Modeling of Uncertain Nonlinear System With Z-Numbers
| Autor: | Satyam Paul, Raheleh Jafari, Alexander Gegov, Sina Razvarz |
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| Rok vydání: | 2021 |
| Předmět: |
Work (thermodynamics)
Artificial neural network Mathematics::General Mathematics Computer science MathematicsofComputing_NUMERICALANALYSIS Order (ring theory) 02 engineering and technology 01 natural sciences Fuzzy logic 010305 fluids & plasmas Nonlinear system ComputingMethodologies_PATTERNRECOGNITION ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Applied mathematics 020201 artificial intelligence & image processing ComputingMethodologies_GENERAL |
| DOI: | 10.4018/978-1-7998-3479-3.ch022 |
| Popis: | In order to model the fuzzy nonlinear systems, fuzzy equations with Z-number coefficients are used in this chapter. The modeling of fuzzy nonlinear systems is to obtain the Z-number coefficients of fuzzy equations. In this work, the neural network approach is used for finding the coefficients of fuzzy equations. Some examples with applications in mechanics are given. The simulation results demonstrate that the proposed neural network is effective for obtaining the Z-number coefficients of fuzzy equations. |
| Databáze: | OpenAIRE |
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