Dynamics under Uncertainty: Modeling Simulation and Complexity

Edited by
August 2021
210 pages
  • ISBN978-3-0365-1576-2 (Hardback)
  • ISBN978-3-0365-1575-5 (PDF)

This book is a reprint of the Special Issue Dynamics under Uncertainty: Modeling Simulation and Complexity that was published in

Computer Science & Mathematics
Physical Sciences
Public Health & Healthcare

The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical states due to the effect of different types of uncertainties. Actually, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes and impreciseness could be modeled by fuzzy sets, rough sets, Dempster–Shafer theory, etc.

  • Hardback
© 2022 by the authors; CC BY-NC-ND license
Fuzzy MARCOS; Fuzzy PIPRECIA; traffic risk; TFN; MCDM; dual-rotor; multi-frequency excitation; non-intrusive calculation; metamodel; NDSL model; AHP; criteria weights; pairwise comparisons; AES; PC; MIMO discrete-time system; state feedback and output feedback; parameter dependence; D numbers; fuzzy sets; DEMATEL; multi-criteria decision-making; criteria weights; multi-criteria optimization; RAFSI method; performance comparison; rank reversal; Magnetic Resonance Imaging (MRI); wavelet transform; GARCH; LLA; LDA; KNN; BWM; BWM-I; criteria weights; multi-criteria; renewable energy; MCDM; the CCSD method; the ITARA method; the MARCOS method; stackers; logistics; ensemble techniques; data mining; classification and discrimination; linear regression; applied mathematics general; prediction theory; theory of mathematical modeling; medical applications; empathic building; fuzzy grey cognitive maps; Thayer’s emotion model; artificial emotions; affective computing; n/a