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[AI-NN-PR] GA结合均匀设计表 DL : 1: 结合均匀设计表和小边经验公式产生初始种群，使TSP应用遗传算法能求出更优更快解，详尽说明见paper.doc。-uniform design combining a small side table and have empirical formula initial population, TSP application of genetic algorithms can be obtained faster and better solutions, detailed description of paper.doc.
Date : 2025-12-29 Size : 625kb User : 姚启迪
[AI-NN-PR] 我编写的monte carlo随机数发生器 DL : 0: 这是我在matlab环境下编写的产生随机数的源程序，包括产生均匀分布和正态分布，参数接口十分灵活。-in Matlab environment prepared by the random numbers generated by the source code, including the Uniform Distribution and normal distribution, parameter interface is very flexible.
Date : 2025-12-29 Size : 2kb User : 存友
[AI-NN-PR] 我编写的monte carlo随机数发生器检验程序 DL : 0: 该程序用来检验monte carlo随机数的分布是否满足随机性要求。包括独立性检验、均匀性检验和参数检验。-procedures used to test the Monte Carlo random number distribution to satisfy the requirements randomness. Including the independence test, uniform test parameters and test.
Date : 2025-12-29 Size : 3kb User : 存友
[AI-NN-PR] randomnumber DL : 0: 关于产生均匀分布和标准正态分布的随机变量的VC程序-on the production and distribution of uniform standard normal distribution of random variables VC Program
Date : 2025-12-29 Size : 1kb User : 宋淑芳
[AI-NN-PR] AI_GA_matlab DL : 0: 遗传算法程序主要程序 ga.m 遗传算法核心程序 BinaryExample.m 二进制编码应用程序 FloatExample.m 浮点编码的应用程序相关算子及函数 initializega.m 种群初始化函数 simpleXover.m 用于二进制编码的简单交叉算子 arithXover.m 用于浮点编码的算术交叉算子 binaryMutation 用于二进制编码的变异算子 nonMutation.m 用于浮点编码的非均匀变异算子 roulette.m 轮盘选择算子 normGeomSelect.m 标准化几何分布排序选择算子 maxGenTerm.m 以最大进化代数为判别条件的进化终止函数 calcbits.m 计算二进制编码染色体串长度的函数 f2b.m 由浮点表达到二进制表达的转换函数 b2f.m 由二进制表达到浮点表达的转换函数 parse.m 字符串识别函数 delta.m 非均匀变异的变异量计算函数 exampleFn 一个二元函数 startup.m 进行路径设置-Genetic Algorithm main proceedings ga.m GA BinaryExample core program. m binary floating-point applications FloatExample.m coding applications relevant operator and function initializega.m Stocks initialization function for binary series simpleXover.m Codes simple crossover operator arithXover.m for floating-point arithmetic coding crossover operator binaryM utation for binary coding mutation operator nonMutation.m float for the non-coding Uniform mutation operator roulette.m roulette selection operator normGeomSelect.m standards Sort of geometric distribution maxGenTerm.m selection operator algebras with the greatest evolutionary criterion for the evolution of termination calcbits.m calculation function binary coding chromosome length of the string from the floating-point functions f2b.m
Date : 2025-12-29 Size : 13kb User : 胡朋
[AI-NN-PR] mvdr_3n DL : 0: 均匀直线阵的自适应波束形成，采用MVDR算法，对一个均匀直线阵求出最佳权，得到方向图。-Uniform linear array adaptive beamforming, MVDR algorithm to find the optimal weight of a uniform linear array pattern.
Date : 2025-12-29 Size : 1kb User : 小雨
[AI-NN-PR] AntRoad DL : 0: 元胞自动机产生的蚂蚁高速公路现象，说明：简单的规则，能够产生非均匀的结果。-Cellular Automata have ants Highway phenomenon Description: simple rules, can produce non-uniform results.
Date : 2025-12-29 Size : 71kb User : yeqing
[AI-NN-PR] Clustering DL : 0: 根据节点分区情况，确定成簇数目是分簇算法设计的重点，这两个算法对成簇数目进行优化，均匀节点能耗，延长网络寿命。-According to the node partition, determine the number of clusters is a clustering algorithm is designed to focus on these two algorithms to optimize the number of clusters, uniform node energy consumption, extend the network lifetime.
Date : 2025-12-29 Size : 756kb User : 苏可
[AI-NN-PR] datainit DL : 0: 神经网络训练样本归一化程序，并能均匀的分离训练样本和检验样本-Neural network training samples normalized procedures, and uniform separation of training samples and testing samples
Date : 2025-12-29 Size : 1kb User : antidoo
[AI-NN-PR] ga1 DL : 0: 遗传算法程序说明: fga.m 为遗传算法的主程序采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作!-Description of the procedures for genetic algorithms: fga.m main program for the genetic algorithm using binary Gray encoding, roulette wheel based on the law of non-linear ranking selection, uniform crossover and mutation operations, but also the introduction of the inversion operation!
Date : 2025-12-29 Size : 3kb User : hexing
[AI-NN-PR] yichaunsuanfa DL : 0: fga.m 为遗传算法的主程序采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作!-fga.m the main program for the genetic algorithm using binary Gray encoding, roulette wheel based on the law of non-linear ranking selection, uniform crossover and mutation operations, but also the introduction of the inversion operation!
