source codes of the book "Genetic Algorithm" by Wang Xiaoping.

一个hopfield神经网络很好的程序，与大家分享！-Hopfield neural network a very good program to share with you!

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

这是马少平 朱小燕编著的人工智能配套课件，由清华大学出版社出版-This is Ma Shao-ping ZHU Xiao-yan matching artificial intelligence authoring courseware, from Tsinghua University Publishing House

经典的BPN人工神经网络例子源码,可用于函数逼近，预测等方面-classic example of artificial neural network source, function approximation can be used to forecast etc.

基于遗传算法实现的人工控制的登月工程的模拟程序-based on genetic algorithm to achieve the artificial control of the simulated lunar landing procedures

该算法经运行，无任何问题，可用于解决遗传算法求解车辆路线问题-The algorithm by running without any problems, can be used for genetic algorithm to solve Vehicle Routing Problem

自组织系统Kohonen网络模型。对于Kohonen神经网络，竞争是这样进行的：对于“赢”的那个神经元c，在其周围Nc的区域内神经元在不同程度上得到兴奋，而在Nc以外的神经元都被抑制。网络的学习过程就是网络的连接权根据训练样本进行自适应、自组织的过程，经过一定次数的训练以后，网络能够把拓扑意义下相似的输入样本映射到相近的输出节点上。网络能够实现从输入到输出的非线性降维映射结构：它是受视网膜皮层的生物功能的启发而提出的。~..~-Kohonen network model. For Kohonen neural network, competition is this : For the "winner" of neurons c, in its switching around the region neurons in varying degrees, to be excited, and the switching outside the neurons were inhibited. Network learning is a process in the network connecting the right under the training samples for adaptive, self-organizing process, after a certain number of training, network topology can sense similar to the mapping of the input samples similar to the output nodes. Network can be achieved from input to output of nonlinear reduced-dimensional mapping structure : it is subject to retinal cortex of the biological function inspired by. ~ ~ ..

因为递归程序运行的效率一般都比较低,本程序用java语言来实现TSP递归程序的优化-because recursive program run average efficiency are low, the procedure used java language to achieve TSP recursive process optimization

matlab神经网络应用设计-Matlab neural network application design

本文采用红外光电传感器技术, 建立了一个基于神经网络的客流计数系统, 与传 统的仅利用光电传感器计数方法相比, 该方法提高了实时客流计数准确率。为了更好的分 类识别计数, 针对采集的连续的时空序列客流数据特点, 改进了已有的分割方法和针对客 流实际情况的特征提取方法。最后经过稀疏和密集客流模拟仿真测试, 结果表明, 本文所 提出的模型能够识别多人并排的情况, 误差率较低, 具有理论和实际意义。-RBF