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|Title:||Spatial complexity in multi-layer cellular neural networks|
|Issue Date:||2020-06-22 13:43:25 (UTC+8)|
|Abstract:||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.|
|Relation:||Journal of Differential Equations, Vol.246, No.2, pp.552-580|
|Appears in Collections:||[應用數學系] 期刊論文|
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