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Saddle-node bifurcation parameter detection strategy with nested-layer particle swarm optimization.
- Source :
-
Chaos, Solitons & Fractals . Feb2019, Vol. 119, p126-134. 9p. - Publication Year :
- 2019
-
Abstract
- Highlights • NLPSO is a non-Lyapunov bifurcation parameter detection strategy. • Problems in detecting saddle-node bifurcation parameters by NLPSO were clarified. • The problems were solved by imposing a simple condition on the objective function. • The extended NLPSO accurately detected saddle-node bifurcation parameters. Abstract Nested-layer particle swarm optimization (NLPSO) detects bifurcation parameters in discrete-time dynamical systems. Previous studies have proven the effectiveness of NLPSO for period-doubling bifurcations, but not for other bifurcation phenomena. This paper demonstrates that NLPSO can effectively detect saddle-node bifurcations. Problems in detecting saddle-node bifurcation parameters by conventional NLPSO are clarified, and are solved by imposing a simple condition on the NLPSO objective function. Under this conditional objective function, the NLPSO accurately detected both saddle-node and period-doubling bifurcation parameters regardless of their stability, without requiring careful initialization, exact calculations or Lyapunov exponents. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09600779
- Volume :
- 119
- Database :
- Academic Search Index
- Journal :
- Chaos, Solitons & Fractals
- Publication Type :
- Periodical
- Accession number :
- 134380225
- Full Text :
- https://doi.org/10.1016/j.chaos.2018.12.016