Search for A Lyapunov Function through Empirical Approximation by Artificial Neural Nets: Theoretical Framework
نویسنده
چکیده
An artificial neural network is proposed as a function approximator for empirical modeling of a Lyapunov function for a nonlinear dynamic system that projects stable behavior as potentially observable in its state space. Theoretical framework for the methodology of designing the so-called Lyapunov neural network, which empirically models a Lyapunov function, is described. Algorithms for training the Lyapunov neural network for a neurodynamics system are presented.
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