粒子群算法优化BP神经网络权值的程序资源-CSDN文库

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粒子群算法（Particle Swarm Optimization, 简称PSO）是一种基于群体智能的优化方法，由Kennedy和Eberhart在1995年提出。它模拟了鸟群寻找食物的过程，通过群体中的每个粒子（代表可能的解）在搜索空间中的移动和更新，寻找最优解。PSO在许多领域，尤其是复杂优化问题中，如函数优化、工程设计、机器学习等，都表现出强大的求解能力。 BP（Backpropagation）神经网络是一种广泛应用的监督学习模型，用于解决非线性映射问题。其通过反向传播误差来调整网络的权重和阈值，从而实现对输入数据的高效分类或回归。然而，BP网络的训练过程可能会陷入局部最优，且收敛速度较慢。将PSO应用于BP神经网络的权重优化，可以有效改善这一问题。在本MATLAB程序中，PSO算法被用来寻找BP网络的最佳权重值，以提高网络的泛化能力和训练效率。通过粒子群的全局探索能力，可以避免传统BP网络在梯度下降法中容易陷入局部最优的问题。程序可能包含以下几个主要部分： 1. 初始化：设置粒子群的初始位置和速度，以及相关参数，如种群大小、迭代次数、惯性权重、学习因子等。 2. 粒子更新：每个粒子根据其当前位置（对应BP网络的权重）和全局最优位置（全局最优权重）更新速度和位置。 3. 适应度函数：定义一个适应度函数，用于评估每个粒子（即一组权重）下的BP网络性能，通常使用训练集上的误差或损失函数。 4. 更新全局最优：比较每个粒子的适应度，如果发现更优的解，则更新全局最优位置。 5. 循环迭代：重复步骤2和3，直到达到预设的迭代次数或满足停止条件。 6. 结果输出：最终得到的全局最优位置即为BP神经网络的最优权重。通过这种结合，PSO-BP网络不仅可以提高训练速度，还可以提高预测精度，尤其对于复杂的非线性问题。MATLAB作为一种强大的数学和科学计算工具，提供了丰富的库函数和便捷的编程环境，使得实现这种算法变得相对简单。在学习这个程序时，应重点理解PSO算法的基本原理、参数设置的影响、以及如何将其与BP网络的训练过程相结合。同时，熟悉MATLAB编程基础和神经网络模型也是必不可少的。通过阅读代码中的注释，可以深入理解算法的具体实现细节，这对于进一步研究和改进优化算法会有很大帮助。

