Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/130212
DC Field | Value | Language |
---|---|---|
dc.contributor | 應數系 | - |
dc.creator | 班榮超 | - |
dc.creator | Ban, Jung-Chao | - |
dc.creator | Chang, Chih-Hung | - |
dc.date | 2013-01 | - |
dc.date.accessioned | 2020-06-22T05:45:11Z | - |
dc.date.available | 2020-06-22T05:45:11Z | - |
dc.date.issued | 2020-06-22T05:45:11Z | - |
dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/130212 | - |
dc.description.abstract | This manuscript considers the learning problem of multi-layer neural networks (MNNs) with an activation function which comes from cellular neural networks. A systematic investigation of the partition of the parameter space is provided. Furthermore, the recursive formula of the transition matrix of an MNN is obtained. By implementing the well-developed tools in the symbolic dynamical systems, the topological entropy of an MNN can be computed explicitly. A novel phenomenon, the asymmetry of a topological diagram that was seen in Ban, Chang, Lin, and Lin (2009) [J. Differential Equations 246, pp. 552–580, 2009], is revealed. | - |
dc.format.extent | 491685 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.relation | Neural Networks, Vol.46, pp.116-123 | - |
dc.subject | Multi-layer neural networks ; Topological entropy ; Sofic shift ; Learning problem ; Linear separation | - |
dc.title | The learning problem of multi-layer neural networks | - |
dc.type | article | - |
dc.identifier.doi | 10.1016/j.neunet.2013.05.006 | - |
dc.doi.uri | https://doi.org/10.1016/j.neunet.2013.05.006 | - |
item.cerifentitytype | Publications | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | restricted | - |
item.openairetype | article | - |
Appears in Collections: | 期刊論文 |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.