Identification of Nonlinear Dynamic Processes with Unknown and Variable Dead Time Using an Internal Recurrent Neural Network

1995 ◽  
Vol 34 (5) ◽  
pp. 1735-1742 ◽  
Author(s):  
Yi Cheng ◽  
Thomas W. Karjala ◽  
David M. Himmelblau
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Nonlinear Dynamic ◽  
Dead Time ◽  
Dynamic Processes ◽  
Nonlinear Dynamic Processes

Near-Epoch Dependence

2021 ◽  
pp. 368-398
Author(s):  
James Davidson
Keyword(s):  
Theoretical Result ◽  
Nonlinear Dynamic ◽  
Dynamic Processes ◽  
Related Concept ◽  
Mixing Processes ◽  
Special Result ◽  
Nonlinear Dynamic Processes

Near‐epoch dependence (NED) is a generalized dependence concept for functions of mixing processes. This chapter gives definitions and examples, and considers the application to nonlinear dynamic processes. The relation to mixingales is the key theoretical result. Results on the preservation of NED under various transformations and a special result for the adapted sequence case are considered next. Finally, the related concept of approximability is defined which, unlike NED, can hold in the absence of integer moments.

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