Conditioning of extreme learning machine for noisy data using heuristic optimization

Salazar, E.; Mora, Marco; Vásquez, A.; Gelvez, E.

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dc.contributor.author	Salazar, E.
dc.contributor.author	Mora, Marco
dc.contributor.author	Vásquez, A.
dc.contributor.author	Gelvez, E.
dc.date.accessioned	2020-12-29T12:35:06Z
dc.date.available	2020-12-29T12:35:06Z
dc.date.issued	2020
dc.identifier.uri	http://repositorio.ucm.cl/handle/ucm/3387
dc.description.abstract	This article provides a tool that can be used in the exact sciences to obtain good approximations to reality when noisy data is inevitable. Two heuristic optimization algorithms are implemented: Simulated Annealing and Particle Swarming for the determination of the extreme learning machine output weights. The first operates in a large search space and at each iteration it probabilistically decides between staying at its current state or moving to another. The swarm of particles, it optimizes a problem from a population of candidate solutions, moving them throughout the search space according to position and speed. The methodology consists of building data sets around a polynomial function, implementing the heuristic algorithms and comparing the errors with the traditional computation method using the Moore–Penrose inverse. The results show that the heuristic optimization algorithms implemented improve the estimation of the output weights when the input have highly noisy data.	es_CL
dc.language.iso	en	es_CL
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 Chile	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/cl/	*
dc.source	Journal of Physics: Conference Series, 1514, 012007	es_CL
dc.title	Conditioning of extreme learning machine for noisy data using heuristic optimization	es_CL
dc.type	Article	es_CL
dc.ucm.facultad	Facultad de Ciencias de la Ingeniería	es_CL
dc.ucm.indexacion	Scopus	es_CL
dc.ucm.doi	doi.org/10.1088/1742-6596/1514/1/012007	es_CL