Sentence examples for more complicated dynamical from inspiring English sources

If those two inequalities do not hold, then model (1) may exhibit much more complicated dynamical behaviors.

Finally, we easily see that model (1) can exhibit more complicated dynamical behaviors from the following two examples.

They always exhibit richer and more complicated dynamical behaviors than continuous-time models, though some of them may be considered as approximations to the continuous-time models.

However, since a discrete model generally can exhibit more complicated dynamical behavior than continuous models such as bifurcations and chaos (see [25, 28] and references therein).

For special values of the belt parameters these sum type and difference type of internal resonances coincide giving rise to even more complicated dynamical behaviour.

The results on the global stability of the endemic equilibrium are quite few for discrete epidemic models due to the following two factors: (1) There is not enough effective theory and methods for global stability of discrete dynamical systems; (2) The discrete model exhibits much more complicated dynamical behaviors than its corresponding continuous model.

Since we consider a three-species-food-chain model, the dynamical behaviors are more complicated and the system has more equilibria than those in [4, 10, 12].

Incorporating two delays in system (2), the dynamical behaviors are more complicated than the system with one delay (for example, see [8, 10, 14, 24]).

On the other hands, we have considered more complicated problem, namely, synchronization of complex dynamical network with hybrid coupling delay and mixed time-varying delay (interval time-varying delay and distributed time-varying delay), which time-varying delay using both state-delayed feedback control as well as intermittent state-delayed feedback control.

From this, we are able to identify four basic types of dynamical mechanism and behaviour transitions, pursuits and two types of captures of trajectories that can be used to classify and understand dynamical behaviour in more complicated and realistic models, such as a full non-autonomous version of the gap gene model.

The discrete model also exhibits a rich dynamical behavior, though it is more complicated than the corresponding continuous model.