用于求解TSP问题的matlab程序，能直接运行

数据挖掘经典Apriori算法C源码实现-Classic Apriori data mining algorithm C source code to achieve

神经网络的运用，比较基础，对初学者比较有用-The use of neural networks, basis of comparison, more useful for beginners

一个利用hough变换来检测图像中直线的函数的源代码-using a Hough transform to detect images linear function of the source code

利用c语言实现人工智能神经网络的bam算法-Using c language bam neural network artificial intelligence algorithms

用c＋＋写的用于FPGA设计中布图布线的工具源码，有几万行程序，包含文档-using c++ to write for FPGA design the layout of the wiring tools source, there are tens of thousands of procedure, the document contains

遗传算法 遗传算法简称GA(Genetic Algorithm)，在本质上是一种不依赖具体问题的直接搜索方法。遗传算法在模式识别、神经网络、图像处理、机器学习、工业优化控制、自适应控制、生物科学、社会科学等方面都得到应用。在人工智能研究中，现在人们认为“遗传算法、自适应系统、细胞自动机、混沌理论与人工智能一样，都是对今后十年的计算技术有重大影响的关键技术”。-Referred to as genetic algorithm genetic algorithm GA (Genetic Algorithm), in essence is a kind of do not rely on specific issues of direct search method. Genetic algorithm in pattern recognition, neural networks, image processing, machine learning, optimization of industrial control, adaptive control, biological sciences, social sciences and so on have been applied. Research in artificial intelligence, now that the " genetic algorithms, adaptive systems, cellular automata, chaos theory and artificial intelligence, are the next ten years of the computing technology have a major impact on the key technology."

文件中包含某老师关于人工神经网络控制系统的PPT，较详细的介绍了人工神经网络及应用于控制的相关内容。-File contains a teacher on the artificial neural network control system of PPT, a more detailed introduction to the artificial neural network and applied to the control of relevant content.

最新的支持向量机工具箱，有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999. -The newest work tools of svm,it will be very convenient to have it.

基于RBF的PID控制matlab程序，望大家参考，共同学习。-PID control based on RBF

模糊技术与神经网络技术选编-fuzzy and neural network technology Selected