標題: Entropy Bifurcation of Neural Networks on Cayley Trees
作者: Ban, Jung-Chao
Chang, Chih-Hung
Huang, Nai-Zhu
應用數學系
Department of Applied Mathematics
關鍵字: Neural networks;learning problem;Cayley tree;separation property;entropy spectrum;minimal entropy
公開日期: 1-Jan-2020
摘要: It has been demonstrated that excitable media with a tree structure performed better than other network topologies, therefore 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 of entropy spectrum of neural networks on Cayley trees and reveals whether the entropy bifurcates when the coupling weights change.
URI: http://dx.doi.org/10.1142/S0218127420500157
http://hdl.handle.net/11536/153925
ISSN: 0218-1274
DOI: 10.1142/S0218127420500157
期刊: INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
Volume: 30
Issue: 1
起始頁: 0
結束頁: 0
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