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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Format: | Electronic Book Chapter |
Language: | English |
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Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2021
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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700 | 1 | |a Kar, Samarjit |4 edt | |
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245 | 1 | 0 | |a Dynamics under Uncertainty: Modeling Simulation and Complexity |
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520 | |a 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. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
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653 | |a Fuzzy MARCOS | ||
653 | |a Fuzzy PIPRECIA | ||
653 | |a traffic risk | ||
653 | |a TFN | ||
653 | |a MCDM | ||
653 | |a dual-rotor | ||
653 | |a multi-frequency excitation | ||
653 | |a non-intrusive calculation | ||
653 | |a metamodel | ||
653 | |a NDSL model | ||
653 | |a AHP | ||
653 | |a criteria weights | ||
653 | |a pairwise comparisons | ||
653 | |a AES | ||
653 | |a PC | ||
653 | |a MIMO discrete-time system | ||
653 | |a state feedback and output feedback | ||
653 | |a parameter dependence | ||
653 | |a D numbers | ||
653 | |a fuzzy sets | ||
653 | |a DEMATEL | ||
653 | |a multi-criteria decision-making | ||
653 | |a multi-criteria optimization | ||
653 | |a RAFSI method | ||
653 | |a performance comparison | ||
653 | |a rank reversal | ||
653 | |a Magnetic Resonance Imaging (MRI) | ||
653 | |a wavelet transform | ||
653 | |a GARCH | ||
653 | |a LLA | ||
653 | |a LDA | ||
653 | |a KNN | ||
653 | |a BWM | ||
653 | |a BWM-I | ||
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653 | |a renewable energy | ||
653 | |a the CCSD method | ||
653 | |a the ITARA method | ||
653 | |a the MARCOS method | ||
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653 | |a prediction theory | ||
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653 | |a Thayer's emotion model | ||
653 | |a artificial emotions | ||
653 | |a affective computing | ||
653 | |a n/a | ||
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856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/76782 |7 0 |z DOAB: description of the publication |