基于主成份分析（PCA）的人脸特征识别核心源程序。

ACO优化，用于SIMO通信系统的盲检测中，对通信系统的性能进行优化-ACO optimization, SIMO communication system for blind detection, the right to optimize the performance of communication systems

goat遗传算法工具包，可参考使用，节省遗传算法开发时间-goat genetic algorithm tool kit, refer to the use of genetic algorithms to save development time

一个介绍基于共识策略的cPLS文档，该方法具有更高的预测精度，由于传统PLS方法。有实例-A consensus-based strategy to introduce cPLS documents, the method has higher prediction accuracy, because of the traditional PLS method. There are examples of

神经网络算法程序，对数学建模的同学很有用处。谢谢大家喜欢。-Neural network algorithm procedures, mathematical modeling of the students very useful. Thank you all like it.

模拟退火算法：源于遗传算法，用来解决优化、决策等复杂的模型和问题。-Simulated annealing algorithm: from the genetic algorithm, to solve optimization, decision-making complex models and problems.

搜索文章中的文字 更快找到符合要求的内容-English text article more quickly find the content to meet the requirements

一个可自我学习的人工智能小模型，可进行训练，也可进行提问。 希望对大家研究人工智能算法上有帮助！-A small self-learning artificial intelligence model for training, but also ask questions. We hope that research on artificial intelligence algorithms help!

针对TSP问题，用类神经网路的方法去求解！-against TSP, and the neural network approach to solving!

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

This a GA implementation using binary and real coded variables. Mixed variables can be used. Constraints can also be handled. All constraints must be greater-than-equal-to type (g >= 0) and normalized (see the sample problem in prob1 in objective()).-This a GA implementation using binary and real coded variables. Mixed variables can be used. Constraints can also be handled. All constraints must be greater-than-equal-to type (g gt;= 0) and normalized (see the sample problem in prob1 in objective()).