Dynamics under Uncertainty: Modeling Simulation and Complexity

Dynamics under Uncertainty: Modeling Simulation and Complexity

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 uncerta...

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Bibliographic Details
Other Authors: Pamučar, Dragan (Editor), Marinkovic, Dragan (Editor), Kar, Samarjit (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Subjects:
Research & information: general
Mathematics & science
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
multi-criteria optimization
RAFSI method
performance comparison
rank reversal
Magnetic Resonance Imaging (MRI)
wavelet transform
GARCH
LLA
LDA
KNN
BWM
BWM-I
multi-criteria
renewable energy
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
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Description
Summary: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.
Physical Description:1 electronic resource (210 p.)
ISBN:books978-3-0365-1575-5
9783036515762
9783036515755
Access:Open Access

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