intrapersonal

介绍了SVM的基础知识，包括：SVM入门（一）SVM简介；SVM入门（二）线性分类器part1；SVM入门（三）线性分类器part2；SVM入门（四）线性分类器的求解—问题的描述part1；SVM入门（五）线性分类器的求解—问题的描述part2等。(Describes the basics of SVM, including: SVM Getting Started (1) SVM Introduction SVM Getting Started (2) linear classifier part1 SVM Getting Started (3) linear classifier part2 SVM entry (d) the solution of a linear classifier- description of the problem part1 SVM Getting Started (5) the solution of the linear classifier- description of the problem part2 and so on.)

说明：  对梁进行动力学分析，计算梁在集中力的作用下的变形(Dynamic analysis of the beam, for beam under the action of concentrated forces in the deformation)

求解指派问题，再输入指令后，根据信息自动快速的求出最佳分配方案(Solve the assignment problem, and then enter the command, according to the information automatically and quickly find the optimal allocation scheme)

Queues simulation in SimEvents

低复杂度信道估计仿真程序，用来降低信道估计的复杂度(Low complexity channel estimation simulation program, used to reduce the complexity of the channel estimation)

复杂网络上演化博弈的小世界网络的算法等等希望有用(Evolutionary Game on Complex Networks of small world network of algorithms, and so wish to be useful)

常见的一维搜索，机械设计优化算法，牛顿，黄金分割，抛物线(Common one-dimensional search, the mechanical design optimization algorithm, Newton, Golden Section, the parabolic)

盲信号分离(BSS)指在源信号混合和传输信道未知的情况下，只利用接收天线的输出观测混合信号抽取源信号的方法。本文简要阐述了常用的瞬时混合盲信号分离的LMS与RLS自适应算法，对RLS自适应算法重点研究分析了基于普通梯度与自然梯度的两种算法，并通过仿真实验来分析比较几种方法的性能。(Blind signal separation (BSS) refers to the source signal and transmission channel mixing unknown circumstances, the only use of the receiving antenna output source signal observed mixed-signal extraction method. This paper describes commonly used blind source separation of instantaneous mixture of LMS and RLS adaptive algorithms, the RLS adaptive algorithm focuses on the analysis of general gradient and the natural gradient based on two algorithms, and through simulation experiments to compare several methods of analysis performance.)

物体围绕一圆周运动，角加速度是白噪声，观测噪声是高斯白噪声。使用卡尔曼滤波器实现物体的跟踪(Follow the trace)

multiuser detection of Decorrelating and MMSE