Neural network-based synchronization of uncertain chaotic systems with unknown states

Adaptive Control Design for Arneodo Chaotic System with Uncertain Parameters and Input Saturation

“…By setting initial parameters λ i = 0, e 1 ð0Þ = 0, i = 1, 2, 3, combine formulas (23) and 25, we have…”

“…In reference [22], the output feedback adaptive robust controller for uncertain chaotic systems is studied. Considering the unmeasured states and unknown parameters, a novel neural network-based adaptive observer and an adaptive controller have been designed [23]. To handle the disturbance, a sliding mode RBF neural network controller is presented by using the disturbance observer [24].…”

Synchronization Analysis of a Class of Neural Networks with Multiple Time Delays

“…Over the past two decades, many scholars have developed various technologies in this research field. More and more synchronization schemes have been proposed, such as projection synchronization, lag synchronization, complete synchronization, antisynchronization, and robust synchronization (see, e.g., [12][13][14][15][16][17][18] and the references therein). For instance, in [12], the authors discussed a fractional-order 3dimensional neural network and obtained some conditions to ensure the projection synchronization of the new system by using the computer simulations and fractional calculus theory.…”

Adaptive projective synchronization for time-delayed fractional-order neural networks with uncertain parameters and its application in secure communications

“…Since Pecora and Carrol (1990) introduced a method to synchronize two identical chaotic systems with different initial conditions, synchronization has received considerable attention among scientists due to its importance in many applications, such as secure communication, chemical systems, biological systems and human heartbeat regulation. Since then, a variety of synchronization methods have been developed (Shen et al, 2014, 2015; Wang et al, 2013a; Wei et al, 2014c, 2015; Wen et al, 2016a, 2016b), which include adaptive control (Liao and Tsai, 2000), non-linear control (Huang et al, 2004), finite-time synchronization (Wu et al, 2015), sliding mode control (Pourmahmood et al, 2011), neural network-based synchronization (Bagheri et al, 2016) and recurrent hierarchical type-2 fuzzy neural networks-based synchronization (Mohammadzadeh and Ghaemi, 2015). Furthermore, as we know, synchronization exists in various types, such as completer synchronization, lag synchronization, projective synchronization and so on.…”