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是MATLAB的模拟退火码 -MATLAB is the simulated annealing code is simulated annealing MATLAB code is simulated annealing MATLAB code
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数学建模常用算法——模拟退火算法，内附算法简介ppt及Matlab程序代码-Commonly used mathematical modeling algorithms- simulated annealing algorithm, containing a brief ppt algorithm and Matlab code
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详细讲述模拟退火算法的理论原理。并TSP问题为例进行讲解，，并各处多种语言的源代码，包括c，matlab和delphi。
-Describe in detail the theoretical principles of the simulated annealing algorithm. And the TSP as an example to explain, and throughout the source code in multiple languages, including
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this code solves vehicle routing problem using simulated annealing algorithm in matlab.
in this code , we create different models and then evaluate them using SA algorithm.
one of the advantage of this code , is that code is splitting diff
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SA with schwefel matlab code
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课程设计时编写的matlab程序代码，基于混沌的模拟退火算法，pwm整流器的建模仿真。- Course designed to prepare the matlab program code, Chaos-based simulated annealing algorithm, Modeling and simulation pwm rectifie.
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通过matlab代码，插值与拟合，解方程，数据分析，基于混沌的模拟退火算法。- By matlab code, Interpolation and fitting, solution of equations, data analysis, Chaos-based simulated annealing algorithm.
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本资源是Mgasa算法解决TSP问题的Matlab代码，资源中包括mgasa_main(Mgasa算法解决TSP问题代码)，mgasa_fitness(适应度求取函数代码)，mgasa_annealing(Mgasa算法中模拟退火代码)，mgasa_select(遗传算法中选择函数代码)，mgasa_crossover(遗传算法中染色体交叉互换函数代码)，mgasa_mutation(遗传算法中基因突变函数代码)，mgasa_change(Mgasa算法中选择过程代码)。同时代码中有Locati
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用模拟退火算法求解TSP问题，matlab源码，绝对可运行。-Solving traveling salesman problem with simulated annealing algorithm，matlab code.
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对于初学matlab的同学会有帮助，基于混沌的模拟退火算法，ldpc码的编解码实现。- Matlab for beginner students will help, Chaos-based simulated annealing algorithm, Codec ldpc code implementatio.
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Chaos-based simulated annealing algorithm, Constituting the modulated signals of different frequencies, Based on wavelet transform digital watermarking algorithm matlab code.
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参考多篇学术论文，可执行的matlab模拟退火算法代码(efer to several academic papers, the executable matlab simulated annealing algorithm code.)
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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 Algorithm, GA )
第十课 粒子群优化( Part
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