java的神经网络工具箱

人工智能经典问题TSP问题的Java实现，内含源程序和调试过程生成的文件-AI classic TSP Java realize that contains source and debugging process produces files

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

SMC1602A(16*2)模拟口线接线方式 连接线图: --------------------------------------------------- |LCM-----51 | LCM-----51 | LCM------51 | --------------------------------------------------| |DB0-----P0.0 | DB4-----P0.4 | RW-------P2.5 | |DB1-----P0.1 | DB5-----P0.5 | RS-------P2.6 | |DB2-----P0.2 | DB6-----P0.6 | E--------P2.7 | |DB3-----P0.3 | DB7-----P0.7 | VLCD接1K电阻到GND| --------------------------------------------------- Keyboard接线 PS/2--------51 1 DATA------P3.4 3 GND 4 VCC 5 CLK-------P3.3 接在51的外部中断，触发方式为低电平 本程序源码只供学习参考，不得应用于商业用途，如有需要请联系作者。-SMC1602A (16* 2) analog line port to connect wiring diagram:- | LCM-51 | LCM-51 | LCM 51 | | | DB0-P0.0 | DB4-P0.4 | RW-P2.5 | | DB1-P0.1 | DB5-P0.5 | RS-P2.6 | | DB2-P0.2 | DB6-P0.6 | E P2.7 | | DB3-P0.3 | DB7-P0.7 | VLCD access 1K resistor to GND |-Keyboard Connection PS/2 51 1 DATA P3.4 3 GND 4 VCC 5 CLK-P3.3 access in 51 of the external interrupt trigger mode for low-level source for learning the procedu

vb的波尔兹曼机（含模拟退火算法），有需要的可参考一下 -vb the Boltzmann machine (with simulated annealing algorithm), the need of a reference

人工智能课件，好不容易从老师那里考的，觉得老师讲的还蛮不错的-AI courseware, finally, where the exam from the teacher felt that the teacher talked about quite a good

Adaptive Resonance Theory Brain Modeling 人工智能人工神经网络源码-Adaptive Resonance Theory Brain Modeling artificial neural network artificial intelligence source

knn分类模式识别可用于文本挖掘和其他的识别分类,vc代码,-Commission pattern recognition can be used for classification of text mining and other identification classification, vc code,

遗传算法，通过选择、交叉、变异求取函数最优解c程序简单实现，并拥有最优保存思想。-Genetic algorithm, selection, crossover and mutation to strike a function of the optimal solution c procedures are simple to achieve, and has saved the best idea.

在vc控制台中运行，用遗传算法求解最短路径，用邻接矩阵表示路线图。-in vc console operations, Genetic Algorithm for the shortest path, adjacency matrices road map.

用于机器人的识别路程的优化,用与其中，去得很好的结果-For the robot to identify the optimal way to use and which to very good results