Asymptotical Stability for a Class of Complex-Valued Projective Neural Network
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Abstract
In this paper, a new class of complex-valued projective neural network is introduced and studied on a nonempty, closed, and convex subset of a finite-dimensional complex space. An existence and uniqueness result for the equilibrium point of complex-valued projective neural network is proved under some suitable conditions. Moreover, by utilizing the linear matrix inequality technique, some sufficient conditions are presented to ensure the asymptotical stability of the complex-valued projective neural network. Finally, two examples are given to illustrate the validity and feasibility of main results.
Keywords
Complex-valued projective neural network Equilibrium point Linear matrix inequality technique Homeomorphism method Asymptotical stabilityMathematics Subject Classification
49J40 34K20 92B20Notes
Acknowledgements
The authors are grateful to the editor and the referees for their valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (11471230, 11671282) and the Applied Basic Research Programs of Department of Science and Technology of Sichuan Province of China (No. 2016JY0249).
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