Global exponential stability of high-order Hopfield-type neural networks with S-type distributed time delays

LMI-Based Approach for Global Asymptotic Stability Analysis of Recurrent Neural Networks with Various Delays and Structures

“…This type of (6) has been studied in [15], [18], [42]- [49] based on the M-matrix method and algebraic inequality methods. However, no any LMI-based stability results for (1) and its special case (6) are available in the literature.…”

Robust Stability Analysis of Fractional-Order Hopfield Neural Networks with Parameter Uncertainties

“…Thus, for integer-order Hopfield neural networks, it is important to study their dynamical properties, in particular, the stability analysis of integer-order Hopfield neural networks which is a prime issue for the practical design and application of neural networks. In recent years, some sufficient conditions for global asymptotic and exponential stability of integer-order Hopfield neural networks have been proposed [1][2][3][4]. However, the above researches did not consider the influence of parameter uncertainties, which are unavoidable due to measure errors, the parameter fluctuation, external disturbance, and so forth.…”

Global Exponential Robust Stability of High-Order Hopfield Neural Networks with S-Type Distributed Time Delays

“…Neural networks and their various generalizations have been successfully employed in many areas such as pattern recognition, cognitive modeling, adaptive control, and combinatorial optimization [1][2][3][4][5][6][7]. Hopfield neural networks (HNNs), as some forms of recurrent artificial neural networks, have been widely studied in recent years [8][9][10][11][12]. The earlier HNNs model proposed by Hopfield [13,14] was based on the theory of analog circuit consisting of capacitors, resistors, and amplifiers and can be formulated as a system of ordinary equations.…”