Date : 2025-12-29 Size : 17kb User : 张生
[AI-NN-PR] GA_TSP DL : 0: 用遗传算法解TSP问题编码方式：次序编码选择算子：轮盘赌杂交：单点变异：均匀变异-Genetic Algorithm for TSP with the issue of encoding: coding sequence selection operator: roulette hybridization: a single point mutation: uniform mutation
Date : 2025-12-29 Size : 108kb User : CuipingSu
[AI-NN-PR] yichuansuanfa DL : 0: fga.m 为遗传算法的主程序采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作!-fga.m the main program for the genetic algorithm binary Gray encoding, roulette wheel method based on non-linear ranking selection, uniform crossover and mutation operations, but also introduces the inversion operation!
Date : 2025-12-29 Size : 8kb User : 曾建
[AI-NN-PR] cp321123 DL : 0: 这是一个非常简单的遗传算法源代码，是由Denis Cormier (North Carolina State University)开发的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用Gaussian变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu,目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为‘gadata.txt’；系统产生的输出文件为‘galog.txt’。输入的文件由几行组成：数目对应于变量数。且每一行提供次序——对应于变量的上下界。如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。-This is a very simple genetic algorithm source code is determined by Denis Cormier (North Carolina State University) developed, Sita S. Raghavan (University of North Carolina at Charlotte) amendment. Code to ensure as little as possible, in fact there is no need troubleshooting. The application of a specific amendment to this code, the user simply by changing the definition of constants, and the definition of "evaluation function" can be. Note that the code is designed to seek maximum value, where the objective function can only take on positive values and the function values and individuals there is no difference between the values of adaptation. The system uses the ratio option, essence model, a single point of hybridization, and uniform mutation. If we replace the uniform Gaussian mutation variation, may get better results. Code without any graphics, or even no screen output, mainly to ensure a high portability between platforms. Readers can ftp.uncc.edu, directory coe/evol file pr
Date : 2025-12-29 Size : 4kb User : 陈朋
[AI-NN-PR] SGA DL : 0: 这是一个非常简单的遗传算法源代码，是由Denis Cormier (North Carolina State University)开发的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用 Gaussian变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu, 目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为‘gadata.txt’；系统产生的输出文件为‘galog.txt’。输入的文件由几行组成：数目对应于变量数。且每一行提供次序——对应于变量的上下界。如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。 -This is a very simple genetic algorithm is Denis Cormier source of Carolina re (State) development, Sita leads S.R aghavan (of Carolina at leads, where re. Code that is actually less as far as possible, don t find fault. For a particular application of this code, the user need revision of the constant change and define "evaluation function definition of". Note the design code for maximum, which is the objective function can take positive, And the function of the individual value and no difference between fitness. This system USES ratio, essence model, single hybridization and uniform variation. If use uniform variation and variation of Gaussian replacement may get better effect. Code without any graphics, nor even screen output, mainly is the guarantee of the platform between high portability. Readers can from the FTP uncc. J, directory coe/evol edu files in prog. C. The documents required input should be named "j" rather gadata System to produce output file for galog. J TXT Input f
Date : 2025-12-29 Size : 8kb User : hua gong
[AI-NN-PR] SimpleGA_1 DL : 0: 实现简单的进化算法，实数编码，轮盘赌选择方式，单点交叉，均匀变异。-Simple evolutionary algorithms, real coding, roulette wheel selection method, single-point crossover, uniform mutation.
Date : 2025-12-29 Size : 5kb User : cxx
[AI-NN-PR] ontrol DL : 0: 一级直线倒立摆匀速行走的模糊控制研究与实现-A uniform linear inverted pendulum walking and Implementation of Fuzzy Control
Date : 2025-12-29 Size : 789kb User : pid
[AI-NN-PR] Genetic-Algorithms DL : 0: 遗传算法的运行机理及特点是具有定向制导的随机搜索技术，其定向制导的原则是：导向以高适应度模式为祖先的“家族”方向．以此结论为基础．利用随机化均匀设计的理论和方法，对遗传算法中的交叉操作进行了重新设计，给出了一个新的GA算法，称之为随机化均匀设计遗传算法．-Operating mechanism and genetic algorithm is characterized by directional guided random search techniques, the directional guidance of the principle is: the fitness-oriented model of high ancestral " family" orientation. The basis of this conclusion. By randomized uniform design theory and method, the crossover operation in genetic algorithm has been redesigned, GA gives a new algorithm, called the randomized uniform design of genetic algorithms.
Date : 2025-12-29 Size : 102kb User : zs
[AI-NN-PR] circleGS DL : 0: GS算法，将高斯光束脉冲整形成为环形光强均匀分布的光束-GS algorithm, the Gaussian pulse shaping a uniform circular beam of light intensity
Date : 2025-12-29 Size : 1kb User : jammy
[AI-NN-PR] GAprog DL : 0: 这是一个非常简单的遗传算法源代码，是由Denis Cormier (North Carolina State University)开发的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用 Gaussian变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu, 目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为‘gadata.txt’；系统产生的输出文件为‘galog.txt’。输入的文件由几行组成：数目对应于变量数。且每一行提供次序——对应于变量的上下界。如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。 -This is a very simple genetic algorithm source code, by Denis Cormier (North Carolina State University) developed, Sita S. Raghavan (University of North Carolina at Charlotte) amendment. Code to ensure that as little as possible, in fact, do not have to troubleshoot. The application of a specific amendment to this code, the user simply changes the definition of constants and the definition of "evaluation function" button. Note that the code is designed to seek maximum value, where the objective function can only take positive and the function value and the individual is no difference between the fitness value. The system uses the ratio of choice, the essence of model, single-point crossover and uniform mutation. If you replace the uniform mutation with Gaussian mutation, may get better results. Code without any graphic or even no screen output, mainly to ensure a high portability between platforms. Readers can ftp.uncc.edu, directory coe/evol file prog.c obtained. Requires the input f
Date : 2025-12-29 Size : 5kb User : qinjian

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