加了动量项和域值，比较好用的bp网络算法

遗传算法优化程序-genetic algorithm optimization procedures

汉字识别系统，非常简单，网络上看到的 一起学学啊，对模式的一个简单匹配.-Chinese character recognition system is very simple, see the Web together to learn ah, the model of a simple matching.

这是一个遗传算法中所目标优化的问题，请大家借鉴，-This is a genetic algorithm-objective optimization in the issue and we learn from

模拟退火算法求解经典图论中的图着色问题的源程序-simulated annealing algorithm classic graph coloring map of the source

模糊技术与神经网络技术选编-fuzzy and neural network technology Selected

pso优化bp神经网络的程序,是师兄论文里的程序,希望对大家有用!-PSO bp neural network optimization procedure is the senior thesis in the process, I hope useful for all of us!

此文档是遗传算法原理加源代码。 生物的进化是一个奇妙的优化过程，它通过选择淘汰，突然变异，基因遗传等规律产生适应环境变化的优良物种。遗传算法是根据生物进化思想而启发得出的一种全局优化算法。 -This document is the principle of genetic algorithm source code increases. Biological evolution is a wonderful optimization process, it eliminated by choosing a sudden variation of genetic and other changes in the law to adapt to the environment arising from the fine species. Genetic algorithm is based on ideas of biological evolution and the inspiration derived from a global optimization algorithm.

我收集整理的有关于系统辨识的资料和一些方法的优缺点-I have collated the information on the system identification and the advantages and disadvantages of some methods

用AMZI做的只能西湖地图，初学者做，人工智能-AMZI do with the only West Lake maps, beginners do, and artificial intelligence

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