├── NNCSAPSO.m ├── NNCSAPSO2.m ├── NNPSO.m ├── NNSAPSO.m ├── README.md ├── fun.m ├── main.m ├── nnfitness.m ├── 各算法拟合效果图（训练目标误差0.00001） ├── PSO误差效果.jpg ├── 三种算法收敛过程对比.jpg ├── 函数实际图形.jpg ├── 基本粒子群算法对比.jpg ├── 基础PSO拟合.jpg ├── 模拟退火粒子群算法对比.jpg └── 混沌模拟退火粒子群算法对比.jpg └── 各算法拟合效果对比图（训练目标误差0.001） ├── 函数实际图像.png ├── 基础粒子群算法拟合效果.png ├── 模拟退火粒子群算法拟合效果.png ├── 混沌模拟退火粒子群算法拟合效果.png └── 目标函数收敛过程.png /NNCSAPSO.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tinysimpler/Neural-network-nonlinear-function-fitting-based-on-particle-swarm-optimization-algorithms/f90c727f0fda8018eed41008c0f11a0351da7987/NNCSAPSO.m -------------------------------------------------------------------------------- /NNCSAPSO2.m: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | # Neural-network-nonlinear-function-fitting-based-on-particle-swarm-optimization-algorithms 2 | PSO , Simulated Annealing PSO , Chaotic SAPSO, Neural network, Nonlinear Function 3 | 一、训练目标误差0.00001 4 | 5 | 1、PSO误差效果图： 6 | 7 | ![image](https://github.com/Tinysimpler/Neural-network-nonlinear-function-fitting-based-on-particle-swarm-optimization-algorithms/blob/master/%E5%90%84%E7%AE%97%E6%B3%95%E6%8B%9F%E5%90%88%E6%95%88%E6%9E%9C%E5%9B%BE%EF%BC%88%E8%AE%AD%E7%BB%83%E7%9B%AE%E6%A0%87%E8%AF%AF%E5%B7%AE0.00001%EF%BC%89/PSO%E8%AF%AF%E5%B7%AE%E6%95%88%E6%9E%9C.jpg) 8 | 9 | 2、三种算法收敛过程比对 10 | 11 | ![image](https://github.com/Tinysimpler/Neural-network-nonlinear-function-fitting-based-on-particle-swarm-optimization-algorithms/blob/master/%E5%90%84%E7%AE%97%E6%B3%95%E6%8B%9F%E5%90%88%E6%95%88%E6%9E%9C%E5%9B%BE%EF%BC%88%E8%AE%AD%E7%BB%83%E7%9B%AE%E6%A0%87%E8%AF%AF%E5%B7%AE0.00001%EF%BC%89/%E4%B8%89%E7%A7%8D%E7%AE%97%E6%B3%95%E6%94%B6%E6%95%9B%E8%BF%87%E7%A8%8B%E5%AF%B9%E6%AF%94.jpg) 12 | 13 | 3、函数实际图形 14 | 15 | ![image](https://github.com/Tinysimpler/Neural-network-nonlinear-function-fitting-based-on-particle-swarm-optimization-algorithms/blob/master/%E5%90%84%E7%AE%97%E6%B3%95%E6%8B%9F%E5%90%88%E6%95%88%E6%9E%9C%E5%9B%BE%EF%BC%88%E8%AE%AD%E7%BB%83%E7%9B%AE%E6%A0%87%E8%AF%AF%E5%B7%AE0.00001%EF%BC%89/%E5%87%BD%E6%95%B0%E5%AE%9E%E9%99%85%E5%9B%BE%E5%BD%A2.jpg) 16 | 17 | 4、基础粒子群算法拟合 18 | 19 | ![image](https://github.com/Tinysimpler/Neural-network-nonlinear-function-fitting-based-on-particle-swarm-optimization-algorithms/blob/master/%E5%90%84%E7%AE%97%E6%B3%95%E6%8B%9F%E5%90%88%E6%95%88%E6%9E%9C%E5%9B%BE%EF%BC%88%E8%AE%AD%E7%BB%83%E7%9B%AE%E6%A0%87%E8%AF%AF%E5%B7%AE0.00001%EF%BC%89/%E5%9F%BA%E6%9C%AC%E7%B2%92%E5%AD%90%E7%BE%A4%E7%AE%97%E6%B3%95%E5%AF%B9%E6%AF%94.jpg) 20 | 21 | 5、模拟退火算法拟合 22 | 23 | ![image](https://github.com/Tinysimpler/Neural-network-nonlinear-function-fitting-based-on-particle-swarm-optimization-algorithms/blob/master/%E5%90%84%E7%AE%97%E6%B3%95%E6%8B%9F%E5%90%88%E6%95%88%E6%9E%9C%E5%9B%BE%EF%BC%88%E8%AE%AD%E7%BB%83%E7%9B%AE%E6%A0%87%E8%AF%AF%E5%B7%AE0.00001%EF%BC%89/%E6%A8%A1%E6%8B%9F%E9%80%80%E7%81%AB%E7%B2%92%E5%AD%90%E7%BE%A4%E7%AE%97%E6%B3%95%E5%AF%B9%E6%AF%94.jpg) 24 | 25 | 6、混沌模拟退火粒子群算法拟合 26 | 27 | ![image](https://github.com/Tinysimpler/Neural-network-nonlinear-function-fitting-based-on-particle-swarm-optimization-algorithms/blob/master/%E5%90%84%E7%AE%97%E6%B3%95%E6%8B%9F%E5%90%88%E6%95%88%E6%9E%9C%E5%9B%BE%EF%BC%88%E8%AE%AD%E7%BB%83%E7%9B%AE%E6%A0%87%E8%AF%AF%E5%B7%AE0.00001%EF%BC%89/%E6%B7%B7%E6%B2%8C%E6%A8%A1%E6%8B%9F%E9%80%80%E7%81%AB%E7%B2%92%E5%AD%90%E7%BE%A4%E7%AE%97%E6%B3%95%E5%AF%B9%E6%AF%94.jpg) 28 | -------------------------------------------------------------------------------- /fun.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Tinysimpler/Neural-network-nonlinear-function-fitting-based-on-particle-swarm-optimization-algorithms/f90c727f0fda8018eed41008c0f11a0351da7987/fun.m -------------------------------------------------------------------------------- /main.m: -------------------------------------------------------------------------------- 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