This paper investigates the mixed Ho~ and passive projective synchronization problem for fractional-order （FO） memristor-based neural networks. Our aim is to design a controller such that, though the unavoidable phe...This paper investigates the mixed Ho~ and passive projective synchronization problem for fractional-order （FO） memristor-based neural networks. Our aim is to design a controller such that, though the unavoidable phenomena of time-delay and parameter uncertainty are fully considered, the resulting closed-loop system is asymptotically stable with a mixed H∞ and passive performance level. By combining active and adaptive control methods, a novel hybrid control strategy is designed, which can guarantee the robust stability of the closed-loop system and also ensure a mixed H∞ and passive performance level. Via the application of FO Lyapunov stability theory, the projective synchronization conditions are addressed in terms of linear matrix inequaiity techniques. Finally, two simulation examples are given to illustrate the effectiveness of the proposed method.展开更多
The dynamics anMysis of recurrent neural networks （RNNs） is a first and necessary step for any practical applications of them. In the present paper, the easily verified theorem is found to ascertain the asymptotical...The dynamics anMysis of recurrent neural networks （RNNs） is a first and necessary step for any practical applications of them. In the present paper, the easily verified theorem is found to ascertain the asymptotical stability for generic RNN model with projection mapping under the critical condition that a discriminant matrix defined by the networks is semi-positive definite. The results given here not only improve deeply upon the existing relevant critical as well as non-critical dynamics conclusions in literature, but also can be used in the practical application of RNNs directly.展开更多
基金Supported by National Natural Science Foundation of China under Grant Nos.U1604146,U1404610,61473115,61203047Science and Technology Research Project in Henan Province under Grant Nos.152102210273,162102410024Foundation for the University Technological Innovative Talents of Henan Province under Grant No.18HASTIT019
文摘This paper investigates the mixed Ho~ and passive projective synchronization problem for fractional-order （FO） memristor-based neural networks. Our aim is to design a controller such that, though the unavoidable phenomena of time-delay and parameter uncertainty are fully considered, the resulting closed-loop system is asymptotically stable with a mixed H∞ and passive performance level. By combining active and adaptive control methods, a novel hybrid control strategy is designed, which can guarantee the robust stability of the closed-loop system and also ensure a mixed H∞ and passive performance level. Via the application of FO Lyapunov stability theory, the projective synchronization conditions are addressed in terms of linear matrix inequaiity techniques. Finally, two simulation examples are given to illustrate the effectiveness of the proposed method.
基金supported by the National Nature Science Foundation of China under Grant Nos.11101327,11471006,and 11171270the National Basic Research Program of China(973 Program)under Grant No.2013C13329406the Fundamental Research Funds for the Central Universities under Grant Nos.xjj20100087 and 2011jdhz30
文摘The dynamics anMysis of recurrent neural networks （RNNs） is a first and necessary step for any practical applications of them. In the present paper, the easily verified theorem is found to ascertain the asymptotical stability for generic RNN model with projection mapping under the critical condition that a discriminant matrix defined by the networks is semi-positive definite. The results given here not only improve deeply upon the existing relevant critical as well as non-critical dynamics conclusions in literature, but also can be used in the practical application of RNNs directly.
