用模拟退火法高效的计算图论中哈密顿贿赂的最短路径

在自然界中生物从其亲代继承特性或性状，这种现象称为遗传（Heredity），研究这种现象的科学叫遗传学（Genetics）。 在构成生物的细胞（Cell）中含有染色体（Chromosome），生物的遗传信息都包含在染色体中。遗传信息由基因（Gene）组成，基因是DNA（Deoxyribonucleic Acid）或RNA（Ribonucleic Acid）中占有一定位置的基本遗传单位。在DNA中遗传信息在长链上按照一定模式排列，即进行了遗传编码。遗传基因在染色体中占据的位置称为基因座（Locus），同一个基因座可能具有的全部基因称为等位基因（Allele），一个细胞核中所有染色体携带的遗传信息的全体称为基因组（Genome）。-err

用Java编的人工智能的归结反演算法-Java series attributed to the anti-artificial intelligence algorithms

CPN能提供强大的分类功能.介绍CPN(Counter Propagation Networks)神经网络的结构、训练及计算的基础上,详细讨论了CPN在离散事件动态系统(DEDS)仿真、仿真优化等方面的应用。 -CPN can provide powerful classification. Introduction CPN (Counter Propagation Networks) neural network structure, training and calculated on the basis of a detailed discussion of the CPN in Discrete Event Dynamic System (DEDS) simulation, simulation optimization applications.

自适应共振理论（ART）-Adaptive Resonance Theory (ART)

遗传算法基本原理PPT下载，适合初学者学习，一共有四部分。-The basic principles of genetic algorithms PPT download, suitable for beginners to learn, a total of four parts.

本人这学期写八数码作业，采用三种人工智能算法，在vc2003开发完成-I write this semester eight digital operations, in three artificial intelligence algorithm, developed in vc2003

聚类分析简介，是个课件，希望对大家有帮助。-Introduction Cluster analysis is a courseware, we want to help.

一组老外编写的神经网络源代码，非常值得收藏和研究。有BPN、ART1、ADALINE、SOM、CPN、HOPFIELD、BOLTZMAN等神经网络。吐血推荐在进行神经网络编程的朋友下载。 -a group of foreigners prepared neural network source code is very worthwhile collection and research. There are 7.82, ART1, ADALINE, SOM, CPN, Hopfield, BOLTZMAN such as neural networks. Suggest to vomit blood during neural network programming downloaded friends.

Apriori算法的JAVA实现A priori算法的JAVA实现-Apriori algorithm JAVA realize A priori algorithm realize JAVA

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.