| LTPDA Toolbox™ | contents | ![]() |
TDFIT fit a MATRIX of transfer function SMODELs to a matrix of input and output signals. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 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: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
| Method Details | |
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| Access | public |
| Defining Class | matrix |
| Sealed | 0 |
| Static | 0 |
| Sets for this method … |
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| Default |
Default |
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| no description | |||
| Key | Default Value | Options | Description |
| 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' |
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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 |
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Use symbolic derivatives or not. Only for gradient-based algorithm or for LinUnc option. |
| DIFFORDER | 1 |
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Symbolic derivative order. Only for SymDiff option. |
| FITUNC | 1 |
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Fit parameter uncertainties or not. |
| UNCMTD | 'hessian' |
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Choose the uncertainties estimation method. For multi-channel fitting hessian is mandatory. |
| LINUNC | 1 |
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Force linear symbolic uncertainties. |
| GRADSEARCH | 0 |
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Do a preliminary gradient-based search using the BFGS Quasi-Newton method. |
| MONTECARLO | 0 |
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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. |
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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 | 8ab8cbbc4bccf7543491a24448f4aae0b1be1c43 |
| Min input args | 1 |
| Max input args | -1 |
| Min output args | 1 |
| Max output args | -1 |
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Method: matrix/sqrt | Method: matrix/tfe | ![]() |
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