feedforward net for regression.
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I have 1000 sets of data. each set consists of 4 input variables and one output variable. Variables 1, 2, and 3 remain same but only variable 4 changes for one set of data that has 10 records. So, total data size is 10000 x 5. As mentioned variables 1,2 and 3 only change from set to set and within a set they remain constant or same.
How would I use this data to train.
Here is an e.g.
.2, .4, .5, .8 ..... 12
.2, .4, .5, .2 ------ 7
-------------- 10 lines
.1, .6, .8, .5 -------- 10
.1, .6, .8, .8 ---------- 6.3
---------------------- 10 lines
and so on..
total 10000, by 5 Matrix.
Thanks in advance.
4 个评论
Abolfazl Chaman Motlagh
2021-9-11
I recommend you to split data :
X = data(1:4,:);
Y = data(5,:);
and just type :
nftool
the mathwork work on it perfectly i think. and it has all documentation in it. i hope it is what you are looking for.
回答(1 个)
Swetha Polemoni
2021-9-14
Hi Pappu Murthy,
It is my understanding you wanted to train a network given input and true lables(Supervised learning). I understand that you have 1000 sets of data. Each set has 4 input variables ,1 output variable and in total you have records in each set. Though the first 3 variables doesn't change within the set, these variables are also important to the network as much as the 4th variables.
This is the example data set you have shared
2, .4, .5, .8 ..... 12
.2, .4, .5, .2 ------ 7
-------------- 10 lines
.1, .6, .8, .5 -------- 10
.1, .6, .8, .8 ---------- 6.3
---------------------- 10 lines
Suppose you ignore the first 3 values while training, the output of the network can be incorrect. For example if the input for the network is as follows
.1, .6, .8, .2
The output of the network can be 7 since we have ignored the first 3 values. Here the network considers only the fourth value i.e., .2 and gives the output 7. So to avoid such errors, all the input variables should be equal importance.
The following documentation might help you
另请参阅
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