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The asymptotical mean-square stability analysis problem is considered for a class of Cohen-Grossberg neural networks CGNNs with random delay. The evolution of the delay is modeled by a continuous-time homogeneous Markov process with a finite number of states. The main purpose of this paper is to establish easily verifiable conditions under which the random delayed Cohen-Grossberg neural network is asymptotical mean-square stability. By employing Lyapunov-Krasovskii functionals and conductingdoi:10.1155/2010/247587 fatcat:uxnr4oht2jfb3pzmuasklanbie