LTPDA Toolbox™ contents Method: matrix/subsData   Method: matrix/tfe

Method matrix/tdfit

  TDFIT fit a MATRIX of transfer function SMODELs to a matrix of input and output signals.
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  DESCRIPTION: TDFIT fits a MATRIX of transfer function SMODELs to a set of 
               input and output signals. It uses ao\tdfit as the core algorithm.
  
 
  CALL:        b = tdfit(outputs, pl)
 
  INPUTS:      outputs  - an array of MATRIXs representing the outputs of a system, 
                          one per each experiment.
               pl       - parameter list (see below)
 
  OUTPUTs:     b  - a pest object containing the best-fit parameters,
                    goodness-of-fit reduced chi-squared, fit degree-of-freedom
                    covariance matrix and uncertainties. Additional
                    quantities, like the Information Matrix, are contained 
                    within the procinfo. The best-fit model can be evaluated
                    from pest\eval.
 
  Parameters Description
 
  EXAMPLES:
 
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Method Details
Access public
Defining Class matrix
Sealed 0
Static 0

Parameter Description

Sets for this method …
Default

Default
no description
Key Default Value Options Description
tdfit
INPUTS [] none A COLLECTION of MATRIXs, one per each experiment, containing the input A0s.
MODELS [] none A MATRIX of transfer function SMODELs.
PADRATIO 1 none PadRatio is defined as the ratio between the number of zero-pad points and the data length.
Define how much to zero-pad data after the signal.
Being tdfit a fft-based algorithm, no zero-padding might bias the estimation, therefore it is strongly suggested to do that.
WHFLTS [] none A MATRIX of FILTERBANKs containing the whitening filters per each output AO.
PNAMES {} [0x0] none A cell-array of parameter names to fit.
P0 [] none An array of starting guesses for the parameters.
LB [] none Lower bounds for the parameters.
This improves convergency. Mandatory for Monte Carlo.
UB [] none Upper bounds for the parameters.
This improves the convergency. Mandatory for Monte Carlo.
ALGORITHM 'fminsearch'
  • 'fminsearch'
  • 'fminunc'
  • 'fmincon'
  • 'patternsearch'
  • 'ga'
  • 'simulannealbnd'
 A string defining the fitting algorithm.
fminunc, fmincon require 'Optimization Toolbox' to be installed.
patternsearch, ga, simulannealbnd require 'Genetic Algorithm and Direct Search' to be installed.
OPTSET '' none An optimisation structure to pass to the fitting algorithm.
See fminsearch, fminunc, fmincon, optimset, for details.
See patternsearch, psoptimset, for details.
See ga, gaoptimset, for details.
See simulannealbnd, saoptimset, for details.
SYMDIFF 0
  • 0
  • 1
 Use symbolic derivatives or not. Only for gradient-based algorithm or for LinUnc option.
DIFFORDER 1
  • 1
  • 2
 Symbolic derivative order. Only for SymDiff option.
FITUNC 1
  • 1
  • 0
 Fit parameter uncertainties or not.
UNCMTD 'hessian'
  • 'hessian'
  • 'jacobian'
 Choose the uncertainties estimation method.
For multi-channel fitting hessian is mandatory.
LINUNC 1
  • 1
  • 0
 Force linear symbolic uncertainties.
GRADSEARCH 0
  • 0
  • 1
 Do a preliminary gradient-based search using the BFGS Quasi-Newton method.
MONTECARLO 0
  • 0
  • 1
 Do a Monte Carlo search in the parameter space.
Useful when dealing with high multiplicity of local minima. May be computer-expensive.
Note that, if used, P0 will be ignored. It also requires to define LB and UB.
NPOINTS 100000 none Set the number of points in the parameter space to be extracted.
NOPTIMS 10 none Set the number of optimizations to be performed after the Monte Carlo.

Example
plist('INPUTS', [[]], 'MODELS', [[]], 'PADRATIO', [1], 'WHFLTS', [[]], 'PNAMES', cell(0,0), 'P0', [[]], 'LB', [[]], 'UB', [[]], 'ALGORITHM', 'fminsearch', 'OPTSET', '', 'SYMDIFF', [false], 'DIFFORDER', [1], 'FITUNC', [true], 'UNCMTD', 'hessian', 'LINUNC', [true], 'GRADSEARCH', [false], 'MONTECARLO', [false], 'NPOINTS', [100000], 'NOPTIMS', [10])

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Some information of the method matrix/tdfit are listed below:
Class name matrix
Method name tdfit
Category Signal Processing
Package name ltpda
VCS Version 967b0eec0dece803a81af8ef54ad2f8c784b20b2
Min input args 1
Max input args -1
Min output args 1
Max output args -1
Can be used as modifier 0
Supported numeric types {'double'}



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