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说明：  基于MATLAB的电路分析 以MATLAB软件为工具，通过几个具体实例介绍了电路分析的方法(MATLAB-based circuit analysis tools in MATLAB software, a few specific examples introduced by the circuit analysis method)

一本关于非线性时间序列分析的书籍，本来论坛有人上传过，但不能下载了。(One on nonlinear time series analysis of books，Originally it was uploaded, but can not download)

这是一个关于BPSK系统的仿真程序，并画出在瑞利信道和高斯信道下的误码率图形(It is a BPSK system simulation program, and draw in the Rayleigh and Gaussian channels channel bit error rate under the graphics)

动态演示不同参数下的lorenz混沌吸引子(Dynamic playing the different parameter of the lorenz chaos attractor)

MATLAB 曲线拟合小工具，适合初学者，高手直接无视之( MATLAB M file for curve fitting )

Listerature survey on Content based image reteival techs

(不错的一篇文章，已被EI收录)模型预测控制（model predictive control，MPC）路径规划算法适用于三维动态环境下的无人机（un-manned aerial vehicle，UAV）路径规划；动态贝叶斯网络（dynamic Bayesian network，DBN）能够有效推理战场态势，对无人机进行威胁评估。针对威胁尾随无人机时的路径规划问题，构建 DBN 威胁评估模型，将 UAV 在战场环境中的威胁态势用威胁等级概率表示，与 MPC 路径规划算法相结合，得到基于 DBN 威胁评估的 MPC UAV 路径规划算法。通过多组仿真分析表明，在三维动态环境下，特别是威胁尾随无人机时，基于 DBN 威胁评估的 MPC无人机路径规划算法可以得到有效的无人机路径。(The model predictive control (MPC)path planning algorithm can solve the problem of dynamic unmanned aerial vehicle (UAV)path planning.Dynamic Bayesian network (DBN)is an effective tool for rea-soning and threat assessment.Considering the problem of path planning when the dynamic threat tags the UAV,the MPC path planning algorithm combined with DBN threat assessment is presented,which used the threat lever probability to describe the threat situation.A group of simulations demonstrate the efficiency of MPC three-dimensional dynamic path planning algorithm for UAV based on DBN threat assessment especially when the dynamic threat tags the UAV.)

用matlabf编程求解通常的图着色问题,经常使用这个原理的.(Matlabf programming is usually used to solve the graph coloring problem, frequent use of this principle.)

该程序是基于Kalman Filter 理论编写的基本的Matlab 滤波程序！(This code is relative with the basic Kalman Filter.)

A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional)