Abstract
Data-driven learning of fractional difference equations is investigated in this paper. Firstly, a multi-layer neural network is designed. Loss functions are constructed by use of the observed data and fractional difference equations. Finally, the Adam algorithm is employed to solve optimal problems. One and two-dimensional examples are demonstrated to show the neural network method’s efficiency in parameter estimations of stable, periodic and chaotic states.
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