资源列表
PSOt
- 这是关于粒子群算法的matlab程序，希望可以对有需要的人有帮助！-This is the particle swarm algorithm matlab procedures, hope that can help people in need!
leaf_by_recursion
- 模拟植物生长算法的分形树源程序，根据迭代次数的不同获得一株完整的植物生长图，对其具体的参数进行修改可以做一些人工智能方面的优化-The plant growth simulation algorithm of the fractal tree source code, modified according to the different number of iterations to obtain a complete plant growth chart, the specific para
PSO-BP
- PSO算法从随机解出发，通过迭代寻找最优解，通过适应度来评价解的品质，粒子群优化Bp网络源程序，仅供参考-PSO algorithm from random solutions to find the optimal solution by iteration, the fitness evaluation solution quality, particle swarm optimization Bp network source, for reference only
16975721
- Fortran基本算法源程序,线性方程,矩阵运算,非线性方程,极值问题()
SVM
- SVM核心思想是:对于输入空间中非线性可分的情形,选择一个适当的非线性映射,将输入空间中的样本点映射到一个高维空间,然后通过一系列核函数、参数因子的选择得到最优分界面。-SVM core idea is: For the non-linear input space can be divided into the case, select an appropriate nonlinear mapping the input space sample point is mapped to a hi
Gauss-SVM
- 基于Gauss 核函数SVM分类机，使用二阶几何方法训练。-Gauss kernel function SVM classification based on machine, using the geometric method of second-order training.
MathSolve
- 北京航空航天大学 计算机学院离散数学习题解答——doc-Beijing University of Aeronautics and Astronautics School of Computer Science Discrete Mathematics Problem Solving
PatternRecognition
- 图象模式识别工程从简单到复杂的各个举例工程的源代码-Image pattern recognition works from simple to complex projects in all, for example source code
遗传算法算例
- 文中详细介绍了函数优化（有无约束均可）、组合优化算法的原理和源程序，算法效率极高，欢迎下载。附件有更多的遗传算法算例，共研究算法用。 -paper describes in detail the function optimization (there may be bound), combinatorial optimization algorithms and the principle source, the algorithm very efficient and welcome to
GSA
- 万有引力搜索算法文献，希望对大家有所帮助-A paper about GSA，Best wishes for you
k12
- k-means 算法接受输入量 k ；然后将n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。-K- means algorithm accept input k Then could be divided into k n data object clustering in order to make the clustering obtained
Guided_Local_Search_to_the_TSP
- 与禁忌搜索动态修改邻域结构的方法不同, GLS的基本原则是通过不断改变搜索空间的地形(landscape)来帮助搜索过程逐步移出局部极值的, 也就是说搜索过程中解结构和邻域结构将保持不变, 而目标函数将被动态修改, 以使得当前的局部极值不再具有局部最优性。-Guided Local Search sits on top of local search heuristics and has as a main aim to guide these procedures in exploring e