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PSO-bp((matlab.rar （2个子文件）

PSO-bp((matlab

20041006.m 13KB

www.pudn.com.txt 279B

function psobp % BP neural network trained by PSO algorithm clc clear all % generate training samples according to eutrophication evaluation standard % of surface water st=cputime; AllSamIn=... [ 70.00 60.00 60.00 60.00 70.00 70.00 65.00 65.00 65.00 70.00 70.00 75.00 70.00 70.00 70.00 70.00 75.00 70.00 65.00 70.00 70.00 75.00 65.00 65.00 68.00 70.00 75.00 65.00 65.00 68.00 70.00 75.00 65.00 65.00 68.00 70.00 75.00 65.00 65.00 68.00 70.00 75.00 65.00 65.00 70.00 70.00 65.00 65.00 60.00 68.00 70.00 65.00 65.00 60.00 68.00 70.00 65.00 65.00 60.00 68.00 70.00 65.00 65.00 60.00 68.00 70.00 65.00 65.00 60.00 68.00 70.00 65.00 65.00 60.00 68.00 70.00 65.00 65.00 60.00 68.00 70.00 65.00 65.00 60.00 68.00 70.00 65.00 65.00 60.00 68.00 70.00 65.00 65.00 60.00 68.00 70.00 65.00 65.00 60.00 68.00 70.00 65.00 65.00 60.00 68.00 70.00 65.00 65.00 60.00 68.00 70.00 65.00 65.00 60.00 68.00 70.00 65.00 65.00 60.00 68.00 70.00 75.00 65.00 65.00 68.00 70.00 75.00 65.00 65.00 68.00 70.00 75.00 65.00 65.00 68.00 70.00 75.00 65.00 65.00 68.00 135.00 120.00 135.00 100.00 105.00 135.00 120.00 135.00 100.00 105.00 135.00 120.00 135.00 100.00 105.00 135.00 130.00 135.00 100.00 105.00 135.00 135.00 135.00 90.00 105.00 135.00 120.00 135.00 105.00 105.00 130.00 120.00 85.00 85.00 90.00 110.00 120.00 80.00 80.00 90.00 120.00 120.00 80.00 80.00 90.00 120.00 120.00 80.00 80.00 90.00 120.00 100.00 80.00 80.00 90.00 135.00 135.00 120.00 105.00 105.00 135.00 130.00 130.00 105.00 105.00 135.00 130.00 130.00 105.00 105.00 135.00 135.00 135.00 105.00 105.00 135.00 135.00 135.00 105.00 105.00 110.00 110.00 110.00 85.00 85.00 110.00 110.00 110.00 85.00 85.00 110.00 110.00 110.00 85.00 85.00 110.00 110.00 110.00 85.00 85.00 100.00 100.00 80.00 85.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 100.00 85.00 80.00 80.00 85.00 100.00 85.00 80.00 80.00 85.00 100.00 100.00 80.00 80.00 85.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 125.00 135.00 135.00 135.00 135.00 125.00 130.00 130.00 130.00 135.00 110.00 130.00 130.00 130.00 130.00 125.00 130.00 130.00 130.00 135.00 125.00 120.00 115.00 115.00 120.00 125.00 120.00 115.00 115.00 120.00 125.00 120.00 115.00 115.00 120.00 125.00 120.00 115.00 115.00 100.00 70.00 75.00 60.00 60.00 66.00 70.00 75.00 60.00 60.00 66.00 70.00 75.00 60.00 60.00 66.00 70.00 75.00 60.00 60.00 66.00 ]'; % 产生噪声，加给输出 % NoiseVar=0.1; % rand('state',sum(100*clock)); AllSamOut=[ 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 135.00 135.00 135.00 135.00 135.00 135.00 130.00 110.00 120.00 120.00 120.00 135.00 135.00 135.00 135.00 135.00 110.00 110.00 110.00 110.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 125.00 125.00 110.00 125.00 125.00 125.00 125.00 125.00 70.00 70.00 70.00 70.00 ]'; %+NoiseVar*randn(1,1000); % 预处理，压缩到[-1,+1] global minAllSamOut; global maxAllSamOut; [AllSamInn,minAllSamIn,maxAllSamIn,AllSamOutn,minAllSamOut,maxAllSamOut] = premnmx(AllSamIn,AllSamOut); % 从总样本中抽取10％的样本作为测试样本，其余作为训练样本 TestSamIn=AllSamIn; RealTestSamOut=[ 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 70.00 68.00 68.00 70.00 70.00 70.00 70.00 70.00 70.00 68.00 70.00 70.00 68.00 68.00 70.00 70.00 70.00 70.00 70.00 133.00 133.00 133.00 135.00 135.00 135.00 133.00 110.00 118.00 118.00 118.00 130.00 135.00 135.00 135.00 135.00 110.00 110.00 110.00 110.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 98.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 135.00 130.00 123.00 68.00 68.00 68.00 68.00 ]'; [TestSamInn,minTestSamIn,maxTestSamIn,RealTestSamOutn,minRealTestSamOut,maxRealTestSamOut] = premnmx(TestSamIn,RealTestSamOut); % 训练样本 TrainSamIn=AllSamIn; TrainSamOut=AllSamOut; [TrainSamInn,minTrainSamIn,maxTrainSamIn,TrainSamOutn,minTrainSamOut,maxTrainSamOut] = premnmx(TrainSamIn,TrainSamOut); % Evaluation Sample %EvaSamIn=... % [6.5400 9.4000 8.2200 5.9100 6.1300 7.8400 8.2600 8.7900 7.2900 5.9300 4.6900 3.5100 % 0.1270 0.0390 0.0820 0.0860 0.1230 0.1370 0.0190 0.0640 0.0880 0.0500 0.0860 0.0750 % 1.2840 1.3580 1.3200 0.4500 2.2200 1.4700 1.0900 1.6600 0.8400 0.6600 0.5500 0.5200 % 3.3400 4.7200 5.0300 6.0000 7.9600 4.6600 4.0000 4.1500 4.0000 3.8100 1.6300 3.4500 %0.3500 0.4500 0.3800 0.4500 0.3000 0.4500 0.4000 0.4000 0.3000 0.3000 0.4000 0.3500]; %EvaSamInn=tramnmx(EvaSamIn,minAllSamIn,maxAllSamIn); % 预处理 %********************************************************** % training set global Ptrain; Ptrain = TrainSamInn; global Ttrain; Ttrain = TrainSamOutn; % testing set Ptest = TestSamInn; %********************************************************** % Initialize BPN parameters global indim; indim=5; global hiddennum; hiddennum=3; global outdim; outdim=1; %********************************************************** % Initialize PSO parameters vmax=0.5; % Maximum velocity minerr=0.0001; % Minimum error wmax=0.90; wmin=0.30; global itmax; %Maximum iteration number itmax=100; c1=2; c2=1.8; %cf=c1+c2; for iter=1:itmax W(iter)=wmax-((wmax-wmin)/itmax)*iter; % weight declining linearly end % particles are initialized between (a,b) randomly a=-1; b=1; %Between (m,n), (which can also be started from zero) m=-1; n=1; global N; % number of particles N=40; global D; % length of particle D=(indim+1)*hiddennum+(hiddennum+1)*outdim; global fvrec; MinFit=[]; BestFit=[]; %MaxFit=[]; %MeanFit=[]; % Initialize positions of particles rand('state',sum(100*clock)); X=a+(b-a)*rand(N,D,1); %Initialize velocities of particles V=m+(n-m)*rand(N,D,1); %********************************************************** %Fun

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