Hi there, I am using Neural Network for making predictions. I have been using the default 'dividerand' with 70%, 15%, 15% of the data for training, validation and testing, respectively. Here is the simple code I use: net = newff (x,y,20); net.perform
3 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
Hi there, I am using Neural Network for making predictions. I have been using the default 'dividerand' with 70%, 15%, 15% of the data for training, validation and testing, respectively. Here is the simple code I use: net = newff (x,y,20); net.performFcn = 'mse'; net.trainParam.goal = 1e-6; net.trainParam.min_grad = 1e-20; net.trainParam.epochs = 1000; net.trainParam.max_fail =50; net=init(net); net=train(net,inputs,target); I use geophysical data (spans over low, high and moderate solar activity). I tried to simulate using new data and I found that the Network was biased towards low solar activity. I thought the problem could be that the percentage for validation and testing does not cover the entire solar cycle. I want to divide the data myself so that all the 3 solar cycles are well represented in the training, validation and testing sets. How can I do this? Found online that I could use the 'divideblock' but how does it work? Thank you Racheal
3 Kommentare
Greg Heath
am 4 Jul. 2015
What are the other 5 inputs?
How many measurements per day?
12627/365.25/24 = 1.44 ?
Is there any time delay between input and output?
You may need to have several models.
If sunspotnum <= ss1 then ..
etc
If so, try no data division with dividetrain to determine the minimum number of hidden nodes.
Then plot error rate vs
a. each of the 8 inputs
b. each of the 8 principal components (PCA)
c. each of the 8 principal coordinates (PLS)
>> lookfor principal
Hope this helps
Greg
Antworten (0)
Siehe auch
Kategorien
Mehr zu Pattern Recognition and Classification finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!