This work investigates the dissipative dynamical system in the infinite lattice ? with cellular neural networks as an example of application. The dynamics of each node depends on itself and nearby nodes by a nonlinear function. When each node is perturbed with weighted Gaussian white noise, there exists a unique pullback attractor and forward attractor whose domain of attraction are random tempered sets. Furthermore, we prove that the pullback and forward attractor are equivalent to a random equilibrium which is also tempered. Both convergence to the pullback and forward attractors are exponentially fast. Index terms: disspativive cellular neural networks, random attractor, stochastic equilibrium. ? 2010 IEEE.
Relation:
2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010 2010, Article number5430258
