function?psobp?
%?BP?neural?network?trained?by?PSO?algorithm
%?Copyright?by?Deng?Da-Peng?@?2005
%?Email:?rexdeng@163.com
%?You?can?change?and?distribute?this?code?freely?for?academic?usage
%?Business?usage?is?strictly?prohibited
clc
clear?all

AllSamIn=...;?%?Add?your?all?input?data
AllSamOut-...;?%?Add?your?all?output?data?

%?Pre-processing?data?with?premnmx?you?can?use?other?functions
global?minAllSamOut;
global?maxAllSamOut;
[AllSamInnminAllSamInmaxAllSamInAllSamOutnminAllSamOutmaxAllSamOut]?=?premnmx（AllSamInAllSamOut）;
%?draw?10?percent?from?all?samples?as?testing?samples，the?rest?as?training?samples
i=[10:10:1000];
TestSamIn=[];
TestSamOut=[];
for?j=1:100
TestSamIn=[TestSamInAllSamInn（:i（j））];?
TestSamOut=[TestSamOutAllSamOutn（:i（j））];
end
TargetOfTestSam=...;?%?add?reall?output?of?testing?samples
TrainSamIn=AllSamInn;
TrainSamOut=AllSamOutn;
TrainSamIn（:i）=[];
TrainSamOut（:i）=[];
%?Evaluating?Sample
EvaSamIn=...
EvaSamInn=tramnmx（EvaSamInminAllSamInmaxAllSamIn）;?%?preprocessing

global?Ptrain;
Ptrain?=?TrainSamIn;
global?Ttrain;
Ttrain?=?TrainSamOut;

Ptest?=?TestSamIn;
Ttest?=?TestSamOut;

%?Initialize?BPN?parameters
global?indim;
indim=5;
global?hiddennum;
hiddennum=3;
global?outdim;
outdim=1;

%?Initialize?PSO?parameters
vmax=0.5;?%?Maximum?velocity
minerr=0.001;?%?Minimum?error
wmax=0.90;
wmin=0.30;
global?itmax;?%Maximum?iteration?number
itmax=300;
c1=2;
c2=2;
for?iter=1:itmax
W（iter）=wmax-（（wmax-wmin）/itmax）*iter;?%?weight?declining?linearly
end?
%?particles?are?initialized?between?（ab）?randomly
a=-1;?
b=1;
%Between?（mn）?（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;
%?Initialize?positions?of?particles
rand（‘state‘sum（100*clock））;
X=a+（b-a）*rand（ND1）;
%Initialize?velocities?of?particles
V=m+（n-m）*rand（ND1）;

global?fvrec;
MinFit=[];
BestFit=[];

%Function?to?be?minimized?performance?functioni.e.mse?of?net?work
global?net;
net=newff（minmax（Ptrain）[hiddennumoutdim]{‘tansig‘‘purelin‘}）;
fitness=fitcal（XnetindimhiddennumoutdimDPtrainTtrainminAllSamOutmaxAllSamOut）;
fvrec（:11）=fitness（:11）;
[CI]=min（fitness（:11））;
MinFit=[MinFit?C];
BestFit=[BestFit?C];
L（:11）=fitness（:11）;?%record?the?fitness?of?particle?of?every?iterations
B（111）=C;?%record?the?minimum?fitness?of?particle
gbest（1:1）=X（I:1）;?%the?global?best?x?in?population

%Matrix?composed?of?gbest?vector?
for?p=1:N?
G（p:1）=gbest（1:1）;
end
for?i=1:N;
pbest（i:1）=X（i:1）;
end
V（::2）=W（1）*V（::1）+c1*rand*（pbest（::1）-X（::1））+c2*rand*（G（::1）-X（::1））;
%V（::2）=cf*（W（1）*V（::1）+c1*rand*（pbest（::1）-X（::1））+c2*rand*（G（::1）-X（::1）））;
%V（::2）=cf*（V（::1）+c1*rand*（pbest（::1）-X（::1））+c2*rand*（G（::1）-X（::1）））;
%?limits?velocity?of?particles?by?vmax
for?ni=1:N
for?di=1:D
if?V（nidi2）>vmax