Plspulsm: Difference between revisions
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===Purpose=== | ===Purpose=== | ||
Builds finite impulse response (FIR) models for multi-input single (MISO) output systems using partial least squares regression. | Builds finite impulse response (FIR) models for multi-input single (MISO) output 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 are a matrix of process input vectors u, and a process output vector y. n is a row vector with the number of FIR coefficents to use for each input, maxlv is the maximum number of latent variables to consider, split is the number of times the model is rebuilt and tested during cross-validation, and delay is a row vector containing the number of time units of delay for each input. | plspulsm calculates a vector of FIR coefficients b using PLS regression. Inputs are a matrix of process input vectors u, and a process output vector y. n is a row vector with the number of FIR coefficents to use for each input, maxlv is the maximum number of latent variables to consider, split is the number of times the model is rebuilt and tested during cross-validation, and delay is a row vector containing the number of time units of delay for each input. | ||
Note: plspulsm uses contiguous blocks of data for cross-validation. | Note: plspulsm uses contiguous blocks of data for cross-validation. | ||
===Examples=== | ===Examples=== | ||
b = plspulsm([u1 u2],y,[25 15],5,10,[0 3]) | b = plspulsm([u1 u2],y,[25 15],5,10,[0 3]) | ||
This 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 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. | This 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 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]] | ||
Revision as of 15:26, 3 September 2008
Purpose
Builds finite impulse response (FIR) models for multi-input single (MISO) output 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. Inputs are a matrix of process input vectors u, and a process output vector y. n is a row vector with the number of FIR coefficents to use for each input, maxlv is the maximum number of latent variables to consider, split is the number of times the model is rebuilt and tested during cross-validation, and delay is a row vector containing the number of time units of delay for each input.
Note: plspulsm uses contiguous blocks of data for cross-validation.
Examples
b = plspulsm([u1 u2],y,[25 15],5,10,[0 3])
This 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 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.