Основы математического анализа и элементы дифференциальных уравнений

Наименование:

A Symbolic Dynamics Approach for Anomaly Detection Complex Systems in Engineering.

Автор:

Shalabh G.

мягкая обложка
152 страниц
2008 год

Издательство:

Книга по Требованию

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2413 руб

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Описание:

Therefore, for safe and reliable operation, it is essential to develop robust analytical tools for online degradation monitoring and for generating advanced warnings of emerging anomalies. Maturity of scientific theories has facilitated creation of advanced technology of human-engineered complex systems. Since it is often infeasible to achieve the required modeling accuracy due to the presence of i) high dimensionality, ii) non-stationarity (possibly chaotic behavior), iii) nonlinearity, and iv) exogenous disturbances, time series analysis of appropriate sensor data provides one of the most powerful tools for degradation monitoring of complex systems. This book presents a data-driven pattern identification methodology, built upon multidisciplinary concepts of Symbolic Dynamics, Automata Theory and Information Theory, with diverse applications to complex electromechanical systems. These anomalies may alter the quasi-static behavior that causes performance degradation and can eventually lead to catastrophic failures. A major challenge in these systems is online detection of behavioral uncertainties due to gradual evolution of anomalies (i. e. , deviations from the nominal condition).

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Study on decision support system for the interpretation of laboratory data by an artificial neural network // Rinsho. Byori.- 1994.- V.42, N.2.- P.195-199. 336. Pedrycz W. Neurocomputations in relational systems // IEEE Trans. Pattern Anal. and Mach. Intell.- 1991.- 13, № 3.- PP. 289-297. 337. Pham D.T., Liu X. Statespace identification of dynamic systems using neural networks // Eng. Appl. Artif. Intell.1990.- 3, № 3.- PP. 198-203. 338. Pineda F.J. Recurrent bakpropagation and the dynamical approach to adaptive neural computation. - Neural Comput., 1989. Vol. 1. PP.161–172. 339. Poli R., Cagnoni S., Livi R. et al. A Neural Network Expert System for Diagnosing and Treating Hypertension // Computer.- 1991.- N.3.- P.64-71. 340. Prechelt L. Comparing Adaptive and Non-Adaptive Connection Pruning With Pure Early Stopping // Progress in Neural Information Processing (Hong Kong, September 24-27, 1996), Springer, Vol. 1 pp. 46-52. 341. Real Brains, Artificial Minds/Ed. by J.L. Casti, A. Karlqvist. Norton-Holland, 1987. 226 p. 342. Reinbnerger G., Weiss G., Werner-Felmayer G. et al ...»

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A Symbolic Dynamics Approach for Anomaly Detection Complex Systems in Engineering. Shalabh G. http://www.booksiti.net.ru/books/14315925

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