粒子群优化神经网络

%% 基于PSO的Bp神经网络预测2022赛季NBA总冠军
clc;
clear;
tic
close all;
%% 加载神经网络的训练样本 测试样本每列一个样本 输入P 输出T
load('basket.mat')%加载数据
P = trains(:,1:end-1) ;%训练集输入
T = trains(:,end) ;%训练集输出
P_test = tests(:,1:end-1) ;%测试集输入
T_test = tests(:,end) ;%测试集输出
cur_season = pred ;%待预测的数据,今年NBA季后赛数据,第1行为勇士队数据,第2行为凯尔特人队数据
inputnum=size(P,2);                                     %输入层神经元个数
hiddennum=2*inputnum+1;                                 %初始隐层神经元个数
outputnum=size(T,2);                                    %输出层神经元个数
w1num=inputnum*hiddennum;                               %输入层到隐层的权值个数
w2num=outputnum*hiddennum;                              %隐层到输出层的权值个数
N=w1num+hiddennum+w2num+outputnum;                      %待优化的变量的个数
%% 定义粒子群优化算法参数
nVar=N;                                                 %变量数目
VarSi

This add-in to the PSO Research toolbox (Evers 2009) aims to allow an artificial neural network (ANN or simply NN) to be trained using the Particle Swarm Optimization (PSO) technique (Kennedy, Eberhart et al. 2001). This add-in acts like a bridge or interface between MATLAB’s NN toolbox and the PSO Research Toolbox. In this way, MATLAB’s NN functions can call the NN add-in, which in turn calls the PSO Research toolbox for NN training. This approach to training a NN by PSO treats each PSO particle as one possible solution of weight and bias combinations for the NN (Settles and Rylander ; Rui Mendes 2002; Venayagamoorthy 2003). The PSO particles therefore move about in the search space aiming to minimise the output of the NN performance function. The author acknowledges that there already exists code for PSO training of a NN (Birge 2005), however that code was found to work only with MATLAB version 2005 and older. This NN-addin works with newer versions of MATLAB till versions 2010a. HELPFUL LINKS: 1. This NN add-in only works when used with the PSORT found at, http://www.mathworks.com/matlabcentral/fileexchange/28291-particle-swarm-optimization-research-toolbox. 2. The author acknowledges the modification of code used in an old PSO toolbox for NN training found at http://www.mathworks.com.au/matlabcentral/fileexchange/7506. 3. User support and contact information for the author of this NN add-in can be found at http://www.tricia-rambharose.com/ ACKNOWLEDGEMENTS The author acknowledges the support of advisors and fellow researchers who supported in various ways to better her understanding of PSO and NN which lead to the creation of this add-in for PSO training of NNs. The acknowledged are as follows: * Dr. Alexander Nikov - Senior lecturer and Head of Usaility Lab, UWI, St. Augustine, Trinidad, W.I. http://www2.sta.uwi.edu/~anikov/ * Dr. Sabine Graf - Assistant Professor, Athabasca University, Alberta, Canada. http://scis.athabascau.ca/scis/staff/faculty.jsp?id=sabineg * Dr. Kinshuk - Professor, Athabasca University, Alberta, Canada. http://scis.athabascau.ca/scis/staff/faculty.jsp?id=kinshuk * Members of the iCore group at Athabasca University, Edmonton, Alberta, Canada.
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