matlab

Sodoku Solver in matlab.

三种角点提取方法的matlab程序，用于摄像机标定。(three methods of corner extract based on matlab。)

This is a very helpful matlab complement for the implementation of neural networks, fuzzy systems, neuro fuzzy systems for control and classification form scratch, I found it very helpful as a strudent and a a researcher

车间作业调度问题遗传算法Matlab源码,用于解决车间的调度问题(Job Shop Scheduling Problem Matlab genetic algorithm source code for the solution to the scheduling problem workshop)

codega in genetic algorythme

libsvm的最新函数包，包含了各种语言的接口，可方便的实现分类，回归等问题(libsvm package)

JITTERBUG 是一个用来仿真线性控制系统的Matlab工具箱。通过JITTERBUG，我们能得到延时、数据丢失对系统的性能影响。同时，这个工具箱还能计算控制系统中信号的谱密度。(JITTERBUG [Lincoln and Cervin, 2002] is a MATLAB-based toolbox that allows the computation of a quadratic performance criterion for a linear control system under various timing conditions. Using the toolbox, one can easily and quickly assert how sensitive a control system is to delay, jitter, lost samples, etc., without resorting to simulation. The tool is quite general and can also be used to investigate jitter-compensating controllers, aperiodic controllers, and multi-rate controllers. As an additional feature, it is also possible to compute the spectral density of the signals in the control system. The main contribution of the toolbox, which is built on well-known theory (LQG theory and jump linear systems), is to make it easy to apply this type of stochastic analysis to a wide range of problems.)

计算有空气孔介质光子晶体的色散曲线，供大家交流，互相学习(Calculated free pore Photonic crystal dispersion curves for all to exchange, learn from each other)

In this paper, we introduce a new machine-learning-based data classification algorithm that is applied to network intrusion detection. The basic task is to classify network activities (in the network log as connection records) as normal or abnormal while minimizing misclassification. Although different classification models have been developed for network intrusion detection, each of them has its strengths and weaknesses, including the most commonly applied Support Vector Machine (SVM) method and the Clustering based on Self-Organized Ant Colony Network (CSOACN). Our new approach combines the SVM method with CSOACNs to take the advantages of both while avoiding their weaknesses. Our algorithm is implemented and evaluated using a standard benchmark KDD99 data set. Experiments show that CSVAC (Combining Support Vectors with Ant Colony) outperforms SVM alone or CSOACN alone in terms of both classification rate and run-time efficiency.

基于DV-Hop的定位算法，利用三边测量以及极大似然估计法实现定位，误差较低，有误差分析图。(Based on DV-Hop localization algorithm, using trilateration and maximum likelihood estimation method to achieve localization, lower error margin of error analysis chart.)