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Asymptotic stability problem of a class of fuzzy cellular neural networks with unbounded distributed delays is studied. New stability criteria are derived by employing a Lyapunov-Krasovskii functional and using LMI approach. Numerical examples are provided to illustrate the effectiveness and less conservativeness of the developed techniques.
In reality, the neural networks (NNs) with different dimensions may be coupled as a complex network. This paper deals with the stabilization problem for an array of coupled NNs with time delays, in which nodes of the isolated NNs have different state dimensions from each other. Without assuming the boundedness or monotonicity of the activation functions and… (More)
In the paper a transit information system for trip prediction is introduced. Several models, including ARIMA, neural network and data fusion, can be employed to forecast the number of passengers entering or leaving a station in a period in transit networks. The data fusion model combines two individual predictions from ARIMA and neural networks models. The… (More)
Exponential synchronization is considered for a class of fuzzy neural networks with distributed delays and time-varying coefficients. By applying Dini derivative and introducing many real parameters, and estimating the upper bound of function, some sufficient conditions on the exponential synchronization of fuzzy neural networks are established. An example… (More)