Plspulsm: Difference between revisions
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===Purpose=== | ===Purpose=== | ||
Builds finite impulse response (FIR) models for multi-input single (MISO) | |||
Builds finite impulse response (FIR) models for multi-input single output (MISO) systems using partial least squares regression. | |||
===Synopsis=== | ===Synopsis=== | ||
:b = plspulsm(u,y,n,maxlv,split,delay) | :b = plspulsm(u,y,n,maxlv,split,delay) | ||
===Description=== | ===Description=== | ||
plspulsm calculates a vector of FIR coefficients b using PLS regression. Inputs | |||
plspulsm calculates a vector of FIR coefficients <tt>b</tt> using PLS regression. | |||
'''Note''': '''plspulsm''' uses contiguous blocks of data for cross-validation. | |||
====Inputs==== | |||
* '''u''' = matrix of process input vectors | |||
* '''y''' = process output vector | |||
* '''n''' = a row vector with the number of FIR coefficients to use for each input | |||
* '''maxlv''' = maximum number of latent variables to consider | |||
* '''split''' = number of times the model is rebuilt and tested during cross-validation | |||
* '''delay''' = row vector containing the number of time units of delay for each input | |||
====Outputs==== | |||
* '''b''' = vector of FIR coefficients | |||
===Examples=== | ===Examples=== | ||
b = plspulsm([u1 u2],y,[25 15],5,10,[0 3]) | |||
:b = plspulsm([u1 u2],y,[25 15],5,10,[0 3]) | |||
In this example, the system has 2 inputs as column vectors u1 and u2 and a single output vector y. The FIR model will use 25 coefficients for input variable u1 and 15 coefficients for input variable u2. For this model a maximum of 5 latent variables will be considered. The cross validation split the data into 10 block-wise subsets. The number of time units of delay for the first input variable u1 is 0 and for the second input variable u2 it is 3. | |||
===See Also=== | ===See Also=== | ||
[[autocor]], [[crosscor]], [[fir2ss]], [[wrtpulse]] | [[autocor]], [[crosscor]], [[fir2ss]], [[wrtpulse]] | ||
Latest revision as of 13:42, 10 October 2008
Purpose
Builds finite impulse response (FIR) models for multi-input single output (MISO) systems using partial least squares regression.
Synopsis
- b = plspulsm(u,y,n,maxlv,split,delay)
Description
plspulsm calculates a vector of FIR coefficients b using PLS regression.
Note: plspulsm uses contiguous blocks of data for cross-validation.
Inputs
- u = matrix of process input vectors
- y = process output vector
- n = a row vector with the number of FIR coefficients to use for each input
- maxlv = maximum number of latent variables to consider
- split = number of times the model is rebuilt and tested during cross-validation
- delay = row vector containing the number of time units of delay for each input
Outputs
- b = vector of FIR coefficients
Examples
- b = plspulsm([u1 u2],y,[25 15],5,10,[0 3])
In this example, the system has 2 inputs as column vectors u1 and u2 and a single output vector y. The FIR model will use 25 coefficients for input variable u1 and 15 coefficients for input variable u2. For this model a maximum of 5 latent variables will be considered. The cross validation split the data into 10 block-wise subsets. The number of time units of delay for the first input variable u1 is 0 and for the second input variable u2 it is 3.