搜索资源列表
beibao
- 模拟退火算法是一种通用的随机搜索算法,是对局部搜索算法的扩展。与一般局部搜索算法不同,SA以一定的概率选择领域中目标值相对较小的状态,是一种理论上的全局最优算法。-Simulated annealing algorithm is a common random search algorithm is an extension of local search algorithm. Different general local searc
COR(v1.1)
- 资源竞争算法,此算法经过测算由于PSO、GA、SA等等,可以快速有效的优化。- Competition Over Resource Optimization Algorithm,
GA_CVRP
- 用遗传算法和模拟退火实现的限重的VRP(CVRP),250个customer,一个仓库,数据文件在里面,导入eclipse就可以运行。-this is a hybrid algorithm of GA and SA to solve CVRP problem, which is devopled on eclise with java.it contains a input dataset involved with 249 custo
TSP(GAaSA)
- 基于模拟退火的遗传算法求解旅行商TSP问题-TSP by GA+SA
PSA-nonuSA_89941
- 粒子群模拟退火算法 混合算法 可以运行 效果不错-pso sa
simulated-nnealing-algorithm
- 模拟退火算法对求解函数最小值很有效果 代码-sa simulated nnealing-spso-algorithm
Radiation-focusing-device
- 这个是用SA模拟退火算法设计的辐射聚焦器,当然自己可以修改成光束整形程序,用在光束整形中,虽然有效果,但是结果还是不完善。-This is SA simulated annealing algorithm designed to focus the radiation is, of course, they can be modified to beam shaping procedures used in beam shaping,
Subspace-Methods-for-Joint
- 基于子空间方法的联合稀疏恢复,通过MUSIC算法进行结合测试,给出了测试结果-We propose subspace-augmented MUSIC (SA-MUSIC), which improves on MUSIC such that the support is reliably recovered under such unfavorable conditions. Combined with a subspace
SA_TSP
- 使用模拟退火算法解决旅行商问题,在matlab里面实现-use SA algorithm to solve traveling salesman problem
Desktop
- 退火算法用于解决SA问题。商旅问题的java算法。(Annealing algorithm is used to solve the SA problem. Java algorithm for business travel problems.)
omp-master
- 压缩感知重构算法之正交匹配追踪(OMP)。 一个不错的MATLAB源代码。(% Othogonal matching pursuit of Tropp et Gilbert % Find greedy approx of s s.t. phi*s = y % y : data % phi : measurement matrix % K :number of vectors used in the approx (typ. =
[muchong.com]WinFormCallMatlab
- 算法学习,通过学习PCA降纬,来实现数据的特征值提取,进而抽取主成份数据来实现功能(learn speak told you alougt sdhekt dhisfnka shdfi isaf ifak if sa)
tsp
- matlab源代码用模拟退火算法求解TSP问题(we use SA to solve TSP problem, This is the matlab code.)
神经网络入门13课源码
- 神经网络入门13课源码 第一课 MATLAB入门基础 第二课 MATLAB进阶与提高 第三课 BP神经网络 第四课 RBF、GRNN和PNN神经网络 第五课 竞争神经网络与SOM神经网络 第六课 支持向量机( Support Vector Machine, SVM ) 第七课 极限学习机( Extreme Learning Machine, ELM ) 第八课 决策树与随机森林 第九课 遗传算法( Genetic Al