I'm dealing with some problem about reproducing results of ANN. Namely in the parameters of ANN there are 2 that I cannot address:
net. Inputs{i}.range
net.output.range
The documentation is very lapidary and cannot help me to understand how it works. Both seem to have massive impact on the output. Let's consider this MWE:
net=feedforwardnet(10);
net.inputs{1}.size=3;
%net.inputs{1}.range=[0 100;0 100;0 100];
%net.output.range=[1 200;];
net.layers{2}.size=1;
L1=[-1.1014, -2.1138, -2.6975;
-2.3545, 0.7693, 1.7621;
-1.1258, -1.4171, -3.1113;
-0.7845, -3.7105, 0.1605;
0.3993, 0.7042, 3.5076;
0.283, -3.914, -1.3428;
-2.0566, -3.4762, 1.3239;
-1.0626, 0.3662, 2.9169;
0.1367, 2.5801, 2.5867;
0.7155, 2.6237, 2.5376;];
B1=[3.5997, 3.1386, 2.7002, 1.8243, -1.9267, -1.6754, 0.8252, 1.0865, -0.0005, 0.6126];
L2=[0.5005, -1.0932, 0.34, -1.5099, 0.5896, 0.5881, 0.4769, 0.6728, -0.9407, -1.0296];
B2=0.1567;
net.IW{1}=L1;
net.Lw{2,1}=L2;
net.b{1}=B1';
net.b{2}=B2;
input=[40; 30; 20];
output=net(input)
If you uncomment line 3 and 4 the result rises from 0.1464 to 119.1379. I'm trying to reproduce this aspect of Matlab ANN in another environment, but the documentation is too short and does not explain anything.
What do these two parameters exactly do? I mean, what exact function is applied on the input and output data?
I'm having some serious issues fitting an exponential function (Beer-Lambert law) to my data. The optimization toolset function that I'm using produces terrible fits:
function [ Coefficients ] = fitting_new( Modified_Spectrum_Data,trajectory )
x_axis = trajectory;
fun = #(x,x_axis) (x(1)*exp((-x(2))*x_axis));
start = [Modified_Spectrum_Data(1) 0.05];
nlm = nlinfit(x_axis,Modified_Spectrum_Data,fun,start,opts);
Coefficients = nlm;
end
Data:
Modified_Spectrum_Data = [1.11111111111111, 1.08784976353957, 1.06352170731165, 1.04099672033640, 1.02649723285838, 1.00423806910703, 0.994116452961827, 0.975928861361604, 0.963081773802984, 0.953191520906905, 0.940636278551651, 0.930360007604054, 0.922259178548511, 0.916659345499171, 0.909149956799775, 0.901241601559703, 0.895375741449218, 0.893308346234150, 0.887985459843162, 0.884657500398024, 0.883852990694089, 0.877158499678129, 0.874817832833850, 0.875428444059047, 0.873170360623947, 0.871461252768665, 0.867913776631497, 0.866459074988087, 0.863819528471106, 0.863228815347816 ,0.864369045426273 ,0.860602502500599, 0.862653463581049, 0.861169231463016, 0.858658616425390, 0.864588421841755, 0.858668693409622, 0.857993365648639]
trajectory = [0.0043, 0.9996, 2.0007, 2.9994, 3.9996, 4.9994, 5.9981, 6.9978, 7.9997, 8.9992, 10.0007, 10.9993, 11.9994, 12.9992, 14.0001, 14.9968, 15.9972, 16.9996, 17.9996, 18.999, 19.9992, 20.9996, 21.9994, 23.0003, 23.9992, 24.999, 25.9987, 26.9986, 27.999, 28.9991, 29.999, 30.9987, 31.9976, 32.9979, 33.9983, 34.9988, 35.999, 36.9991]
I've tried using multiple different fitting functions and messing around with the options, but they don't seem to make too big of a difference. Additionally, I've tried changing the initial guess, but again that doesn't really make a difference.
Excel seems to be able to fit the data perfectly fine, but I have 900 rows of data I want to fit so doing it in Excel is not possible.
Any help would be greatly appreciated, thank you.
You'll want to use the cftool. Your data looks to follow a power law. Then choose 'Modified Spectrum Data' as your x axis and 'Trajectory' as your y. Select 'Power' from the drop down menu towards the top of the GUI.
Modified_Spectrum_Data = [1.11111111111111, 1.08784976353957, 1.06352170731165, 1.04099672033640, 1.02649723285838, 1.00423806910703, 0.994116452961827, 0.975928861361604, 0.963081773802984, 0.953191520906905, 0.940636278551651, 0.930360007604054, 0.922259178548511, 0.916659345499171, 0.909149956799775, 0.901241601559703, 0.895375741449218, 0.893308346234150, 0.887985459843162, 0.884657500398024, 0.883852990694089, 0.877158499678129, 0.874817832833850, 0.875428444059047, 0.873170360623947, 0.871461252768665, 0.867913776631497, 0.866459074988087, 0.863819528471106, 0.863228815347816 ,0.864369045426273 ,0.860602502500599, 0.862653463581049, 0.861169231463016, 0.858658616425390, 0.864588421841755, 0.858668693409622, 0.857993365648639]
trajectory = [0.0043, 0.9996, 2.0007, 2.9994, 3.9996, 4.9994, 5.9981, 6.9978, 7.9997, 8.9992, 10.0007, 10.9993, 11.9994, 12.9992, 14.0001, 14.9968, 15.9972, 16.9996, 17.9996, 18.999, 19.9992, 20.9996, 21.9994, 23.0003, 23.9992, 24.999, 25.9987, 26.9986, 27.999, 28.9991, 29.999, 30.9987, 31.9976, 32.9979, 33.9983, 34.9988, 35.999, 36.9991]
cftool
Screenshot:
For more information on the curve fitting (cftool), see: https://www.mathworks.com/help/curvefit/curvefitting-app.html
This is an extension of my previous question: https://dsp.stackexchange.com/questions/28095/choosing-low-pass-filter-parameters
I am recording people from an overheard camera. I have tracks of each's head using some software. I want to periodicity from tracks due to head wobbling.
