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Schrödinger equation is a well known example of the so-called complex partial differential equations (C-PDE). This paper presents a technique based in the Differential Neural Networks (DNN) methodology to solve the nonparametric identification problem of systems described by C.PDE. In this case, the identification scheme is proposed as the composition of two coupled DNN: the first one is used to...
In this paper a strategy based on differential neural networks for the identification of the parameters in a mathematical model described by partial differential equations is proposed. The identification problem is reduced to finding an exact expression for the weights dynamics using the differential neural networks properties. The adaptive laws for weights ensure the convergence of the neural network...
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