Continuous neural identifier for uncertain nonlinear systems with time delays in the input signal

—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 timedelay 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, non-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 non-parametric representations. This article introduces the design of a speci c class of non-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 LyapunovKrasovskii functional. A numerical example regarding the human innmunode ciency virus dynamical behavior is used to show the performance of the suggeted non-parametric identi er based on CNN.