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Improved approach to the problem of the global Mittag-Leffler synchronization for fractional-order multidimension-valued BAM neural networks based on new inequalities.
Xiao, Jianying; Zhong, Shouming; Wen, Shiping.
Afiliação
  • Xiao J; School of Sciences, Southwest Petroleum University, Chengdu, 610050, PR China. Electronic address: shawion1980@yahoo.com.
  • Zhong S; School of Mathematical Sciences, University of Electronic Science and Technology, Chengdu, 611731, PR China. Electronic address: zhongsm@uestc.edu.cn.
  • Wen S; Centre for Artificial Intelligence, Faculty of Engineering Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia. Electronic address: shiping.wen@uts.edu.au.
Neural Netw ; 133: 87-100, 2021 Jan.
Article em En | MEDLINE | ID: mdl-33152567
ABSTRACT
This paper studies the problem of the global Mittag-Leffler synchronization for fractional-order multidimension-valued BAM neural networks (FOMVBAMNNs) with general activation functions (AFs). First, the unified model is established for the researched systems of FOMVBAMNNs which can be turned into the corresponding multidimension-valued systems as long as the state variables, the connection weights and the AFs of the neural networks are valued to be real, complex, or quaternion. Then, without any decomposition, the criteria in unified form are derived by constructing the new Lyapunov-Krasovskii functionals (LKFs) in vector form, combining two new inequalities and considering the easy controllers. It is worth mentioning that the obtained criteria have many advantages in higher flexibility, more diversity, smaller computation, and lower conservatism. Finally, a simulation example is provided to illustrate the availability and improvements of the acquired results.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Simulação por Computador / Redes Neurais de Computação Tipo de estudo: Prognostic_studies Idioma: En Revista: Neural Netw Assunto da revista: NEUROLOGIA Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Simulação por Computador / Redes Neurais de Computação Tipo de estudo: Prognostic_studies Idioma: En Revista: Neural Netw Assunto da revista: NEUROLOGIA Ano de publicação: 2021 Tipo de documento: Article