Abstract
Discrete fractional calculus is suggested to describe neural networks with memory effects. Fractional discrete-time recurrent neural network is proposed on an isolated time scale. Stability results are investigated via Banach fixed point technique. The attractive solution space is constructed and stability conditions are provided. Furthermore, short memory and variable-order fractional neural networks are given according to the stability conditions. Two and three dimensional numerical examples are used to demonstrate the theoretical results.
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