Global exponential stability and lag synchronization for delayed memristive fuzzy Cohen-Grossberg BAM neural networks with impulses.
This paper investigates the stability and lag synchronization for memristor-based fuzzy Cohen-Grossberg bidirectional associative memory (BAM) neural networks with mixed delays (asynchronous time delays and continuously distributed delays) and impulses. By applying the inequality analysis technique, homeomorphism theory and some suitable Lyapunov-Krasovskii functionals, some new sufficient conditions for the uniqueness and global exponential stability of equilibrium point are established. Furthermore, we obtain several sufficient criteria concerning globally exponential lag synchronization for the proposed system based on the framework of Filippov solution, differential inclusion theory and control theory. In addition, some examples with numerical simulations are given to illustrate the feasibility and validity of obtained results.
['Yang|Wengui|W|', 'Yu|Wenwu|W|', 'Cao|Jinde|J|', 'Alsaadi|Fuad E|FE|', 'Hayat|Tasawar|T|']
D000465:Algorithms D017143:Fuzzy Logic D016571:Neural Networks, Computer D013997:Time Factors
Global exponential stability Impulses Globally exponential lag synchronization Cohen–Grossberg BAM neural networks Fuzzy cellular neural networks Memristive neural networks