Global attractivity in delayed Hopfield neural network models
SIAM Journal on Applied Mathematics
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
On the Controllability of the Continuous-Time Hopfield-Type Neural Networks
SYNASC '05 Proceedings of the Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Long-term attraction in higher order neural networks
IEEE Transactions on Neural Networks
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In this letter, using methods proposed by E. Kaslik, St. Balint, and their colleagues, we develop a new method, expansion approach, for estimating the attraction domain of asymptotically stable equilibrium points of Hopfield-type neural networks. We prove theoretically and demonstrate numerically that the proposed approach is feasible and efficient. The numerical results that obtained in the application examples, including the network system considered by E. Kaslik, L. Brăescu, and St. Balint, indicate that the proposed approach is able to achieve better attraction domain estimation.