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Dynamics under Uncertainty: Modeling Simulation and Complexity
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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.
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Keywords
- AES
- Affective Computing
- AHP
- applied mathematics general
- artificial emotions
- BWM
- BWM-I
- classification and discrimination
- criteria weights
- D numbers
- Data mining
- DEMATEL
- dual-rotor
- empathic building
- ensemble techniques
- fuzzy grey cognitive maps
- Fuzzy MARCOS
- Fuzzy PIPRECIA
- Fuzzy sets
- GARCH
- kNN
- LDA
- linear regression
- LLA
- Logistics
- magnetic resonance imaging (MRI)
- Mathematics & science
- MCDM
- Medical applications
- metamodel
- MIMO discrete-time system
- multi-criteria
- multi-criteria decision-making
- multi-criteria optimization
- multi-frequency excitation
- n/a
- NDSL model
- non-intrusive calculation
- pairwise comparisons
- parameter dependence
- PC
- performance comparison
- Prediction theory
- RAFSI method
- rank reversal
- Reference, information & interdisciplinary subjects
- renewable energy
- Research & information: general
- stackers
- state feedback and output feedback
- TFN
- Thayer’s emotion model
- the CCSD method
- the ITARA method
- the MARCOS method
- theory of mathematical modeling
- traffic risk
- wavelet transform
Links
DOI: 10.3390/books978-3-0365-1575-5Editions