Wrtpulse: Difference between revisions

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===Purpose===
===Purpose===


Creates input and output matrices for finite impulse response (FIR) dynamic model identification and prediction.
Create input/output matrices for dynamic model identification.


===Synopsis===
===Synopsis===


:[newu,newy] = wrtpulse(u,y,n,delay)
:[newu,newy,settings] = wrtpulse(u,y,n,delay);
:[newu,newy] = wrtpulse(u,y,settings);
:[newu,newy,settings] = wrtpulse;      %interactive mode
:[newu,newy] = wrtpulse(settings);      %interactive mode w/default settings
:wrtpulse demo


===Description===
===Description===


WRTPULSE is used to write time series data with muliple inputs and a single output into a form to obtain finite impulse response (FIR) and ARX models. Inputs are a matrix of input vectors u, and an output vector y. n is a row vector with the number of coefficents to use for each input, and delay is a row vector containing the number of time units of delay for each input. The output is a matrix of lagged input variables newu and the corresponding output vector newy.
This function rewrites vectors of system inputs and output so that they may be used with PLS and other modeling routines to obtain finite impulse response (FIR) and ARX models. If the inputs (u and y) are DataSet objects then so are the outputs (newu and newy).
 
If no inputs are provided or an empty data matrix is provided, WRTPULSE will provide step-by-step loading of each input and the results will be written to the workspace. These results will include a model structure (settings) that can be used to reproduce the same decomposition on new data if passed in as (u) or (n).
 
====Inputs====
* '''u''' = a M by N matrix of input vectors where the each input is a column vector.
* '''y''' = the corresponding M by 1 output vector to be predicted by the model.
* '''n''' = a 1 by N row vector with the number of coefficents to use for each input (each element of '''n''' is the number of past periods to consider for each input).
* '''delay''' =  a 1 by N row vector containing the number of time units of delay for each input.
 
====Outputs====
 
* '''newu''' = a matrix of lagged input variables.
* '''newy''' = the corresponding output vector.
* '''settings''' = a model structure that can be used to reproduce the same decomposition on new data if passed in as (u) or (n).


===See Also===
===See Also===


[[autocor]], [[crosscor]], [[fir2ss]], [[plspulsm]]
[[autocor]], [[crosscor]], [[fir2ss]], [[plspulsm]]

Latest revision as of 10:56, 13 November 2014

Purpose

Create input/output matrices for dynamic model identification.

Synopsis

[newu,newy,settings] = wrtpulse(u,y,n,delay);
[newu,newy] = wrtpulse(u,y,settings);
[newu,newy,settings] = wrtpulse; %interactive mode
[newu,newy] = wrtpulse(settings); %interactive mode w/default settings
wrtpulse demo

Description

This function rewrites vectors of system inputs and output so that they may be used with PLS and other modeling routines to obtain finite impulse response (FIR) and ARX models. If the inputs (u and y) are DataSet objects then so are the outputs (newu and newy).

If no inputs are provided or an empty data matrix is provided, WRTPULSE will provide step-by-step loading of each input and the results will be written to the workspace. These results will include a model structure (settings) that can be used to reproduce the same decomposition on new data if passed in as (u) or (n).

Inputs

  • u = a M by N matrix of input vectors where the each input is a column vector.
  • y = the corresponding M by 1 output vector to be predicted by the model.
  • n = a 1 by N row vector with the number of coefficents to use for each input (each element of n is the number of past periods to consider for each input).
  • delay = a 1 by N row vector containing the number of time units of delay for each input.

Outputs

  • newu = a matrix of lagged input variables.
  • newy = the corresponding output vector.
  • settings = a model structure that can be used to reproduce the same decomposition on new data if passed in as (u) or (n).

See Also

autocor, crosscor, fir2ss, plspulsm