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• W2913946880 abstract "The goal of this paper is to investigate the theoretical properties, the training algorithm, and the predictive control applications of Echo State Networks (ESNs), a particular kind of Recurrent Neural Networks. First, a condition guaranteeing incremetal global asymptotic stability is devised. Then, a modified training algorithm allowing for dimensionality reduction of ESNs is presented. Eventually, a model predictive controller is designed to solve the tracking problem, relying on ESNs as the model of the system. Numerical results concerning the predictive control of a nonlinear process for pH neutralization confirm the effectiveness of the proposed algorithms for the identification, dimensionality reduction, and the control design for ESNs." @default.
• W2913946880 created "2019-02-21" @default.
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• W2913946880 date "2019-02-05" @default.
• W2913946880 modified "2023-09-27" @default.
• W2913946880 title "Echo State Networks: analysis, training and predictive control" @default.
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• W2913946880 hasPublicationYear "2019" @default.
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