A contraction-theory separation principle yields global exponential stability for controller-observer pairs and sharp LMI certificates for contractive RNNs, enabling stable output tracking and implicit neural network design.
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A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
A contraction-theory separation principle yields global exponential stability for controller-observer pairs and sharp LMI certificates for contractive RNNs, enabling stable output tracking and implicit neural network design.