Examinando por Autor "Baldera-Moreno, Y"
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Ítem Estimation of the optimal number of neurons in extreme learning machine using simulated annealing and the golden section(IOP Publishing, 2023) Gelvez-Almeida, E; Mora, M; Huérfano-Maldonado, Y; Salazar-Jurado, E; Martínez-Jeraldo, N; Lozada-Yavina, R; Baldera-Moreno, Y; Tobar, LExtreme learning machine is a neural network algorithm widely accepted in the scientific community due to the simplicity of the model and its good results in classification and regression problems; digital image processing, medical diagnosis, and signal recognition are some applications in the field of physics addressed with these neural networks. The algorithm must be executed with an adequate number of neurons in the hidden layer to obtain good results. Identifying the appropriate number of neurons in the hidden layer is an open problem in the extreme learning machine field. The search process has a high computational cost if carried out sequentially, given the complexity of the calculations as the number of neurons increases. In this work, we use the search of the golden section and simulated annealing as heuristic methods to calculate the appropriate number of neurons in the hidden layer of an Extreme Learning Machine; for the experiments, three real databases were used for the classification problem and a synthetic database for the regression problem. The results show that the search for the appropriate number of neurons is accelerated up to 4.5× times with simulated annealing and up to 95.7× times with the golden section search compared to a sequential method in the highest-dimensional database.Ítem Parallel methods for linear systems solution in extreme learning machines: an overview(2020) Gelvez-Almeida, E; Baldera-Moreno, Y; Huérfano, Y; Vera, M; Mora, M; Barrientos, RThis paper aims to present an updated review of parallel algorithms for solving square and rectangular single and double precision matrix linear systems using multi-core central processing units and graphic processing units. A brief description of the methods for the solution of linear systems based on operations, factorization and iterations was made. The methodology implemented, in this article, is a documentary and it was based on the review of about 17 papers reported in the literature during the last five years (2016-2020). The disclosed findings demonstrate the potential of parallelism to significantly decrease extreme learning machines training times for problems with large amounts of data given the calculation of the Moore Penrose pseudo inverse. The implementation of parallel algorithms in the calculation of the pseudo-inverse will allow to contribute significantly in the applications of diversifying areas, since it can accelerate the training time of the extreme learning machines with optimal results.