Nonlinear System Identification with Composite Relevance Vector Machines

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2007-01-01

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Camps Valls, Gustavo
Martínez Ramón, Manel
Rojo-Álvarez, José Luis
Muñoz Marí, Jordi

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Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output information in the mapping function efficiently and hence generalize the standard approach. An improved trade-off between accuracy and sparsity is obtained in several benchmark problems. Also, the RVM yields confidence intervals for the predictions, and it is less sensitive to free parameter selection.

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