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To address the high-dimensionality of big data, numerous iterative algorithms have been introduced including least absolute shrinkage selection operator (Lasso) and iteratively sure independent screening (ISIS). However, the iterative nature of these algorithms renders the computational cost of retraining the learning model impractical. We take advantage of this key observation to propose a novel...
In this paper, an identification scheme via extreme learning machine neural network is proposed. The proposed identification scheme ensures the convergence of the residual state error to zero and boundedness of all associated approximation errors, even in the presence of approximation error and disturbances. Lyapunov-like analysis using Barbalat's Lemma and a dynamic single-hidden layer neural network...
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