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Prediction of stochastic fields by RBFNNGrabec, Igor ; Mandelj, SimonǂA ǂstatistical description of stochastic phenomena is utilized to formulate a general modeler of physical laws having the structure of a radial basis function neural network. As a basis for the ... description of a phenomenon the concept of an auto-regressive field is utilized. Its evolution is represented by a non-linear mapping relation in which the generating function is modeled empirically by a non-parametric statistical estimator. The estimator represents a radial basis function neural network which learns from a set of empirical records of field transitions to predict the field outside some initially given domain. The performance of the generator is demonstrated by its prediction of a chaotic time series and examples of surfaces.Source: WCCI 98 [Elektronski vir] : proceedings of IJCNN '98, FUZZ-IEEE '98, ICEC '98 (J0358.pdf (str. 1960-1965))Type of material - conference contributionPublish date - 1998Language - englishCOBISS.SI-ID - 3963163
Author
Grabec, Igor |
Mandelj, Simon
Topics
mreže nevronalne |
procesi stohastični |
statistika neparametrična |
modeliranje |
kaos |
serije časovne
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source: WCCI 98 [Elektronski vir] : proceedings of IJCNN '98, FUZZ-IEEE '98, ICEC '98 (J0358.pdf (str. 1960-1965))
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Database name | Field | Year |
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Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
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Grabec, Igor | 00800 |
Mandelj, Simon | 18294 |
Source: Personal bibliographies
and: SICRIS
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