Complex engineering systems: rational choice of evolutionary projects | Revista Publicando
Complex engineering systems: rational choice of evolutionary projects
PDF (EN)

Palabras clave

documentos digitales
herramienta informática
información
metadatos.

Cómo citar

Ilyas I., Svetlana F., & Pavel A. (2018). Complex engineering systems: rational choice of evolutionary projects. Revista Publicando, 5(16 (1), 409-420. Recuperado a partir de https://revistapublicando.org/revista/index.php/crv/article/view/1510

Resumen

This article is devoted to several actual problems of complex engineering system design and development. The main subject is how to choose most rational project for system future evolution from various proposed one. Fuzzy forecasting approach seems to be mostly fruitful solution to overcome these problems. A new technique for realizing this approach via fuzzy shaping of system”™s acceptability domain is considered. As example several useful yield results of the approach practical implementation achieved by choosing the best improving project for the real complex engineering system are established. Some perspective directions for future project development exploration based on fuzzy forecasting approach are briefly discussed.

PDF (EN)

Citas

J.F. Meyer, “On evaluating the performability of degradable computing system”, IEEE Trans. on Computers, vol. 29, â„– 8, pp. 720–731, 1980.

D.M. Nicol, W.H. Sanders and K.S. Trivedy, “Model-based evaluation: from dependability to security”, IEEE Trans. on Dependable and Secure Computing, vol. 1, â„– 1, pp. 48–65, 2004.

Zinov”™ev P.A., Moiseev V.S., Ginatullin I.A. (et al.), “Topical problems of corporative control in aircraft manufacturing”, Russian Aeronautics, â„– 2, 2007.

V.F. Nicola, P. Shahabuddin and M. Nakayama, “Techniques for fast simulation of models of highly dependable systems”, IEEE Trans. on Reliability, vol. 50, â„–3, pp. 246–264, 2001.

I.P. Norenkov, P.A. Zinov”™ev, “Multilevel optimization of a large-scale engineering systems”, Electronic modelling, â„– 6, 1984.

Zinoviev PA, "Modeling of the fuzzy appearance of the domain of survivability of the corporate IT system", Dynamics of systems, mechanisms and machines, No. 1, Vol. 4, p. 15-18, 2016.

M. Sugeno, K. Tanaka, “Succesive identification of fuzzy model and its applications to prediction of a complex systems”, Fuzzy Sets and Systems, vol. 42, pp. 315–334, 1991.

M Tahmassebpour (2016). Immediate detection of DDoS attacks with using NetFlow on cisco devices IOS, Indian Journal of Science and Technology 9 (26)

Ismagilov II, Zinkin VA, "Fuzzy prediction of quantitative indicators of complex systems", Studies in Informatics, â„– 11, p. 49-56, 2007.

Ismagilov II, Zinoviev PA, "Multicriteria estimation of the level of development of complex technical systems", Studies in Informatics, No. 8, p. 25-32, 2004.

A Foroughi, M Esfahani (2002). An Empirical study for ranking risk factors using linear Assignment: A Case Study of road construction, Management Science Letters 2 (2), 615-622

J. Fodor, M. Roubens, “Fuzzy preference relations and multicriteria decision support”, Kluwer Academic Publishers, Dordrecht, 1994.

J.F. Baldwin, T.P. Martin, J.M. Rossiter, “Time series modelling and prediction using fuzzy trend information”, Proc. Fifth Intern. Conf. Soft Comput. Inf./Intell.Syst., pp. 499–502, 1998.

Mehdi Mafi, “Integration of Mobile Ad hoc and WIMAX Networks with Approach of Admission Control and Hand off Combination Applied in Telemedicine Services,” American Journal of Scientific Research, vol. 83, 2012, pp. 14-24.

T.L. Saaty. “The analytic hierachy process: planning, priority setting, resource allocation”, McGraw-Hill, New York, 1980.

Descargas

La descarga de datos todavía no está disponible.