Rungkuta4

三维Hashin准则，分层准则的渐进损伤失效模拟子程序(progress damage evaluation)

全国研究生数学建模竞赛优秀获奖论文(National Graduate School Mathematical Contest in Modeling outstanding thesis award)

用R做贝叶斯估计，估计随机微分方程中的参数(Bayesian estimation made by R, the parameter estimates stochastic differential equation)

这是一个关于数字预失真功能仿真的matlab程序，效果不错，希望对大家有所帮助。(This is a matlab program about function estimation of digital estimation,and it has a good performance,really hope it can help you.)

说明：  瑞利分布的随机变量，标准高斯分布的随机变量，莱斯分布的随机变量(Random Variables of Rayleigh Distribution, Standard Gauss Distribution and Rice Distribution)

可进行最小二乘法线性拟合输出，包含源代码及生成后的程序(Least-squares linear fit can be output)

说明：  本程序是matlab编写的imm（交互式多模型算法）目标跟踪程序。这种方法的特点是在各模型之间“转换”，自动调节滤波带宽，和适合机动目标的跟踪。(This program is matlab written imm (interacting multiple model algorithm) target tracking program. This method is characterized among the various models &quot conversion&quot , automatically adjust filter bandwidth, and suitable for maneuvering target tracking.)

说明：  可用于层次分析法比较评价，主函数输入初始矩阵，调用层次分析函数(It can be used for comparative evaluation of analytic hierarchy process. The main function input the initial matrix and call the analytic hierarchy process function.)

自制的计算器，可实现加减运算，清除，后退等功能(Homemade calculator, can realize the addition and subtraction, clear, back, etc)

《数值分析方法库_第三版 》本书选材内容丰富，包括了当代科学计算过程中涉及的大量内容：求特殊函数值、随机数、排序、最优化、快速傅里叶变换、谱分析、小波变换、统计描述和数据建模、偏微分议程数值解、若乾编码算法和任意精度计算等。本书科学性和实用性统一，不仅对每种算法进行了数学分析和比较，而且根据作者经验对算法给出了评论和建议，并在此基础上提供了用C++语言编写的实用程序。 (《Numerical Recipes 3rd Edition: The Art of Scientific Computing》, Do you want easy access to the latest methods in scientific computing? This greatly expanded third edition of Numerical Recipes has it, with wider coverage than ever before, many new, expanded and updated sections, and two completely new chapters. The executable C++ code, now printed in color for easy reading, adopts an object-oriented style particularly suited to scientific applications. Co-authored by four leading scientists from academia and industry, Numerical Recipes starts with basic mathematics and computer science and proceeds to complete, working routines. The whole book is presented in the informal, easy-to-read style that made earlier editions so popular. Highlights of the new material include: a new chapter on classification and inference, Gaussian mixture models, HMMs, hierarchical clustering, and SVMs a new chapter on computational geometry, covering KD trees, quad- and octrees, Delaunay triangulation, and)