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https://ah.lib.nccu.edu.tw/handle/140.119/130204
題名: | Spatial complexity in multi-layer cellular neural networks | 作者: | 班榮超 Ban, Jung-Chao Chang, Chih-Hung Lin, Song-Sun Lin, Yin-Heng |
貢獻者: | 應數系 | 關鍵詞: | Learning problem | 日期: | 一月-2009 | 上傳時間: | 22-六月-2020 | 摘要: | This study investigates the complexity of the global set of output patterns for one-dimensional multi-layer cellular neural networks with input. Applying labeling to the output space produces a sofic shift space. Two invariants, namely spatial entropy and dynamical zeta function, can be exactly computed by studying the induced sofic shift space. This study gives sofic shift a realization through a realistic model. Furthermore, a new phenomenon, the broken of symmetry of entropy, is discovered in multi-layer cellular neural networks with input. | 關聯: | Journal of Differential Equations, Vol.246, No.2, pp.552-580 | 資料類型: | article | DOI: | http://dx.doi.org/10.1109/CNNA.2010.5430257 |
Appears in Collections: | 期刊論文 |
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