A new robust training law for dynamic neural networks with external disturbance: an LMI approach.
Ahn, Choon Ki (2010)
Discrete Dynamics in Nature and Society
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Displaying similar documents to “Local stability conditions for discrete-time cascade locally recurrent neural networks”
A new robust training law for dynamic neural networks with external disturbance: an LMI approach.
Stability analysis and absolute synchronization of a three-unit delayed neural network
Lin Jun Wang, You Xiang Xie, Zhou Chao Wei, Jian Peng (2015)
Kybernetika
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In this paper, we consider a three-unit delayed neural network system, investigate the linear stability, and obtain some sufficient conditions ensuring the absolute synchronization of the system by the Lyapunov function. Numerical simulations show that the theoretically predicted results are in excellent agreement with the numerically observed behavior.
On global exponential stability of discrete-time Hopfield neural networks with variable delays.
Zhang, Qiang, Wei, Xiaopeng, Xu, Jin (2007)
Discrete Dynamics in Nature and Society
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Jalab, H.A., Ibrahim, R.W. (2009)
Surveys in Mathematics and its Applications
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Robust stability analysis of fuzzy neural network with delays.
Zhao, Kaihong, Li, Yongkun (2009)
Mathematical Problems in Engineering
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Delay-dependent exponential stability for discrete-time BAM neural networks with time-varying delays.
Chen, Yonggang, Bi, Weiping, Wu, Yuanyuan (2008)
Discrete Dynamics in Nature and Society
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Boundedness and stability for discrete-time delayed neural network with complex-valued linear threshold neurons.
Duan, Chengjun, Song, Qiankun (2010)
Discrete Dynamics in Nature and Society
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On Popov-type stability criteria for neural networks.
Danciu, Daniela, Răsvan, Vladimir (2000)
Electronic Journal of Qualitative Theory of Differential Equations [electronic only]
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Dynamic properties of cellular neural networks.
Slavova, Angela (1993)
Journal of Applied Mathematics and Stochastic Analysis
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Neural workbench: An object-oriented neural network simulatior.
Margaris, Athanasios, Kotsialos, Efthymios, Styliadis, Athansios, Roumeliotis, Manos (2004)
Acta Universitatis Apulensis. Mathematics - Informatics
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Neural networks as a tool for georadar data processing
Piotr Szymczyk, Sylwia Tomecka-Suchoń, Magdalena Szymczyk (2015)
International Journal of Applied Mathematics and Computer Science
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In this article a new neural network based method for automatic classification of ground penetrating radar (GPR) traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector) are neural network inputs for automatic classification of a special kind of geologic structure-a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from...