CPN算法的matlab实现，CPN人工神经网络

采用的是康力山等人确定的实验参数。 对于n个城市的旅行商问题，其参数如下： 初始温度：t0=280, 每一个温度下采用固定的迭代次数L=100n, 温度的衰减系数alpha=0.92 算法停止的准则是当相邻两个温度得到的解变化很小时算法停止。-used the Stanozolol Hill were determined by the experimental parameters. N cities for the traveling salesman problem, the parameters are as follows : initial temperature : t0 = 280, each a fixed temperature of the iteration number L = 100n, temperature attenuation coefficient alpha = 0.92 algorithm to stop the criteria when two adjacent the temperature change was very Solution Algorithm stop.

求图中的祖先节点(不含父亲节点) 内含说明，学图论或人工智能的朋友会用到的。希望有利于理解书上的论述。-plans for their ancestral nodes (excluding the father nodes) intron show that studying graph theory in artificial intelligence or a friend will use the. Hope to understand book expositions.

前馈神经网络算法-feed forward neural network algorithm

一个简单遗传算法，用一个测试问题测试,到到得结果比较好-This is a simple genetic algorithm and the algorithm is tested by a test problem, and the result is not bad

英国Sheffield大学遗传算法函数包，可节省遗传算法函数开发-Sheffield University United Kingdom function genetic algorithm package, can save the development of genetic algorithm function

我的文件是用VC设计的一个求解人工智能中8数码问题的程序。-my VC document is designed to solve a digital AI 8 issue procedures.

销售预测系统，可以根据历史销售数据对未来的销售量进行有效预测，采用BP神经网络对预测模型进行训练，可以达到不错效果-sales forecasting system, based on historical sales data for the future effective sales forecasts, BP neural network model to predict the training, they can achieve good results

DataMining软件(集成了关联规则、k-均值聚类、模糊聚类、k-中心点聚类四种算法) -DataMining software (integrated association rules, k-means clustering, fuzzy clustering, k-center clustering of four algorithms)

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

遗传算法的函数优化sga-genetic algorithm optimization function sga