MATLAB的遗传算法工具

基于EKF的机器人SLAM演示程序, 对于搞机器人定位及地图构建的朋友会有一定帮助, 这个版本用的人比较多-EKF-based SLAM demo of the robot, the robot carry out positioning and mapping will have a certain friend to help, this version used by many people

一份中科院开发的语义分析源码，可编译运行。里面的代码都很完整，注释也清晰，是研究语义语法分析的必不可少的学习工程。-A semantic analysis of source code developed by Chinese Academy of Sciences, can be compiled to run. The code inside the very integrity of annotations is also clear, is to study the semantic parsing essential to study engineering.

A branch-and-bound algorithm for asymmetric TSP

人工智能的过程表示法求解九宫问题,vc开发-artificial intelligence to the process that method JiuGongTu, vc Development

单神经元pid源码实现，对初学的朋友很有帮助-The program can realize single neural pid with MATLAB.The algorithm is simple and easy to understand.

用遗传算法求解背包问题是南京航空航天大学信息与计算科学专业编写的.本程序利用遗传算法来求解背包问题.采用二进制字符串编码,1表示选择物体,0则不选择. 背包问题描述:在M件物品取出若干件放在空间为W的背包里，每件物品的重量为W1，W・2……Wn，与之相对应的价值为P1,P2……Pn。求出获得最大价值的方案。注意：在本题中，所有的重量值均为整数。-genetic algorithm knapsack problem is the Nanjing University of Aeronautics and Astronautics, Information and Computing Science prepared. The procedure the use of genetic algorithms to solve knapsack problem. binary string encoding, an option objects, 0 no choice. Knapsack Problem Description : M items removed several pieces on the space-W backpack, Each of the weight W1, W 2 ... Wn, the corresponding value of P1, P2 ... Pn. Sought to gain maximum value of the program. NOTE : In this issue, all the weight values are integers.

BP神经网络分类器C程序-BP neural network classifiers C Program

ArcObject的空间查询代码，比较简单和初步的供大家一起学习-The spatial query ArcObject code is relatively simple and everyone together for a preliminary study

一个高人的VC实现NN的程序。很不错。先运行Matrixbase.dsw,again arithmetic.dsw, next nnbp.dsw,final neuralNetWork.dsw.-an expert in VC NN procedures. Very good. First running Matrixbase.dsw, again arithmetic.dsw. next nnbp.dsw, final neuralNetWork.dsw.

根据粒子群算法的特点，用vc写出了源程序，可以实现粒子群的优化-According to the characteristics of particle swarm optimization, using vc wrote source code, you can achieve the particle swarm optimization