遗传算法应用

根据 trilearn_base_sources-3.3 (一般称做UVA底层)修改的robocup球队代码,对某些模块进行了详细的注释(例如BasicPlayer,WorldMode模块),并且进行了一些基本的动作添加的示范(增加了射门动作,四处看动作,截球动作和贴身带球动作),示范了简单的高层决策修改和队型的修改,书写了一套完整的守门员高层代码,希望对大家有帮助-According to trilearn_base_sources-3.3 (generally known as UVA bottom) to amend the code of RoboCup teams, for certain modules in detail the Notes (such as BasicPlayer, WorldMode module), and carried out some basic moves to add a model (an increase of shooting action , look around action, action and cut the ball moves the ball next to the skin), demonstrated a simple modification of the high-level decision-making and team-based changes, write a complete set of high-level code goalkeeper, in the hope that everyone has to help

本程序可以对输入.avi 序列中的车辆数进行计算，准确性非常高，是一个值得参考的代码:)-This procedure can be input. Avi sequence to calculate the number of vehicles, accuracy is very high, is a reference to the code:)

这个程序是关于遗传算法c源程序，可以通过这个源程序明白什么是遗传算法-on genetic algorithm c source, the source can understand what genetic algorithms

用java编写的基于小生境的遗传算法，NASG-II，对于用java的朋友来说绝对是个好东西！-Prepared by using java-based niching genetic algorithm, NASG-II, for the friends with java is definitely a good thing!

用人工智能实现的足球机器人人仿真比赛的程序-using artificial intelligence robot soccer simulation games who procedures

< 神经网络设计源代码>>一书的源代码。-err

HIGHLIGHT TRUE #define NOHIGHLIGHT FALSE #define UPDATE TRUE #define NOUPDATE FALSE #define FORMAT TRUE #define NOFORMAT FALSE #define LEFT 0-HIGHLIGHT# define NOHIGHLIGHT TRUE FALSE UPDATE# define TRUE## define FALSE de NOUPDATE fine FORMAT# define TRUE FALSE# defin NOFORMAT e LEFT 0

面试老鼠和猫连动问题的集中解决方案 可以建议应付面试问题-interview rats and cats even move the concentrated solution can meet the proposed interview questions

该程序是基于BP网络的PID控制系统。系统由两部分构成：经典的PID控制器和神经网络。-that the procedure was based on BP network PID control system. System consists of two parts : the classic PID controller and neural networks.

CPN能提供强大的分类功能.介绍CPN(Counter Propagation Networks)神经网络的结构、训练及计算的基础上,详细讨论了CPN在离散事件动态系统(DEDS)仿真、仿真优化等方面的应用。 -CPN can provide powerful classification. Introduction CPN (Counter Propagation Networks) neural network structure, training and calculated on the basis of a detailed discussion of the CPN in Discrete Event Dynamic System (DEDS) simulation, simulation optimization applications.