Multistability analysis for a general class of delayed Cohen–Grossberg neural networks
In this paper, by discussing parameter conditions based on properties of activation functions, we decompose state space into positively invariant sets and establish sufficient conditions for the existence of locally stable equilibria for delayed Cohen–Grossberg neural networks (CGNNs) through Cauchy...
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| Veröffentlicht in: | Information sciences 2012-03, Vol.187, p.233-244 |
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| creator | Huang, Zhenkun Feng, Chunhua Mohamad, Sannay |
| description | In this paper, by discussing parameter conditions based on properties of activation functions, we decompose state space into positively invariant sets and establish sufficient conditions for the existence of locally stable equilibria for delayed Cohen–Grossberg neural networks (CGNNs) through Cauchy convergence principle. Some new criteria are derived for ensuring equilibria (periodic orbits) to be locally or globally exponentially stable in any designated region. Finally, our results are demonstrated by four numerical simulations. |
| doi_str_mv | 10.1016/j.ins.2011.10.019 |
| format | Article |
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Some new criteria are derived for ensuring equilibria (periodic orbits) to be locally or globally exponentially stable in any designated region. 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| ispartof | Information sciences, 2012-03, Vol.187, p.233-244 |
| issn | 0020-0255 1872-6291 |
| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Cohen–Grossberg neural networks Computer simulation Convergence Criteria Equilibrium Exponential stability Invariants Mathematical analysis Mathematical models Multistability analysis Neural networks Orbits Periodic orbit |
| title | Multistability analysis for a general class of delayed Cohen–Grossberg neural networks |
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