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Control of Generalization with a Bi-Objective Sliding Mode Control Algorithm

Marcelo Azevedo CostaAntônio de Pádua BragaBenjamin Rodrigues de MenezesGustavo Guimarães ParmaRoselito de Albuquerque Teixeira

This paper presents a new sliding mode control algorithm that is able to guide the trajectory of a Multi-Layer Perceptron (MLP) within the plane formed by the two objectives: training set error and norm of the weight vectors. The results show that the neural networks obtained are able to generate the Pareto set, from which a model with the smallest validation error is selected.

http://csdl.computer.org/comp/proceedings/sbrn/2002/1709/00/17090038abs.htm

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