KPMstats

不考虑压缩的REYNOLDS方程的求解简单程序(Not consider compression REYNOLDS equations simple procedure)

两种比较常见的最短路径的matlab算法 (Two of the more common of the shortest path algorithm matlab)

基于matlab的微粒群优化算法解决多目标的问题。(Matlab-based particle swarm optimization algorithm to solve the multi-objective problem.)

Matlab 利用GUI 实现了串口读取功能(Matlab GUI Serial Read and Write)

CDMA matlab code for single user

此matab函数文件可以用来计算模型的李雅普诺夫指数，用于稳定性分析。(this Lyapunov.m file can be used to compute the lyapunov exponential)

huffman dec2..........................................

本节概括智能控制理论及应用，包括模糊控制、神经控制的基础及其拓展应用等，在揭露传统方法局限性的基础上提出新的技术，是东南大学和南京航空航天大学的核心教材。(This section summarizes the intelligent control theory and applications, including fuzzy control, neural control on the basis of its expanding applications, expose the limitations of traditional methods based on a new technology, Southeast University and Nanjing University of Aeronautics and Astronautics, core materials.)

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.

Make the Huffman-tree from the lengths of the Huffman codes. The Huffman codes are also needed, and if they are known, they can be given as an extra input argument.