simulated annealing and genetic algorithms hybrid vehicle routing problem solvin...

一个关于神经网络遗传算法的源程序，对于初学者肯定有帮助-A source code about NN(nerve network),it is helpful for beginners

This code is to estimate the parameter of model of Mixture Gauss which is popular model used in pattern classification . JAVA 写的估计复合高斯模型中的参数 -This code is to estimate the parameter of model of Jian Gauss which is popular model used in pattern classification. Java was estimated composite Gaussian model parameters

< 神经网络设计源代码>>一书的源代码。-err

一种通过自组织竞争学习网络实现数据降维和可视化的单层神经网络模型。用此算法可以把输入空间的多维映射到低维的（一维或者二维）的离散网络上，并将保持相同性质的输入数据在映射到低维空间时的拓扑一致性。iris以及letter两个数据集进行分类-A competitive learning through self-organizing network for data dimensionality reduction and visualization of single-layer neural network model. Using this algorithm can be multi-dimensional input space is mapped to the low-dimensional (one-dimensional or two-dimensional) discrete network, and will remain the same as the nature of the input data is mapped to the low-dimensional space of topological consistency. iris as well as the letter the two data sets to classify

我自己编写的A*搜索算法，效率还是很不错的，推荐。-myself prepared by the A* search algorithm, the efficiency is still very good and recommended.

利用双层感知器神经网络处理线性不可分的分类问题-use double perceptron neural networks to handle non-linear classification

细胞自动机的，很好的-Cellular Automata and fine

用C语言实现的BP网络的程序,严格按照BP的算法流程,开发环境为WIN-TC-C language of BP procedures in strict accordance with the BP algorithm. Development Environment for the WIN-TC

基于C开发的三个隐层神经网络，输出权值、阈值文件，训练样本文件，提供如下函数：1）初始化权、阈值子程序；2）第m个学习样本输入子程序；3）第m个样本教师信号子程序；4）隐层各单元输入、输出值子程序；5）输出层各单元输入、输出值子程序；6）输出层至隐层的一般化误差子程序；7）隐层至输入层的一般化误差子程序；8）输出层至第三隐层的权值调整、输出层阈值调整计算子程序；9）第三隐层至第二隐层的权值调整、第三隐层阈值调整计算子程序；10）第二隐层至第一隐层的权值调整、第二隐层阈值调整计算子程序；11）第一隐层至输入层的权值调整、第一隐层阈值调整计算子程序；12）N个样本的全局误差计算子程序。-C development based on the three hidden layer neural network, the output weights, threshold documents, training sample documents, for the following functions : a) initialization, the threshold subroutine; 2) m learning samples imported subroutine; 3) m samples teachers signal Subroutine ; 4) hidden layer of the module input and output value subroutine; 5) the output layer of the module input and output value subroutine; 6) the output layer to the hidden layer subroutine error of generalization; 7) hidden layer to the input layer subroutine error of generalization; 8) the output layer to the third hidden layer Weight adjustment, the output layer threshold adjustment routines; 9) 3rd hidden layer t

本程序用NetLogo开放，模拟行人流。为本人亲字编程。-the procedures used NetLogo open simulated flow. I affinity for the word programming.