Artificial Cooperative Search Algorithm for Parameter Identification of Chaotic Systems
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Date
2015Author
Turgut, Oguz Emrah
Turgut, Mert Sinan
Coban, Mustafa Turhan
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Parameter estimation of chaotic systems is a challenging and critical topic in nonlinear science. Problem
at hand is multi-dimensional and highly nonlinear thereof conventional optimization methods generally
fail to extract the unknown parameters of chaotic system. In this study, Artificial Cooperative Search
algorithm is put into practice for successful parameter estimation of chaotic systems and compared the
parameter estimation performance of Artificial Cooperative Search with Bat, Artificial Bee Colony,
Quantum behaved Particle Swarm Optimization algorithms. Parameter identification performance of
each algorithm is outlined and benchmarked with several numerical simulations including Lörenz system,
Duffing equation and Josephson junction. Results show that Artificial Cooperative Search algorithm
outperforms other algorithms in terms of robustness and effectiveness.
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