| Title: | On the dense entropy of two-dimensional inhomogeneous cellular neural networks |
| Authors: | 班榮超
Ban, Jung-Chao
Chang, Chih-Hung |
| Contributors: | 應數系 |
| Keywords: | Entropy;learning problem;ICNN |
| Date: | 2008-11 |
| Issue Date: | 2020-06-22 13:41:53 (UTC+8) |
| Abstract: | 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. |
| Relation: | International Journal of Bifurcation and Chaos, Vol.18, No.11, pp.3221-3231 |
| Data Type: | article |
| DOI 連結: | https://doi.org/10.1142/S0218127408022378 |
| Appears in Collections: | [應用數學系] 期刊論文
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