Options
Identification of spatially interconnected systems using neural network
Publikationstyp
Conference Paper
Date Issued
2010
Sprache
English
Institut
Start Page
6938
End Page
6943
Article Number
5717080
Citation
Proceedings of the IEEE Conference on Decision and Control (): 5717080 6938-6943 (2010)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
IEEE
This paper presents an identification technique based on linear recurrent neural network to identify spatially interconnected systems both in open and closed-loop form. The latter has not been addressed in the literature for the systems under consideration. The paper considers identification of two-dimensional (time and space) systems; the method can be easily extended to have more than one dimension in space. In this paper we consider a semi-causal (causal in time and non-causal in space) two-dimensional (2-D) system, which may be separable or non-separable but the method can also be used for 2-D systems which are causal in both dimensions. Furthermore the algorithm can handle boundary conditions. The effectiveness of the method is shown with application to simulation examples. ©2010 IEEE.
DDC Class
600: Technik
620: Ingenieurwissenschaften