员的实际问题展开讨论，从而引导读者理解问题并学会解决问题的技能，这些都是程序员实际编程生涯中的基本技能。为此，本书给出了一些精心设计的有趣而且颇具指导意义的程序，这些程序能够为那些复杂的编程问题提供清晰而且完备的解决思路，书中还充满了对实用程序设计技巧及基本设计原则的清晰而睿智的描述。 ()

分数阶混沌系统的Lyapunov指数,有利用分数及的计算(fractional chaotic newsysytems Lyapunov)

this program simulate tarcking algorithm based on wavelet.

DIGITAL MULTI-SIGNATURE SCHEME BASED ON ELLIPTIC CURVE

Principle Component Analysis

说明：  支持向量机的matlab代码。其中包括支持向量机的训练代码。(Support vector machine matlab code. Including the training of support vector machine code.)

levy法编制辨识程序，基本精确辨识了系统各个参数，因为误差准则的原因，系数略有差别。(Levy method prepare identification procedure, basic accurate identification of each parameter of the system, because the cause of the error criterion, a slightly different coefficient.)

基于MATLAB的电力系统仿真运行，教程。(Power system based on MATLAB simulation run)

说明：  CMMB系统符号同步算法设计，基于同步训练序列的同步算法(CMMB system symbol synchronization algorithm design, based on the synchronization training sequence synchronization algorithm)

高速铁路路基沉降是一个复杂的系统过程,涉及不同时期、不同工况,选用单一的预测模型不足以反映路基整体的变形规律.Logistic模型及Verhulst模型的曲线均为S形,可以反映全过程的地基沉降与时间的关系.针对路基填筑期和降水期两个时段利用MATLAB软件编制程序计算模型参数,得到Logistic模型和灰色Verhulst模型,进而求得两者的最优加权组合模型.对比结果表明:在路基填筑期由两者的最优加权组合模型预测得到的结果与实测结果较吻合,拟合精度高于单一模型 而在降水期Verhulst模型的预测值更接近实测值,平均相对误差约为1.75％,拟合精度较高.(High-speed railway subgrade settlement process is a complex system involving different times and in different conditions, the choice of a single forecasting model does not adequately reflect the overall deformation of roadbed. Curve Logistic model and Verhulst models are S-shaped, the whole process can be reflected foundation settlement versus time. against roadbed construction period of two hours and precipitation using MATLAB software programming model parameters, the Logistic model and gray Verhulst model, and then get the optimal model of a weighted combination of the two. comparative results indicate : results and experimental results in the roadbed construction period by the weighted combination of the best of both models predicted than the anastomosis fitting accuracy than a single model precipitation predicted value of Verhulst model closer to the actual value of the average relative error approximately 1.75 fitting accuracy.)