Passivity and Passification of Fuzzy Memristive Inertial Neural Networks on Time Scales Academic Article uri icon

abstract

  • © 1993-2012 IEEE. A class of Takagi-Sugeno (T-S) fuzzy memristor-based inertial neural networks (FMINNs) is studied on time scales. The second-order derivative of the state variable in the network denotes the inertial term. At first, one timescale-Type FMINNs is formulated on the basis of T-S fuzzy rules. By a variable transformation, the original network is transformed into first-order differential equations. Then, passivity criteria for the FMINNs are presented based on the characteristic function approach, linear matrix inequality techniques, and the calculus of time scales. Furthermore, two classes of control protocols, i.e., memristor-and fuzzy-related control protocols are designed to solve the passification problem for the considered FMINNs. The optimization problem of the passivity performance is also involved. Finally, simulation examples are given to show the effectiveness and validity of the obtained results, and an application is also given in pseudorandom number generation.

author list (cited authors)

  • Xiao, Q., Huang, T., & Zeng, Z.

citation count

  • 29

publication date

  • November 2018