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In this paper, the problem of adaptive synchronization is investigated for stochastic neural networks of neutral-type with Markovian switching parameters. Using the M-matrix approach and the stochastic analysis method, some sufficient conditions are obtained to ensure three kinds of adaptive synchronization for the stochastic neutral-type neural networks.(More)
In this paper, the robust stability is investigated for neural networks of neutral-type with both discrete and distributed time-varying delays. Based on Lyapunov-Krasovskii stability theory and stochastic analysis approaches, several new criteria are derived to guarantee the robust stability of the system. Some numerical examples are given to demonstrate(More)
In this paper, the adaptive lag synchronization control problem for uncertain chaotic neural networks with parameters perturbation is investigated. Both the discrete and distributed time-varying delays terms are considered to model a more realistic dynamical behavior of systems in practice. Based on the Lyapunov functional method and the adaptive feedback(More)
In this paper, we propose a systematic approach to the design of optimal training for multiple-antenna communications. We first derive two design criteria for general optimal training: one is in the time domain and the other is in the frequency domain. The frequency-domain design criterion leads to a systematic design procedure that can be used to construct(More)