Please use this identifier to cite or link to this item: https://ah.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 連結: https://doi.org/10.1142/S0218127420500157
Appears in Collections:[應用數學系] 期刊論文

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