蚂蚁算法在服务质量方面的代码，主要是服务质量方面的东西...

遗传算法的通用程序，可以根据需要改编以适应不同的应用-Generic genetic algorithm procedure can be adapted to the needs of different applications

这是我自己花了很多时间写的一个模式识别源码。奉献出来与感兴趣的朋友分享，绝对可靠。.. - Dedication out with friends interested in sharing, absolutely reliable. ..

A branch-and-bound algorithm for asymmetric TSP

高手学习人工智能心得体会。想学习人工智能的可以来-master learning artificial intelligence experiences. Want to study artificial intelligence can s

这是神经网络和遗传算法想结合的c++程序，希望对需要的朋友 有所帮助。-This is a neural network and genetic algorithm would like to combine c++ Procedures, and they hope to help a friend in need.

对于学习混沌理论的人很有用的，这也是搜集到的-Chaos Theory for the study were very useful, which is collected

人工智能GA问题的C语言实现，已经调试通过，含源程序和实验报告-the C language, debugging, and experiment with the source report

模拟退火算法 模拟退火算法(Simulated Annealing,简称SA算法)是模拟加热熔化的金属的退火过程，来寻找全局最优解的有效方法之一。 模拟退火的基本思想和步骤如下： 设S＝{s1,s2,…,sn}为所有可能的状态所构成的集合， f：S―R为非负代价函数，即优化问题抽象如下： 寻找s*∈S，使得f(s*)＝min f(si) 任意si∈S （1）给定一较高初始温度T，随机产生初始状态S （2）按一定方式，对当前状态作随机扰动，产生一个新的状态S’ S’＝S＋sign(η).δ 其中δ为给定的步长， η为[－1,1]的随机数-simulated annealing algorithm (Simulated Annealing, or SA algorithm) is a simulation of heating molten metal in the annealing process, to find the global optimum one of the effective ways. Simulated Annealing basic ideas and the steps are as follows : S = (s1, s2, ..., sn) for all possible state posed by the pool, f : S-R non-negative cost function, that is abstract optimization problems are as follows : Find S* s, making f (s*) = min f (si) arbitrary si S (1) to set a higher initial temperature T, randomly generated initial state S (2) of a certain form, the current state of random disturbance, have a new state S "S" = S+ sign (). delta where given for the step, [-1,1] Random Number

神经网络vc源代码神经网络的VC++代码呀-Neural network source code vc neural network VC++ Code you

自组织系统Kohonen网络模型。对于Kohonen神经网络，竞争是这样进行的：对于“赢”的那个神经元c，在其周围Nc的区域内神经元在不同程度上得到兴奋，而在Nc以外的神经元都被抑制。网络的学习过程就是网络的连接权根据训练样本进行自适应、自组织的过程，经过一定次数的训练以后，网络能够把拓扑意义下相似的输入样本映射到相近的输出节点上。网络能够实现从输入到输出的非线性降维映射结构：它是受视网膜皮层的生物功能的启发而提出的。~..~-Kohonen network model. For Kohonen neural network, competition is this : For the "winner" of neurons c, in its switching around the region neurons in varying degrees, to be excited, and the switching outside the neurons were inhibited. Network learning is a process in the network connecting the right under the training samples for adaptive, self-organizing process, after a certain number of training, network topology can sense similar to the mapping of the input samples similar to the output nodes. Network can be achieved from input to output of nonlinear reduced-dimensional mapping structure : it is subject to retinal cortex of the biological function inspired by. ~ ~ ..