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With PRP conjugate gradient method for solving unconstrained problem

使用MATLAB程序求一阶倒立摆系统的状态空间以及其极点配置的小程序，对于设计计算一阶倒立摆系统的控制算法及其仿真有较好的辅助作用。(Using the MATLAB program seeking an inverted pendulum system, the state space and its pole configuration applet for the design and calculation of an inverted pendulum control algorithm and its simulation system has good supporting role.)

解决图论中最短路问题的最好方法--“Dijstra算法,”通过解析实例模型,对模型算法进行描述、拓展,并给出了求最短路以及求最短路长的MATLAB程序,此程序具有通用性。(The best way to solve the shortest path problem in graph theory is- "Dijstra algorithm," by parsing instance model, the model describes the algorithm, expand, and gives the shortest and seek seek the shortest length of the MATLAB program, this program has versatility.)

一些有用的无线通信物理层的matlab代码，每个文件夹内都有代码使用说明。(some useful MATLAB codes about PHY layer of wireless communication.)

专门用于石油工程中饱和度-单轴抗压强度曲线逼近作图，效果很好(Dedicated to petroleum engineering saturation- uniaxial compressive strength curve approximation mapping, good results)

雷达系统分析主要包括脉冲压缩、相干积累、非相干积累、CFAR、脉冲测角(Radar systems analysis including pulse compression, coherent accumulation, non-coherent accumulation, CFAR, pulse goniometer)

基于模糊rbf网络的逼近算法及仿真实例源代码，包括学习算法等(rbf)

用LMS算法从白噪声中提出单频正弦信号模型(LMS algorithm with white noise from the proposed single-frequency sinusoidal signal model)

matlab工作空间常用命令，比较实用的文档，可以查阅需要使用的命令，免去了记忆之苦，因为命令实在是太多了，没必要都记住吧。(Matlab work space commands, a practical document, the need to use the inspection order get rid of the bitter memory, because there are really too many orders, there is no need to remember it all.)

MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。(MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of view, the signal processing can be decomposed observation space the signal subspace and the noise subspace, it is clear that the two spaces are orthogonal. Signal Subspace data received by the array covariance matrix and eigenvectors corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector components.)