I apply low-pass butterworth filter. I want the starting point and ending point of the filtered to be same as unfiltered tracks.
Data:
K>> [xcor_i,ycor_i ]
ans =
-101.7000 -77.4040
-102.4200 -77.4040
-103.6600 -77.4040
-103.9300 -76.6720
-103.9900 -76.5130
-104.0000 -76.4780
-105.0800 -76.4710
-106.0400 -77.5660
-106.2500 -77.8050
-106.2900 -77.8570
-106.3000 -77.8680
-106.3000 -77.8710
-107.7500 -78.9680
-108.0600 -79.2070
-108.1200 -79.2590
-109.9500 -80.3680
-111.4200 -80.6090
-112.8200 -81.7590
-113.8500 -82.3750
-115.1500 -83.2410
-116.1500 -83.4290
-116.3700 -83.8360
-117.5000 -84.2910
-117.7400 -84.3890
-118.8800 -84.7770
-119.8400 -85.2270
-121.1400 -85.3250
-123.2200 -84.9800
-125.4700 -85.2710
-127.0400 -85.7000
-128.8200 -85.7930
-130.6500 -85.8130
-132.4900 -85.8180
-134.3300 -86.5500
-136.1700 -87.0760
-137.6500 -86.0920
-138.6900 -86.9760
-140.3600 -87.9000
-142.1600 -88.4660
-144.7200 -89.3210
Code(answer by #SleuthEye):
dataOut_x = xcor_i(1)+filter(b,a,xcor_i-xcor_i(1));
dataOut_y = ycor_i(1)+filter(b,a,ycor_i-ycor_i(1));
Output:
In the above example, the endpoint(to the left) is different for filtered and unfiltered tracks. How can I ensure it is same?
Your question is pretty ambiguous, and doesn't really have a specific question. I'm assuming you want to have your filtered data start at the same points as the measured data, but are unsure why this is not happening already, and how to do so.
A low pass filter is a filter which lowers the effect of rapid changes. One way of doing this, and the method which appears to be used here, is by using a rolling average. A rolling average is simply an average (mean) of the previous data points. It looks like you are using a rolling average of 5 data points. Therefore you need five points of raw data before your filter will give you a single data point.
-101.7000 -77.4040 }
-102.4200 -77.4040 } }
-103.6600 -77.4040 } }
-103.9300 -76.6720 } }
-103.9900 -76.5130 } Filter point 1. }
-104.0000 -76.4780 } Filter point 2.
-105.0800 -76.4710
-106.0400 -77.5660
-106.2500 -77.8050
-106.2900 -77.8570
-106.3000 -77.8680
-106.3000 -77.8710
In order to solve this problem, you could just append the first data point to the data set four times, as this means that the filter will produce the same number of points. This is a pretty rough solution, however, as you are creating new data. This could be achieved quite simply, for example if your dataset is called myArray:
firstEntry = myArray(1,:);
myNewArray = [firstEntry; firstEntry; firstEntry; firstEntry; myArray];
This will create four data points equal to your first data point, which should then allow you to apply the low pass filter to your data, and have it start at the same point.
Hope this helps, although it's worth bearing in mind that filtering ALWAYS results in a loss of data.
Because you don't want to implement it but want someone else to:
The theory as above is correct, but instead you need to add 2 values at the end of your vectors:
x_last = xcor_i(end);
y_last = ycor_i(end);
xcor_i = [xcor_i;x_last;x_last];
ycor_i = [ycor_i;y_last;y_last];
This gives the following:
As you can see the ends are pretty close to being the same now.
My file format is *.Vt2 , It' strong ground motion record.
It can be download at http://peer.berkeley.edu/smcat/search.html
choose a earthquake then download the record of any station .Data like this:
PEER STRONG MOTION DATABASE RECORD. PROCESSING BY PACIFIC ENGINEERING.
TAIWAN SMART1 (45) 11/14/86, SMART1 C00, EW
VELOCITY TIME HISTORY IN UNITS OF CM/S. FILTER POINTS: HP=0.1 Hz LP=25.0 Hz
NPTS= 4000, DT= .01000 SEC
.9437205E-03 .1497919E-01 .3328475E-01 .5111011E-01 .6865002E-01
.8659123E-01 .9975034E-01 .1072606E+00 .1168364E+00 .1217983E+00
.1135203E+00 .8993586E-01 .6435175E-01 .3819334E-01 .1840042E-01
What I want to do is skip the 4 headerlines and read the numbers (by row),then save them in a
N×1 matrix M. But I don't know how to make it.
Any help is greet
Try:
x=importdata('filename.txt');
Your data will be:
x.data
I believe you might try this:
fid = fopen(filename,'r');
A = textscan(fid,'%f%f%f%f%f','Delimiter',',','headerLines',4);
data = cat(2,A{:});
I hope it helps.