《游戏编程中的人工智能技术》一书中123章源代码

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

apriori算法c语言版,apriori算法c语言版-algorithm language version algorithm algorithm c language version

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 .

遗传算法解决TSP问题,在vb开发环境中，在网上找的。-Genetic algorithm to solve TSP problems in vb development environment, in the Internet looking for.

这是一个用matlab编制的DE算法的源程序，希望大家喜欢！-This is a preparation using the Matlab DE algorithm source, hope you like them!

遗传算法初始化网络权值及其阈值参数 提高控制性能-Genetic algorithm to initialize the network weights and threshold parameters improve control performance

雷达数据预测，包括航迹仿真、卡尔曼滤波等 -radar data projections, including track simulation, Kalman filtering

模糊神经网络的源码,课程设主计.主要用来识别电子鼻的信号.-FNN source, the Comptroller course is set up. Primarily used to identify the electronic nose signal.

神经网络BP算法-neural network algorithm

文件中包含某老师关于人工神经网络控制系统的PPT，较详细的介绍了人工神经网络及应用于控制的相关内容。-File contains a teacher on the artificial neural network control system of PPT, a more detailed introduction to the artificial neural network and applied to the control of relevant content.