Titlebook: Convergence Analysis of Recurrent Neural Networks; Zhang Yi,K. K. Tan Book 2004 Springer Science+Business Media Dordrecht 2004 calculus.co

Sedative

Preparation of Research-Oriented Study,us activation functions have been used for neural networks. Recently, the studies reported in [19, 51, 75, 76, 203, 204, 208, 54] are focused on a class of RNNs with unsaturating linear threshold activation functions (LT networks). This class of neural networks has potential in many important applic

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Preparation of Research-Oriented Study, applications in winner-take-all, winner-share-all and .-winner-take-all problems, see [15, 16, 62]. Due to the application potential of this class of neural networks, it is necessary and useful to study its general dynamic properties.

有角

Vibration Analysis of Discrete Systems,without decaying linear term is discussed. This model has been found applications in optimization problems. A concept, called input-to-state convergence (ISC), will be proposed and analyzed to this model. In the second part, a model which can be used to attract eigenvectors of any symmetric matrix i

Engulf

Convergence Analysis of Recurrent Neural Networks978-1-4757-3819-3Series ISSN 1568-1696

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GRAVE

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Lumbar-Spine

Vibration Analysis of Discrete Systems,The model of Cellular Neural Networks (CNNs) was first proposed by Chua and Yang [42, 43] in 1988. Since then, CNNs have been widely studied both in theory and applications. They are inherently local in nature and are easily to be implemented in very large scale integration (VLSI).

漫步

https://doi.org/10.1007/978-981-16-5457-2It is well known that activation functions of RNNs are important characters of RNNs. Usually, activation functions are nonlinear functions, thus they make RNNs be described by nonlinear systems. Since they represent some structural parts of the the nonlinear systems, they crucially decide the dynamical properties of RNNs.

Flirtatious

Natural Vibrations of Symmetric Structures,Generally, a discrete time RNN can be described by the difference system model. where .., . : .... is a mapping which can be bounded or unbounded. By suitably selecting the mapping . in (8.1), various classes of discrete RNNs can be obtained.