资源列表
SGA
- 简单的遗传算法，可以参考-simple genetic algorithm, can refer to
gailvshenjingwangluodeyuce
- 用基于概率的神经网络进行预测，仿真表明，预测精度良好-Probability-based neural network prediction, simulation results show that good prediction accuracy
network5
- 人工神经网络的具体应用实例，包含源代码完整程序。-Specific application examples of artificial neural network, contains the source code complete program.
改进遗传算法－郭涛算法做最优化问题很管用
- 改进遗传算法－郭涛算法做最优化问题很管用,算法的基本思想是 先任意产生n个随机数，然后从n个数里随机选择m个数，再有这m个 数合成一个新数，将这个新数同n个数中间适应值函数值的最差的比较， 如果好的话就取代最差的那个，如果它比最好的还要好的话，则把最好的 也取代。如果比最差的坏，则重新合成一个新数。依次循环下去。 程序的奇妙之处是GA_crossover()函数，产生的新数确实比较好，看看 那位大侠能改进一下，产生比这跟好的数。-improved genetic algo
PNN-faultdiagnosis
- 对于大数据学习的同学，本案例具有·非常好的引领作用，特别适合初学者！非常实用！-For large data learning students, this case has a very good leading role, especially for beginners! Very useful!
2007011374
- Solving Traveling Sales Man Problem by the Algorithm of Simulated Annealing. Src only. 100 free of compile error. Including 2 test cases.-Solving Traveling Sales Man Problem by the Algorithm of Simulated Annealing. Src only. 100 free of compile erro
preprocess_data.
- 利用matlab实现BP神经网络数据预处理-BP neural network using matlab data pre-processing to achieve
ganzhiqi_s200502106
- 模式识别-基于感知函数准则的线性分类器设计，完全自编代码，有详细说明。-pattern recognition-based perceptual function criteria for the classification of linear design, completely writing code is described in detail.
chengxu.rar
- 利用改进后的遗传算法来实现排样优化的关键代码,The use of genetic algorithm to optimize the layout to achieve
GA
- 遗传算法源代码。。以一个实际优化为例。。包括各个子程序和主程序。可运行。-Genetic algorithm source code. . Optimization of a practical example. . Including all subroutines and main program. Can run.
bacteria
- 智能算法中的细菌算法的matlab源程序。运行出来的那个动态图，细菌算法的收敛速度非常快，程序也非常简单，整个流程也非常容易，适合刚接触智能算法的人学习。-The bacterial algorithm in the intelligent algorithm matlab source. Run out of dynamic map, bacteria convergence rate of the algorithm is very fast, the program is very sim
sga
- 改代码是单目标遗传算法的简单实现，简单易懂，注释明了，适合初学者！-Change the code is simple to achieve a single objective genetic algorithms, easy to understand, comment and clear, suitable for beginners!