在matlab 环境下用遗传算法求解TSP问题。包含源代码和说明文档...

： 移动机器人路径规划是机器人学的一个重要研究领域。 针对移动场地的特点对其进行了建 模与存储，然后将场地处理成简单的连通图-: Mobile robot path planning is an important study robot research field. Characteristics for mobile sites for their modeling and storage, and then deal with the venue into a simple connected graph

RBF实现神经网络预测程序，压缩包，不用解压缩密码！-Realize RBF neural network prediction program, compressed packet, do not extract the password!

随机神经网络工具箱-random neural network toolbox

一个关于BP网络的设计，实现一个函数的逼近，了解关于MATLAB神经网络工具箱的应用。-On the design of BP network, the realization of a function approaches, learn more about the MATLAB neural network toolbox applications.

根据教材整理的神经网络程序！很多种神经网络程序-Finishing materials in accordance with the neural network program! A variety of neural network procedures

一个牛人的神经网络支持向量机代码,对于搞模式识别的朋友强烈推荐!-A wonderful program of svm code coming from a superman,and strong recommendation to someone who learn about pattern-recognition .

一套美国国家宇航局人工智能中心NASA的专家系统工具源代码-a National Center of NASA NASA artificial intelligence expert system tool for source code

模式识别HK分类算法 vc下dos程序-pattern recognition algorithm vc HK classification procedures under dos

K-均值聚类算法的编程实现。包括逐点聚类和批处理聚类。K－均值聚类的的时间复杂度是n*k*m，其中n为样本数，k为类别数，m为样本维数。这个时间复杂度是相当客观的。因为如果用每秒10亿次的计算机对50个样本采用穷举法分两类，寻找最优，列举一遍约66.7天，分成3类，则要约3500万年。针对算法局部最优的缺点，本人正在编制模拟退火程序进行改进。希望及早奉给大家，倾听高手教诲。-K-means clustering algorithm programming. Point by point, including clustering and clustering batch. K-means clustering of the time complexity of n* k* m, n samples, several types of k, m sample dimension. The time complexity is a very objective. Because if we use one billion times per second the computer using 50 samples of two exhaustive method to find the optimal set out again about 66.7 days, divided into three categories, offering 3,500 years. Local optimal algorithm against the shortcomings, I was prepared simulated annealing process improvements. Early Feng hope for everyone, listen to the master teachings.

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!