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題名: | Entropy bifurcation of neural networks on Cayley trees | 作者: | 班榮超 Ban, Jung-Chao Chang, Chih-Hung Huang, Nai-Zhu |
貢獻者: | 應數系 | 關鍵詞: | Neural networks ; learning problem ; Cayley tree ; separation property ; entropy spectrum ; minimal entropy | 日期: | Jun-2019 | 上傳時間: | 28-Apr-2020 | 摘要: | 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. | 關聯: | International Journal of Bifurcation and Chaos, 30:1 | 資料類型: | article | DOI: | https://doi.org/10.1142/S0218127420500157 |
Appears in Collections: | 期刊論文 |
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