Neural identifier for unknown discrete-time nonlinear delayed systems
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.