```Date: Jan 19, 2013 9:34 PM
Author: Greg Heath
Subject: Re: neural network

"Jamaa Ambarak" <jamaa73@yahoo.com> wrote in message <kd49q6\$c8u\$1@newscl01ah.mathworks.com>...---SNIP> > Dear Greg,> > the training data for example > > let we say that there are 15 drivers here and every driver has his own number of lane deviation and they vary e.g d1 has 50 times , d2 has 300,  d3 has 400 times ,,,,,,,d15 has 200 times out of lane. the drift is very also some times one second or 2 second and after that the driver take action. so the most important for me the first sample  of the lane deviation this happen at lateral position = -/+ 0.81 m and then  I cut out the rest. so I have two kinds of windows ''1'' or ''0'' , the  first '1' window  start one second = 50 samples before the lane happen, and the '0' start after the driver go back to the lane and its same size of window one.the 1 window is [150X50], inputs are 50 samples lateral  position, 50 samples speed velocity, and 50 samples steer-angle. and the target is 1 it means out of the lane, as well the 0 window but has  target =0.  then the total 1 windows and 0 windows > > for driver d1 100 windows 50/50 in and out. so as well for the other drivers. the > >training data I did select 10 drivers for training and 5 drivers for testing. Less bias if you average over 3 trials so that each driver is used twice for training and once for testing.> > for testing data example d1 has 50 times lane  deviation > the total length of  lateral  position is 400320 after I cut the duration when the drivers >out of the lane as well same length for other features...Training data:The 150-dim input vector corresponding to a "1" target is one second of 3-dim data (@ 50 samples/sec) with lateral deviation < 0.81m JUST BEFORE a continuous interval >= 1sec with lateral deviation >=  0.81 m.Correct?The 150-dim input vector corresponding to a "0" target is one second of 3-dim data (@ 50 samples/sec) with lateral deviations < 0.81 m JUST AFTER a continuous interval >= 1sec with lateral deviations > 0.81 m.Correct?TestingThe 150-dim input vector corresponding to a test target is a one second sliding window of 3-dim data (@ 50 samples/sec). Correct? > > here is the code > > %% load inputs and targats data > > Ptemp = P;          random 1's or 0's windows > > Ttemp = T;              (0 or 1)> > rnd=randperm(3858);> > for ii=1:length(rnd)> >     P(:,ii)=Ptemp(:,rnd(ii));> >     T(:,ii)=Ttemp(:,rnd(ii));> > endIf they are originally random, why do you have to randomize them again?> > load y21; testing files > > A=y21;> > % mapping and normalizingYou might want to try it without mapping and normalizing> > [pn,ps]=mapminmax(P);> > [tn,ts]=mapminmax(T); should I normalizing the target or no I see no reason to normalize the targets. {0,1} is fine and shouldbe used with a logsig output transfer function.> > [an]= mapminmax('apply',A,ps);> > % %% create network (3 layers with 6 nodes 10 nodes)'' There is no reason to use more than one hidden layer> >traingdm '','trainscg','trainlm',trainbfg) ,,,, which one should I use as traing functionUse 'trainscg' for classification problems with {0,1} targets. Otherwise use the default 'trainlm' unless there are speed/memory problems.> > % net = newff(minmax(pn),[8  6  1],{'tansig','tansig','purelin'},'traingdm');> > which one should I use as activation function.. rand(0)                           % Initialize the RNG so you can duplicate runsnet = newff(minmax(pn),[ H  1],{'tansig','logsig'},'trainscg');Optimize a minimum successful H using trial and error.> > % % view(net)delete below to use as many defaults as possible> > % net.trainParam.epochs=1000;> > % net.performFcn = 'mse';> > % neuronal_net.trainParam.show =50; > > % net.trainParam.goal = 1e-6;> > % net.trainParam.lr=0.3;> > % net.trainParam.min_grad	=1e-6;> > % net.efficiency.memoryReduction;> > % net.trainParam.mc=0.6;> > % net = init(net);> > % % net=train (net,P,T);delete above to use as many defaults as possiblenet.trainParam.goal = 0.01*mean(var(T))net.trainParam.show = 10loop over candidate values for H (10 trials each) to optimize H and random initial weights> > % [net,tr] = train(net,pn,tn);[ net, tr, Y, E ] = train(net,pn,T);   %  Yn = sim(net,pn); E = T-YnI'll let you figure out the rest.Greg> > % t_pred=sim(net,pn);> > % y=sim(net,an);> > % y1 =mapminmax('reverse',y,ts);> > % % y1 =mapstd('reverse',y,ts);> > % predicted = hardlim(y1' - 0.5);> >      please read my notes beside the code and please tell me is it correct or no, I have training the network and give me %100 Sensitivity and %98 Specificity for most testing drivers
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