Presentation	2000/6/16 Generalization of Complete Stability Conditions of Neural Networks with Piecewise Linear Output Function Norikazu Takahashi,
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Abstract(in English)	A recurrent neural network is said to be completely stable if its state trajectory converges to an equilibrium point for any initial condition. The author recently derived a sufficient condition for recurrent neural networks with the piecewise linear output function to be completely stable. In this report, a new complete stability condition which is a generalization of the above mentioned sufficient condition is given. A convergence theorem of the Gauss-Seidel method, which is a well-known iterative technique for solving linear algebraic equations, plays an important role while most of the conventional stability criteria were obtained by constructing Lyapunov functions.
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Keyword(in English)	recurrent neural networks / complete stability / Gauss-Seidel method
Paper #	CAS2000-29,VLD2000-38,DSP2000-50
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Committee	CAS
Conference Date	2000/6/16(1days)
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Paper Information
Registration To	Circuits and Systems (CAS)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Generalization of Complete Stability Conditions of Neural Networks with Piecewise Linear Output Function
Sub Title (in English)
Keyword(1)	recurrent neural networks
Keyword(2)	complete stability
Keyword(3)	Gauss-Seidel method
1st Author's Name	Norikazu Takahashi
1st Author's Affiliation	Department of Computer Science and Communication Engineering, Kyushu University()
Date	2000/6/16
Paper #	CAS2000-29,VLD2000-38,DSP2000-50
Volume (vol)	vol.100
Number (no)	119
Page	pp.pp.-
#Pages	6
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