Exponential passivity of memristive neural networks with mixed time-varying delays
Abstract This paper is concerned with the exponential passivity for memristive neural networks with mixed time-varying delays. Non-positive definite matrices are applied in an appropriate quadratic Lyapunov functional, a new delay-dependent exponential passivity condition is proposed in terms of linear matrix inequalities (LMIs), such that for all admissible delay bounds. The criterion obtained is only composed of four LMIs, hence compared with the existing results, no excessive calculation is needed to solve the passivity issue. Finally, two examples are listed to illustrate the efficiency and accuracy of the theoretical results.
We model cross-market Bitcoin prices as long-memory processes and study dynamic interdependence in a fractionally cointegrated VAR framework. We find long-memory in both the individual markets and the system of markets depicting non-homogeneous informational inefficiency. Moreover, Bitcoin markets are found to be fractionally cointegrated, where uncertainty negatively impacts this type of cointegration relationship.