Body: <p>My Problem consists of Input sequences in the form of <span class="math-container">$x=[B,z]$</span> and one output <span class="math-container">$y_i$</span> for each data point <span class="math-container">$x_i=[B,z_i]$</span>. For one sequence/dataset <span class="math-container">$B$</span> is a constant, whereas <span class="math-container">$z$</span> is continously between 0 and 5.</p>
  <p>In the moment I just train a standard MLP on this. However, I wonder if I can &quot;help&quot; my neural network because I know, that <span class="math-container">$y_i$</span> is a smooth function of <span class="math-container">$z_i$</span>. This seems analog to a time series problem, where I computate my solution at <span class="math-container">$y_i(t=0)$</span> and use this information for the prediction of <span class="math-container">$y_i(t=1)$</span>.</p>
  <p>Am I right with this assumption? Can I use RNN or a combination of RNN with MLP for this?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-03-14T09:08:57.797Z
Comments.Text: Can you please edit your post to format the symbols and equations with mathjax to improve the readability? Moreover, the question in the title is very general. Please, put your **specific question** in the title.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-03-14T14:35:20.497Z
Comments.Text: Done and changed title
Comments.UserDisplayName: Gesetzt
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-03-15T15:17:24.980Z
Comments.Text: I think the best way is just to try it out. If it is in fact something like a time series RNN/LSTM can be helpful because in many applications (but not all) they yield good results when having sequence data.
Comments.UserDisplayName: PeterBe
Tags: <neural-networks><recurrent-neural-networks><time-series>
Title: MLP or RNN for Regression of Smooth Function (No Time Data)?
UserDisplayName: Gesetzt
ThreadText: # MLP or RNN for Regression of Smooth Function (No Time Data)?
  
  (Asked by: username_0 on 2022-03-13)
  
  My Problem consists of Input sequences in the form of $x=[B,z]$ and one output $y\_i$ for each data point $x\_i=[B,z\_i]$. For one sequence/dataset $B$ is a constant, whereas $z$ is continously between 0 and 5.
  
  In the moment I just train a standard MLP on this. However, I wonder if I can "help" my neural network because I know, that $y\_i$ is a smooth function of $z\_i$. This seems analog to a time series problem, where I computate my solution at $y\_i(t=0)$ and use this information for the prediction of $y\_i(t=1)$.
  
  Am I right with this assumption? Can I use RNN or a combination of RNN with MLP for this?
  
  **Tags:** neural-networks, recurrent-neural-networks, time-series
  
  > **Comments:**
  > **username_1**: Can you please edit your post to format the symbols and equations with mathjax to improve the readability? Moreover, the question in the title is very general. Please, put your **specific question** in the title.  
  > **username_0**: Done and changed title  
  > **username_2**: I think the best way is just to try it out. If it is in fact something like a time series RNN/LSTM can be helpful because in many applications (but not all) they yield good results when having sequence data.  
  
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  ## 0 answers
  
  