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dc.contributor.authorSalazar, E
dc.contributor.authorMora, M
dc.contributor.authorVásquez, A
dc.contributor.authorGelvez, E
dc.description.abstractThis 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.eng
dc.publisherIOP Publishingeng
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.sourceJournal of Physics: Conference Serieseng
dc.sourceVol. 1514 No. 1 (2020)
dc.subjectExact scienceseng
dc.subjectOptimization algorithmseng
dc.subjectHeuristic algorithmseng
dc.titleConditioning of extreme learning machine for noisy data using heuristic optimizationeng
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dc.type.spaArtículo científicospa

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