GA Utility 5 (pascal)

ACO优化，用于SIMO通信系统的盲检测中，对通信系统的性能进行优化-ACO optimization, SIMO communication system for blind detection, the right to optimize the performance of communication systems

用GAlib库实现的解决0/1背包问题的遗传算法程序源代码。- Solution 0/1 knapsack question heredity algorithm procedure source code realizes which with the GAlib storehouse.

人工智能中的全局搜索的九宫问题用VC实现 这下九宫问题的三种搜索方法都齐了-artificial intelligence of the global search JiuGongTu problems with VC JiuGongTu this issue under the three search methods are here

神经网络相关算法，使用java语言进行描述的-Neural network-related algorithms, using the java language to describe the

神经网络的相关资料 神经网络的基础知识及其在MATLAB中的实现-matlab16neuro network

一个A*算法的的实现过程的演示，是个非常清晰的能理解到A是怎么实现的-A* algorithm is a realization of the demonstration process, it is a very clear to understand how the realization of A

仿真1：首先把网络温度参数T固定在100，按工作规则共进行1000次状态更新,把这1000次状态转移中网络中的各个状态出现的次数Si（i＝1，2，…，16）记录下来 按下式计算各个状态出现的实际频率： Pi=Si/∑i=1,NSi=Si/M 同时按照Bo1tzmann分布计算网络各个状态出现概率的理论值： Q（Ei）=（1/Z）exp（-Ei/T） 仿真2：实施降温方案，重新计算 采用快速降温方案：T（t）= T0/（1+t） T从1000降到0.01，按工作规则更新网络状态 当T=0.01时结束降温，再让T保持在0.01进行1000次状态转移，比较两种概率-a simulation : First of all network parameters temperature T fixed at 100 and, according to the rules for a total of 1000 to update the state, this state of the 1000 network transfer of all states for the number of Si (i = 1, 2, ..., 16) all recorded determined by the formula state-of the actual frequency : Pi = Si/i = 1, NSi = Si/M in accordance with Bo1tzmann distributed computing network of states all probability the theoretical value : Q (Ei) = (1/Z) exp (- Ei/T) Simulation 2 : implementation of cooling, re-using rapid cooling programs : T (t) = T0/(1 t) T dropped to 0.01 from 1000 and, according to the rules updated network state when T = 0.01 at the end of cooling, let T at 0.01 for the 1000 state tran

基于VC开发的自适应遗传算法源程序 大家一起学习吧-development of the VC-based Adaptive genetic algorithm source you learn it

1、编制程序显示印章图像（24位真彩色位图）； 2、读出位图中每一像素点的（R，G，B）样本值； 3、以RGB其中某两个（或三个）为坐标，取一定数量的图像点为分析样本，分析其坐标系中的分布； 4、采用本章学习的方法找到分类判别函数，对这些样本进行分类；（要求首先将印章与底纹区分，如有可能将印章、底纹、签字区分） 5、将分类后的结果标记到原始图像上，检查其效果。 -1, the preparation procedures showed that the seal image (24-bit true color bitmap) 2, read out each bitmap pixel (R, G, B) sample value 3 to RGB which a two (or three ) for the coordinates, take a certain number of image points for the analysis of samples, analysis of its coordinate system of distribution 4, the method used in this chapter to learn to find discriminant function classification of these samples for classification (required first to distinguish between the seal and Shading, if possible, will seal, Shading, signatures to distinguish between) 5, will be the result of classification markings to the original image to check its effectiveness.