Bifurcations of fuzzy nonlinear dynamical systems |
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Affiliation: | 1. School of Mathematics, South China University of Technology, Guangzhou 510641, People''s Republic of China;2. Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, People''s Republic of China;1. School of Mathematics and Physics, China University of Geosciences (Wuhan), Wuhan 430074, China;2. Zhejiang Institute, China University of Geosciences, Hangzhou, Zhejiang 311305, China;3. School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran 14399–57131, Iran;4. Department of Mechanical Engineering, University of Manitoba, Winnipeg R3T 5V6, Canada;5. Department of Computer Technologies, Vocational School of Karacabey, Bursa Uludag University, Karacabey 16700, Bursa, Turkey;6. Mathematical Institute, University of Oxford, Oxford OX2 6GG, England |
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Abstract: | We present some recent developments of the fuzzy generalized cell mapping method (FGCM) in this paper. The topological property of the FGCM and its finite convergence of membership distribution vector are discussed. Powerful algorithms of digraphs are adopted for the analysis of topological properties of the FGCM systems. Bifurcations of fuzzy nonlinear dynamical systems are studied by using the FGCM method. A backward algorithm is introduced to study the unstable equilibrium solutions and their bifurcation. We have found that near the deterministic bifurcation point, the fuzzy system undergoes a complex transition as the control parameter varies. In this transition region, the steady state membership distribution is dependent on the initial condition. If we use the measure and topology of the α-cut (α = 1) of the steady state membership function of the persistent group representing the stable fuzzy equilibrium solution to characterize the fuzzy bifurcation, assuming the uniform initial condition within the persistent group, the bifurcation of the fuzzy dynamical system is then completed within an interval of the control parameter, rather than at a point as is the case of deterministic systems. |
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