What are the most commonly used algorithms for nonlinear parameterized systems?
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The most commonly used algorithms for nonlinear parameterized systems include the Least Mean Square Algorithm (LMS), Recursive least square (RLS), Artificial Neural Network (ANN), Genetic Algorithm (GA), Differential Evolution (DE), and fuzzy algorithms. LMS, RLS, and ANN are derivative-based techniques that have been successfully employed for nonlinear system identification . GA is a derivative-free technique but takes more time to converge . DE, on the other hand, is an efficient and powerful population-based stochastic search technique that is expected to have superior performance in system identification . Fuzzy algorithms, such as type-1 fuzzy algorithm with parameterized conjunctors and interval type-2 fuzzy algorithm with parameterized conjunctors, are also commonly used for modeling nonlinear functions . These algorithms aim to preserve expert knowledge about the system during the optimization process .
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01 Dec 2011 | The paper does not mention the most commonly used algorithms for nonlinear parameterized systems. The paper is about the development of DE based adaptive techniques for nonlinear system identification. |
1 Citations | The paper does not mention the most commonly used algorithms for nonlinear parameterized systems. |
6 Citations | The paper does not mention the most commonly used algorithms for nonlinear parameterized systems. |
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