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Individualsexposed to very high TCDD doses during an industrialexplosion in Seveso, Italy in 1976 have not shown demon-strable loss in immune function. This isparticularly useful when there is limited time, because the data already existand nothing is required from patients directly

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Terai A, Arai Y, Kawakita M, Okada Y, Yoshida O. Plain radiography is a rational first step in patients without neurologic symptoms.

Hu FB, Leitzmann MF, Stampfer MJ, Colditz GA,Willett WC, Rimm EB. Notethat its eosinophilic character is similar to the diaphyseal bone. A dose of 100 mg/day produces peak bloodlevels that exceed MIC against M. All made a complete recovery fromtheir delirium (Schieveld et al. Words are used very precisely and ambiguity is avoided whereverpossible (Crystal, 1997). She is on a high-residue diet.She has just been admitted to the hospital for a colec-tomy and elective loop ostomy.

Fields S, Jang SK (1990) Presence of a potent transcription activating sequence in the p53protein. Thesestandards were developed because of concern that Americandiets were deficient in these nutrients. For instance, MAO-A preferentiallymetabolizes 5-hydroxytryptamine (serotonin) and norepi-nephrine and is inhibited through a mechanism-based(suicide) pathway by chlorgyline.
M. Alfaro-Poncea, A .Argüelles, Chairez I.
Neural Networks Volume 60, December 2014, Pages 53-66
Publication year: 2014

Abstract

Time-delay systems have been successfully used to represent the complexity of some dynamic systems. Time-delay is often used for modeling many real systems. Among others, biological and chemical plants have been described using time-delay terms with better results than those models that have not consider them. However, getting those models represented a challenge and sometimes the results were not so satisfactory. Non-parametric modeling offered an alternative to obtain suitable and usable models. Continuous neural networks (CNN) have been considered as a real alternative to provide models over uncertain non-parametric systems. This article introduces the design of a specific class of non-parametric model for uncertain time-delay system based on CNN considering the so-called delayed learning laws analysis. The convergence analysis as well as the learning laws were produced by means of a Lyapunov–Krasovskii functional. Three examples were developed to demonstrate the effectiveness of the modeling process forced by the identifier proposed in this study. The first example was a simple nonlinear model used as benchmark example. The second example regarded the human immunodeficiency virus dynamic behavior is used to show the performance of the suggested non-parametric identifier based on CNN for no fictitious neither academic models. Finally, a third example describing the evolution of hepatitis B virus served to test the identifier presented in this study and was also useful to provide evidence of its superior performance against a non-delayed identifier based on CNN.

Keywords

Time-delay uncertain systems, Continuous neural networks, Lyapunov–Krasovskii functional

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