Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/130197
題名: | On the dense entropy of two-dimensional inhomogeneous cellular neural networks | 作者: | 班榮超 Ban, Jung-Chao Chang, Chih-Hung |
貢獻者: | 應數系 | 關鍵詞: | Entropy ; learning problem ; ICNN | 日期: | Nov-2008 | 上傳時間: | 22-Jun-2020 | 摘要: | This investigation elucidates the dense entropy of two-dimensional inhomogeneous cellular neural networks (ICNN) with/without input. It is strongly related to the learning problem (or inverse problem); the necessary and sufficient conditions for the admissibility of local patterns must be characterized. For ICNN with/without input, the entropy function is dense in [0, log 2] with respect to the parameter space and the radius of the interacting cells, indicating that, in some sense, ICNN exhibit a wide range of phenomena. | 關聯: | International Journal of Bifurcation and Chaos, Vol.18, No.11, pp.3221-3231 | 資料類型: | article | DOI: | https://doi.org/10.1142/S0218127408022378 |
Appears in Collections: | 期刊論文 |
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.