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政大典藏 > College of Science > Department of Mathematical Sciences > Periodical Articles > Item 140.119/129557

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/129557

Title:	Entropy bifurcation of neural networks on Cayley trees
Authors:	班榮超 Ban, Jung-Chao Chang, Chih-Hung Huang, Nai-Zhu
Contributors:	應數系
Keywords:	Neural networks;learning problem;Cayley tree;separation property;entropy spectrum;minimal entropy
Date:	2019-06
Issue Date:	2020-04-28 13:55:03 (UTC+8)
Abstract:	It has been demonstrated that excitable media with a tree structure performed better than other network topologies, it is natural to consider neural networks defined on Cayley trees. The investigation of a symbolic space called tree-shift of finite type is important when it comes to the discussion of the equilibrium solutions of neural networks on Cayley trees. Entropy is a frequently used invariant for measuring the complexity of a system, and constant entropy for an open set of coupling weights between neurons means that the specific network is stable. This paper gives a complete characterization for entropy spectrum of neural networks on Cayley trees and reveals whether the entropy bifurcates when the coupling weights change.
Relation:	International Journal of Bifurcation and Chaos, 30:1
Data Type:	article
DOI link:	https://doi.org/10.1142/S0218127420500157
DOI:	10.1142/S0218127420500157
Appears in Collections:	[Department of Mathematical Sciences] Periodical Articles

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