How can I train multiple sequences in neural network using feedforwardnet?

2016 2 11
2 个回答
回答已采纳
更新时间：2016 2 14
7 次查看（30 天）

2 个投票

Hello,
I have 5 pairs (input-output) of time series. I know the way to train multiple time series using a dynamic neural network ( http://www.mathworks.com/help/nnet/ug/multiple-sequences-with-dynamic-neural-networks.html ), however I'd like to use a static neural network (feedforwardnet function) instead. How could I do that?
Thanks,
Ghazi

请先登录，再回答此问题。

 采纳的回答

Greg Heath
Greg Heath 2016-2-11

2 个投票

close all, clear all, clc
[ X0 T0 ] = simplenarx_dataset;
X(1,:) = X0(1:end-2); X(2,:) = X0(2:end-1);
X(3,:) = T0(1:end-2); X(4,:) = T0(2:end-1);
T = T0(3:end);
x= cell2mat(X); t = cell2mat(T);
vart = var(t,1)
net = feedforwardnet;
for i =1:10
net = configure(net, x, t);
[ net tr y e ] = train(net,x,t);
NMSE(i,1) = mse(e)/vart;
end
NMSE = NMSE
For your 5 series problem with 4 default 1:2 input and feedback delays,
size(X,1) = 5*4 = 20 and size(T,1) = 5*1 = 5
Hope this helps.
Thank you for formally accepting my answer
Greg

5 个评论
显示 3更早的评论 隐藏 3更早的评论

Ghazi Binarandi
Ghazi Binarandi 2016-2-12
The sample code really helps! However, I am still confused with several parts:
  1. In 'configure' function (line 10), it changes the number of input and output to 1. But, why do they interpret it that way? Since we have 4xN input matrix and 1xN target matrix.
  2. Why did you make a 'for' loop from 1 to 10?
  3. By '4 default,' does that the same with '4 elements'?
  4. What does 1:2 input mean?
  5. Why does the feedback being included in the feedforward network?
Greg Heath
Greg Heath 2016-2-12
% The sample code really helps! However, I am still confused with several parts:
% 1.In 'configure' function (line 10), it changes the number of input and output to 1. But, why do they interpret it that way?
Because there is one input and 1 output.
% Since we have 4xN input matrix and 1xN target matrix.
The N input vectors are 4-dimensional
% 2.Why did you make a 'for' loop from 1 to 10?
To mitigate getting a bad combination of random datadivision AND random
initial weights
% 3.By '4 default,' does that the same with '4 elements'? % 4.What does 1:2 input mean?
For your 5 series problem you have 4 input series and 1 target
series. The default input and feedback delays of narxnet are
both 1:2. To simulate that with feedforward net, you have to
increase the input dimension from 4 to 20.
% 5.Why does the feedback being included in the feedforward network?
Because you wanted to simulate narxnet with feedforwardnet.
Ghazi Binarandi
Ghazi Binarandi 2016-2-13
Thanks for the explanations! I just need to confirm one more thing.
So in the 'for' loop that you made, do you basically 'retrain' the network 9 times (so the network is trained 10 times) with the same input and output data set? That is why the NMSE value that you put on the code is getting less as the 'retraining' process continues (or in other words, performance is getting better)?
Greg Heath
Greg Heath 2016-2-14
I do retrain the net 9 times. HOWEVER, in order to get 10 INDEPENDENT designs, I need to use CONFIGURE to reinintialize BOTH the random trn/val/tst data division AND the random weight initialization.
You can keep the current best net through the loop, or save the current RNG state corresponding to the best net for a later redesign.
In general, I use a double loop approach with the outer loop to determine the smallest value of number of hidden nodes, H that will yield the desired goal.
Search both NEWSGROUP and ANSWERS using
for h = Hmin:dH:Hmax

请先登录，再进行评论。

更多回答（1 个）

Dennis
Dennis 2016-2-11
编辑：Dennis 2016-2-11

0 个投票

Do you mean the "input output and curve fitting" neural network type from nnstart? Since in that network, no delayed information about previous inputs/outputs is used, you can simply add all your datasets in one big matrix:
Example input u and output y (where 1 and 2 are two seperately measured datasets):
u1 = [1 2 3 4 5] y1 = [1 1 1 2 1]
u2 = [2 2 3 2 3] y2 = [3 3 3 4 3]
X = [u1 u2] T = [y1 y2]
and train the nn with input X and output T. Be aware that you still need to convert these matrices to a nn dataset with tonndata(). Watch the dimensions of your data, e.g. if it is a vertical vector instead of the shown horizontal vector. If you are not familiar with the training commands, type in nnstart and let the tool help you.
However, if you want to use a nn as mathematical model for a dynamic system (engineering background, machinery, some electrical filter), then you must use the dynamic ones from the nnstart toolbox "dynamic time series". Since this kind of nn reacts to past events, multiple datasets for training cannot be simply put together - it would look like your physical system would "jump" every time a new dataset it coming. Instead, for dynamic nn you must use catsample to make the training algorithm acknowledge that there are multiple, separated datasets. You can see the effect of catsample in the following screenshot:

1 个评论
显示 -1更早的评论 隐藏 -1更早的评论

Ghazi Binarandi
Ghazi Binarandi 2016-2-12
I am intending to train a multiple time series using a feedforward neural network. I couldn't put them together into a single big matrix because it is consisted of several separate sequences.

请先登录，再进行评论。

类别

在 帮助中心 和 File Exchange 中查找有关 Sequence and Numeric Feature Data Workflows 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by