TY - GEN
T1 - Continuous neural identifier for uncertain nonlinear systems with time delays in the input signal
AU - Alfaro-Ponce, Mariel
AU - Arguelles, Amadeo
AU - Chairez, Isaac
N1 - Funding Information:
M. Alfaro Ponce and A. Arguelles would like to thank the Instituto Politécnico Nacional (Secretaría Académica, COFAA, SIP, and CIC - projects SIP-20130303 and SIP-20131867) for their financial contributions that led to develop this work. Isaac Chairez acknowledges the financial support provided by the Instituto Politecnico Nacional and its Secretaria de Investigacion y Posgrado.
PY - 2013
Y1 - 2013
N2 - Time-delay systems have been succesfully used to represent complex dynamical systems. Indeed, time-delay is usually encountered as part of many real systems. Among others, biological and chemical plants have been modeled using Time-delay terms with better results than those models that do not consider them. However, getting those models represents a formidable effort and sometimes the results are not so satisfactory. On the other hand, no parametric modelling offer an alternative to obtain suitable and usable models. Continuous neural networks (CNN) have been considered as a real alternative to produce such no parametric representations. This article introduces the design of a specific class of no parametric model for uncertain Time-delay system based on CNN considering the so-called delayed learning laws. The convergence analysis as well as the learning laws are produced from a Lyapunov-Krasovskii functional. A numerical example regarding the human innmunodeficiency virus dynamical behavior is used to show the performance of the suggeted no parametric identifier based on CNN.
AB - Time-delay systems have been succesfully used to represent complex dynamical systems. Indeed, time-delay is usually encountered as part of many real systems. Among others, biological and chemical plants have been modeled using Time-delay terms with better results than those models that do not consider them. However, getting those models represents a formidable effort and sometimes the results are not so satisfactory. On the other hand, no parametric modelling offer an alternative to obtain suitable and usable models. Continuous neural networks (CNN) have been considered as a real alternative to produce such no parametric representations. This article introduces the design of a specific class of no parametric model for uncertain Time-delay system based on CNN considering the so-called delayed learning laws. The convergence analysis as well as the learning laws are produced from a Lyapunov-Krasovskii functional. A numerical example regarding the human innmunodeficiency virus dynamical behavior is used to show the performance of the suggeted no parametric identifier based on CNN.
KW - HIV system
KW - Lyapunov-Krasovskii functional
KW - Time-delay uncertain systems
KW - continuous neural networks
UR - http://www.scopus.com/inward/record.url?scp=84893587584&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2013.6706917
DO - 10.1109/IJCNN.2013.6706917
M3 - Contribución a la conferencia
AN - SCOPUS:84893587584
SN - 9781467361293
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 100
EP - 107
BT - 2013 International Joint Conference on Neural Networks, IJCNN 2013
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2013 International Joint Conference on Neural Networks, IJCNN 2013
Y2 - 4 August 2013 through 9 August 2013
ER -