Presentation	1999/5/20 A Larger Family of Objective Functions to which Hopfield Neural Networks can give Globally Optimal Solutions Yoshinori Uesaka,
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Abstract(in English)	A family of objective functions is discussed for which the conjecture, stating that globally optimal solution (not a local one) of Hopfield neural networks may be obtained by starting from an initial point sufficiently close to the origin, truly holds. To the conjecture, Nishi (1998) has been giving an interesting family of objective functions through the eigenvector analysis. The present paper extends his result to the larger family which suggests that the conjecture really holds for a wide range of objective functions.
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Keyword(in English)	Combinatorial optimization / Global optimum / Hopfield neural networks / Dynamical system
Paper #	NC99-3
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Conference Information
Committee	NC
Conference Date	1999/5/20(1days)
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Paper Information
Registration To	Neurocomputing (NC)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	A Larger Family of Objective Functions to which Hopfield Neural Networks can give Globally Optimal Solutions
Sub Title (in English)
Keyword(1)	Combinatorial optimization
Keyword(2)	Global optimum
Keyword(3)	Hopfield neural networks
Keyword(4)	Dynamical system
1st Author's Name	Yoshinori Uesaka
1st Author's Affiliation	Department of Information Sciences, Faculty of Science and Technology, Science University of Tokyo()
Date	1999/5/20
Paper #	NC99-3
Volume (vol)	vol.99
Number (no)	58
Page	pp.pp.-
#Pages	8
Date of Issue