Dynamics under Uncertainty: Modeling Simulation and Complexity
Pamučar, Dragan
Dynamics under Uncertainty: Modeling Simulation and Complexity - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (210 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-1575-5 9783036515762 9783036515755
10.3390/books978-3-0365-1575-5 doi
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
Dynamics under Uncertainty: Modeling Simulation and Complexity - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (210 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-1575-5 9783036515762 9783036515755
10.3390/books978-3-0365-1575-5 doi
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