Particle Swarm Optimization (PSO) for the TSP

介绍人工智能语言Prolog的书籍-introduced artificial intelligence language books Prolog

一套从入门到精通的Matlab学习教材，附有大量例子，包括神经网络源代码-a proficient from entry to the study and teaching of Matlab, with a large number of examples, including source code neural network

pso智能算法程序，结合这个程序会对算法有更深的认识-intelligent algorithm PSO procedures, combined with this procedure will have a deeper understanding of algorithm

OPENCV系列的，关于CORE，应该挺有用的，大家自己看看吧！-OPENCV series of CORE, should be quite useless, we all see!

Hopfield Model Autoassociative Memory 源码, 经典的HOPFIELD人工神经网络例子源码-Hopfield Model Autoassociative Memory source, HOPFIELD classic example of artificial neural network source

支持向量机的matlab代码.很有参考价值的源代码-Support vector machine matlab code. A good reference source code

实现了RBF网络，已载入训练和测试样本，可根据需要将其更换-Achieved a RBF network, has been printed in the training and test samples may be needed to replace

Pascal Programs Printed in GENETIC ALGORITHMS IN SEARCH, OPTIMIZATION, AND MACHINE LEARNING by David E. Goldberg -Pascal Programs Printed in GENETIC ALGORITHMS IN SEARCH, OPTIMIZATION, AND MACHINE LEARNING by David E. Goldberg

人工股市（Artificial Stock Market，简称ASM）是模拟股市运作的一个程序。在这个电脑中的虚拟市场中，若干被称为交易者的人工智能程序(Agent)通过观察它们所在的数字世界中股价和股息的不断变换而做出预测，并且根据这些预测做出购买股票与否以及购买股票数量的决策。反过来，所有的交易者的决策又决定了股票的价格，这样，整个的股票交易市场就构成了一个自我封闭的计算系统。同时，这些交易者都具有学习的能力，可以根据以前预测的成功或者失败对自己的决策进行调整，并且通过一种被称为遗传算法的方法产生创新能力。-artificial stock market (Artificial Stock Market, or ASM) is a stock market simulation operation of the procedure. In this computer in the virtual market, a number of traders known as artificial intelligence (Agent) by observing them where the digital world and the stock dividend and the constantly changing projections, and based on these projections made whether or not to purchase stocks and the purchase of shares in decision-making. Conversely, all the decision-making transactions in turn determines the share price, so that the entire stock market would constitute a self-enclosed computer system. Moreover, these transactions are capable of learning, can be predicted based on previous success or failure of their decisions for the

基于自组织特征映射网络的聚类分析，是在神经网络基础上发展起来的一种新的非监督聚类方法，分析了基于自 组织特征映射网络聚类的学习过程，分析了权系数自组织过程中邻域函数和学习步长的一般取值问题，给出了基于自组织 特征映射网络聚类实现的具体算法，并通过实际示例测试，证实了算法的正确性。 -Based on self-organizing feature map network cluster analysis, neural network is developed on the basis of a new non-supervised clustering method, analysis based on self-organizing feature map network clustering of the learning process, analyzed the weights of self-organization the process of neighborhood function and learning step-size problem of the general values are given based on the self-organizing feature map network clustering algorithm to achieve concrete and practical examples of tests, confirmed the correctness of the algorithm.