space_error_model

this is a mat lab simulation file including the m file.

Particle Swarm Optimization Algorithm Trial

nemerica l model for the test result at sandia

This program alows you to add noise to a source and the remove the noise by filtering.

许多系统包含了流量问题。例如交通系统有车流量，金融系统有现金流，控制系统有信息流等。许多流问题主要是确定这类系统网络所能承受的最大流量以及如何达到这个最大流量。MATLAB环境下解决最大流问题(Many systems contain a flow problem. Traffic such as traffic system, the financial system has cash flow, information flow control system. Many flow problem is to determine the maximum flow of such systems and how the network can bear to achieve this maximum flow. Maximum flow problem solving MATLAB environment)

Lattice Boltzmann Matlab Scripts 2-D Model of Porous Medium Flows

第四版prokias数字通信配套代码之第七章(source code(matlab )for prokias Digital Communication (4th edition))

流形学习，局部线性嵌入式算法(LLE)，一种智能的算法去推测捕捉高维空间中所包含的低维特征。与适合于局部维数约减的聚类算法不同，LLE算法在单一的低维的全域坐标系统中表征采样空间，然而它并没有优化最小局域。通过对线性重构的局域对称的研究应用，LLE能够描述非线性流形的全局结构，例如那些人脸的数据集或者文本文档集(Manifold learning, embedded local linear algorithm (LLE), an intelligent algorithm to predict capture high-dimensional space contained in the low-dimensional features. And for local dimensionality reduction of the different clustering algorithms, LLE algorithm in a single low-dimensional characterization of the global coordinate system, the sampling space, but it does not optimize the local minimum. Reconstructed by the local symmetry of the linear study of the application, LLE can describe the global structure of nonlinear manifolds, such as those who face data sets or text document collection)

this is a very good book for learners of Visual C++. As a beginner i found this was very useful for me. And i hope it will be very useful for those who are interested to learn Visual C++.

(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.)