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Nesterov-Accelerated Concurrent Learning for Lyapunov-Based Deep Neural Networks
Concurrent learning (CL) has recently been extended to Lyapunov-based deep neural networks (DNNs) to obtain parameter convergence under finite rather than persistent excitation. However, the existing first-order law converges slowly, especially for high-dimensional systems and overparameterized netw
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