predictive state recurrent neural networks

In this state space model, the stochastic system model is represented by the stochastic Equations (4) and (5) and the probability distributions given in Section (2.3); the

In this paper we have investigated the stochastic stability analysis problem for a class of neural networks with both Markovian jump parameters and continuously distributed delays..

In section 3, we state some results on the large time behavior of subsonic solutions (for classical solutions in section 3.1 and for subsonic shock solutions in

By employing the theory of topological degree, M -matrix and Lypunov functional, We have obtained some sufficient con- ditions ensuring the existence, uniqueness and global

After this Introduction, in Section 2 we introduce some necessary notation, recall some basic facts about convex and concave functions and state, prove and discuss our main result

In Section 7, we state and prove various local and global estimates for the second basic problem.. In Section 8, we prove the trace estimate for the second

Li, “Simplified exponential stability analysis for recurrent neural networks with discrete and distributed time-varying delays,” Applied Mathematics and Computation, vol..

The generalized projective synchronization GPS between two different neural networks with nonlinear coupling and mixed time delays is considered.. Several kinds of nonlinear