Discrete-Time Fractional-Order Dynamical Networks Minimum-Energy State Estimation

Fractional-order dynamical networks are increasingly being used to model and describe processes demonstrating long-term memory or complex interlaced dependencies among the spatial temporal components of a wide variety networks. Notable examples include networked control systems neurophysiological which created using electroencephalographic (EEG) blood-oxygen-level-dependent data. As result, estimation states fractional-order poses an important problem. To this effect, article addresses problem minimum-energy state for discrete-time networks, where output equations affected by additive noise that is considered be deterministic, bounded, unknown. Specifically, we derive corresponding estimator show resulting error exponentially input-to-state stable with respect disturbances signal decreasing increase accuracy adopted approximation model. An illustrative example shows effectiveness proposed method on real-world Our results may significantly contribute development novel neurotechnologies, particularly in paradigms neural signals such as EEG, often noisy known artifacts not having any particular stochastic characterization.