Without assuming the boundedness and monotonicity of neuron activations, we investigate passivity of delayed neural networks with discontinuous activations. Based on differential inclusion theory, sufficient conditions are established in form of linear matrix inequality by employing the generalized Lyapunov approach. In addition, a kind of control input is designed to stabilize neural network with activation functions having special form. Finally, some numerical examples are proposed to show the effectiveness of developed results.
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Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R ChinaHunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China
Duan, Lian
Huang, Lihong
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Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China
Hunan Womens Univ, Dept Informat Technol, Changsha 410004, Hunan, Peoples R ChinaHunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China
Huang, Lihong
Guo, Zhenyuan
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Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R ChinaHunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China
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Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Peoples R China
China Univ Min & Technol, Coll Sci, Xuzhou 221116, Peoples R ChinaHuazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Peoples R China
Zhu, Song
Shen, Yi
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Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Peoples R ChinaHuazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Peoples R China
机构:
Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R ChinaHunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China
Wang, Jiafu
Huang, Lihong
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Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R ChinaHunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China