Description Usage Arguments Details Value References See Also Examples
Creates a configuration object for TrainIBHM
function which creates IBHM approximation
models.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ConfigureIBHM( stop.criterion = IterationSC(3),
weighting.function = function(y, w.par){ 0.01+dnorm(y,sd=abs(w.par))},
scal.optim = 'multi.CMAES',
scal.optim.params = list(retries=3, inner=list(maxit=50, stopfitness=-1)),
scal.candidates = c('dot.pr','radial','root.radial'),
activ.optim = 'multi.CMAES',
activ.optim.params = list(retries=3, inner=list(maxit=100, stopfitness=-1)),
activ.candidates = c('tanh','logsig','lin'),
jit=TRUE,
verbose=FALSE,
final.estimation = 'all',
final.estimation.x = NULL,
final.estimation.y = NULL,
final.estimation.maxit = 100
)
|
stop.criterion |
The stop criterion for the model construction process. Possible values include objects created using For simplicity, the default value causes the method to construct a model with a fixed number of components (3), however it's actually best to use |
weighting.function |
Definition of the weighting function used during model construction. This function puts emphasis on local features of the approximated function (see details). |
scal.optim |
The optimization method used to estimate scalarization functions' parameters . Possible values are: The parameter values set the optimization methods as follows:
|
scal.optim.params |
The parameters passed to the optimization method used to estimate scalarization functions' parameters.
In case of In case of multistart versions of optimization methods this parameter is a |
scal.candidates |
Candidate scalarization functions (see details). |
activ.optim |
The optimization method used to estimate activation functions' parameters - see description of |
activ.optim.params |
The parameters passed to the optimization method used to estimate activation functions' parameters - see description of the |
activ.candidates |
Candidate activation functions (see details). |
jit |
Enables the just-in-time compilation feature provided by the |
verbose |
Enables verbose output (disabled by default). |
final.estimation |
The type of final parameter estimation step. Possible values are: |
final.estimation.x |
The |
final.estimation.y |
The |
final.estimation.maxit |
The number of iterations (of the optimizer) during final parameter estimation. |
The model constructed by IBHM has the following form:
f(x) = w_0 + ∑ w_i g(a_i h(x,d_i)+b_i) ,
where h:R^n->R is a scalarization function, g:R->R is an activation function, d_i is a parameter vector, and a_i, b_i, w_i are scalar parameters.
The parameter estimation is based on optimizing weighted correlation measures between the model output and the approximation residual. This allows for an iterative model construction process which estimates both model structure and parameter values. For more details see [Zawistowski and Arabas].
A configuration object for TrainIBHM
.
Zawistowski, P. and Arabas, J.: "Benchmarking IBHM method using NN3 competition dataset." In Proc. 6th int. conf. on Hybrid artificial intelligent systems - Vol. 1, HAIS'11, pp 263–270, 2011. Springer-Verlag.
TrainIBHM
, ValidationSC
,IterationSC
1 2 3 4 5 6 7 8 9 10 11 12 |
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