Observer Design for Fractional-Order Chaotic Neural Networks With Unknown Parameters
Suxia Wang, Xiulan Zhang · IEEE Access · 2020
Unlike the observer design for a conventional system, designing observer for a fractional-order one is a challenging work due to the different operational properties between the traditional calculus and the fractional calculus. In this paper, two observers for fractional-order neural networks (FONNs) with and without parametric uncertainties are designed, respectively. By using fractional stability criteria, it is shown that the observe errors converge to an arbitrary small region eventually. By using a new sliding term in the synchronization controller desgin, the proposed observers have good robustness. Simulation studies are given to verify the theoretical derivation at last.