Some Local Stability Properties Of An Autonomous Long Short-Term Memory Neural Network Model

2018 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)(2018)

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摘要
In this paper some local stability results for an autonomous Long Short-Term Memory neural network model with respect to the origin are provided. In particular, it is shown through linearization that the local asymptotic stability conditions with respect to the origin only depend on one of the weight matrices. Simulations indicate that these local stability conditions greatly influence the behavior of the autonomous four-dimensional neural network in the region where each variable's values vary between minus one and one. Finally, some sufficient stability conditions for the nonlinear model are formulated as a convex program involving linear matrix inequalities.
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关键词
local asymptotic stability conditions,four-dimensional neural network,sufficient stability conditions,local stability properties,autonomous long short-term memory neural network model,convex program,linear matrix inequalities
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