Using the genetic algorithm automatically Timetable

遗传算法解决随机期望值模型问题的例题-random genetic algorithms to solve the problem expectations model example

专家浅谈人工智能，doc文档-expert on artificial intelligence, doc file

支持向量机(5)libSVM-Support vector machines (5) libsvm

该程序是遗传算法的具体实现，通过该程序可以对遗传算法有更深入的了解。-The program is a concrete realization of genetic algorithm, through which the genetic algorithm program can have a deeper understanding.

线性神经网络，BP神经网络，Hopfield神经网格，Elman神经网络，RBF神经网络；在模型应用模块中实现了六种实际应用：RBF网络的船用柴油机故障诊断，BP网络的齿轮箱故障诊断，SOM网络的回热系统故障诊断，BP网络的设备状态分类器，SOM网络的人口比例样本分类，SOM网络的土壤性状样本分类。-Linear neural network, BP neural network, Hopfield neural network, Elman neural network, RBF neural network application modules in the model to achieve practical application of Six: RBF network of marine diesel engine fault diagnosis, BP network gearbox fault diagnosis, SOM network regenerative system fault diagnosis, BP network classifier equipment, SOM network the proportion of the population sample classification, SOM network classification of soil samples.

人工智能中的广度搜索的九宫问题 用VC实现-breadth of artificial intelligence search JiuGongTu issue with VC

有关粒子群算法的求解tsp的c++程序。-the PSO for the c tsp procedures.

BP小波神经网络快速学习算法研究and 小波神经网络程序-BP wavelet neural network learning algorithm for rapid research and wavelet neural network procedures

神经网络学习二维非线性函数 ,多维非线性辨识的MATLAB程序-neural network learning 2D nonlinear function, multi-dimensional nonlinear identification procedures MATLAB

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.