Vásquez, AMora, MSalazar, EGelvez, E2020-08-272020-08-27202017426588https://hdl.handle.net/20.500.12442/6380The extreme learning machine for neural networks of feedforward of a single hidden layer randomly assigns the weights of entry and analytically determines the weights the output by means the Moore-Penrose inverse, this algorithm tends to provide an extremely fast learning speed preserving the adjustment levels achieved by classifiers such as multilayer perception and support vector machine. However, the Moore-Penrose inverse loses precision when using data with additive noise in training. That is why in this paper a method to robustness of extreme learning machine to additive noise proposed. The method consists in computing the weights of the output layer using non-linear optimization algorithms without restrictions. Tests are performed with the gradient descent optimization algorithm and with the Levenberg-Marquardt algorithm. From the implementation it is observed that through the use of these algorithms, smaller errors are achieved than those obtained with the Moore-Penrose inverse.pdfengAttribution-NonCommercial-NoDerivatives 4.0 InternacionalOptimization algorithmMoore-PenroseLearninginfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://iopscience.iop.org/article/10.1088/1742-6596/1514/1/012006/pdf