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?

Testing

The 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));

> > end

If they are originally random, why do you have to randomize them again?

> > load y21; testing files

> > A=y21;

> > % mapping and normalizing

You 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 should

be 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 function

Use '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 runs

net = 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 possible

net.trainParam.goal = 0.01*mean(var(T))

net.trainParam.show = 10

loop 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-Yn

I'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