搜索资源列表
神经网络-退火算法
- 神经网络在python中应用,并引入退火算法(Neural network entry annealing algorithm in Python)
模拟退火算法
- 提出了一种基于粒子群优化(PSO)算法的径向基(RBF)网络参数优化算法,首先利用减聚类算法确定网络径向基函数中心的个数,再用PSO算法优化径向基函数的中心及宽度,最后用PSO算法训练隐含层到输出层的网络权值,找到神经网络权值的最优解,以达到优化神经网络学习的目的。最后,通过一个实验与最小二乘法优化的神经网络进行了比较,验证了算法的有效性。(Particle swarm optimization (PSO) optimization of RBF network)
模拟退火算法和遗传算法程序
- 使用MATLAB编写的模拟退火算法和遗传算法的源代码(The source code of the simulated annealing algorithm and genetic algorithm written in MATLAB)
新建文件夹 (2)
- 模拟退火算法求最短路径,matlab完整代码,可以直接运行(The shortest path of simulated annealing algorithm)
l3_simulated_annealing_algorithm
- 模拟退火算法MATLAB程序(寻找最佳路径的优化方式)(Simulated annealing algorithm MATLAB (search for optimal path optimization))
SA_TSP
- 介绍模拟退火算法,并应用到TSP问题上。并附有小程序(Introduce simulated annealing algorithm and apply it to TSP problem. With a small program)
MATLAB智能算法30个案例分析
- 采用案例形式,以智能算法为主线,讲解了遗传算法、免疫算法、退火算法、粒子群算法等最常用的智能算法的MATLAB实现。(Taking the intelligent algorithm as the main line, we explain the MATLAB implementation of the most commonly used intelligent algorithms, such as genetic algorithm, immune algorithm, annealin
Simulatedannealing
- 这是关于模拟退火算法的文献,应用于化工领域的间歇精馏过程。(This is the literature of simulated annealing algorithm, which is applied to the batch distillation process in the chemical field.)
test1
- 模拟退火算法的MATLAB简单例程,可供初学者学习了解(Simulated annealing algorithm MATLAB simple routines for beginners to learn)
模拟退火算法
- 模拟退火算法解决背包问题,matlab环境实现c流程(Simulated annealing algorithm solves knapsack problem, and realizes C process in Matlab environment.)
遗传算法退火算法
- 利用MATLAB实现模拟退火算法的在各实际情况中的应用(The application of simulated annealing algorithm.)
matlab m文件
- 基于模拟退火算法和旅行商模型的火灾预警数学建模(Mathematical modeling program of fire early warning based on simulated annealing algorithm)
chapter20 基于遗传模拟退火算法的聚类算法
- 基于matlab实现遗传、模拟算法在聚类中的应用(Application of genetic algorithm and simulation algorithm in clustering based on MATLAB)
Annealing algorithm
- 利用退火算法实现无人机路径巡航,但是效果比不上遗传算法。(The annealing algorithm is used to realize the route cruise of UAV.)
sa_bp
- 基于模拟退火算法对BP神经网络进行的改进,有效提高了精度和迭代速度。(The improvement of BP neural network based on simulated annealing algorithm effectively improves the accuracy and speed of iteration.)
30个智能算法模型
- 1-8遗传算法,9 多目标Pareto最优解搜索算法,10 基于多目标Pareto的二维背包搜索算法,11-12免疫算法,13-17粒子群算法,18鱼群算法,19-21模拟退火算法,22-24蚁群算法,25-27神经网络,28 支持向量机的分类,29 支持向量机的回归拟合,30 极限学习机的回归拟合及分类(1-8 genetic algorithm, 9 multi-objective Pareto optimal solution search algorithm, 10 multi-obje
2019美赛必备LATEX模板(仅供参考)
- 运用退火算法解决公共场合人流量问题,一次解决关于人员疏散相关问题(Applying annealing algorithm to solve the traffic problem in public places)
SA模拟退火
- 模拟退火算法寻优支持向量机C和g,实现识别分类。(Simulated Annealing Optimizes Support Vector Machines C and G for Classification)
SAforVRP
- 模拟退火算法解决VRP问题,包含所有函数的M文件,有例子(Simulated annealing algorithm solves VRP problem, including M files of all functions, and some examples)
模拟退火算法
- 含有模拟退火算法基本代码和改进后的模拟退火算法,适合算法初学者学习