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[29] discussed the adaptive synchronization in an array of linearly coupled NNs with reaction-diffusion terms and time delays.
A control problem is said to be stochastic when it is subjected to some disturbances or noise terms and time dependent, that is being uncertain of its future state.
Owing to the interaction terms and time dependency specified in the model, it is difficult to interpret coefficients directly, in particular the assessment of statistical significance of overall probabilities.
To find the best estimate for baseline mortality rate in the absence of influenza activity, we applied regression methods, using harmonic terms and time trends, to mortality rate data (6, 16, 17 ) (Technical Appendix).
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In this paper, we consider a class of nonlinear stochastic systems with respect to neutral terms and time-varying delays.
In this paper, we will consider a class of nonlinear stochastic systems with respect to neutral terms and time-varying delays.
In this paper, the problem of asymptotic synchronization for a class of neural networks with reaction-diffusion terms and time-varying delays is investigated.
In Section 3, the robustness of global exponential stability of nonlinear stochastic systems with respect to neutral terms and time-varying delays is analyzed and a corollary for our theorem is derived.
These models utilise information from both censored and non-censored observations [ 18], and have been extended to incorporate frailty terms and time-varying predictors [ 19], making it possible to study effects of, for example, infections which are absent at the start of a study and occurring at some point during the study observation time.
In this paper we alternate between the terms "delay" and "time interval".
Both short-term and time-dependent hinge curls were evaluated.
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