Abstract
This chapter applies and compares three computational intelligence algorithms—genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO)—to maximize the positive Lyapunov exponent in a multiscroll chaotic oscillator based on a saturated nonlinear function series based on the modification of the standard settings of the coefficient values of the mathematical description, and taking into account the correct distribution of the scrolls drawing the phase-space diagram. The experimental results show that the DE and PSO algorithms help to maximize the positive Lyapunov exponent of truncated coefficients over the continuous spaces.
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Acknowledgments
The first author wants to thank CONACyT-Mexico for the scholarship 331697. This work has been partially supported by CONACyT-Mexico under grant 131839-Y, in part by the TEC2013-45638-C3-3-R, funded by the Spanish Ministry of Economy and Competitiveness and ERDF, by the P12-TIC-1481 project, funded by Junta de Andalucia, and by CSIC project PIE 201350E058.
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Carbajal-Gómez, V.H., Tlelo-Cuautle, E., Fernández, F.V. (2015). Application of Computational Intelligence Techniques to Maximize Unpredictability in Multiscroll Chaotic Oscillators. In: Fakhfakh, M., Tlelo-Cuautle, E., Siarry, P. (eds) Computational Intelligence in Analog and Mixed-Signal (AMS) and Radio-Frequency (RF) Circuit Design. Springer, Cham. https://doi.org/10.1007/978-3-319-19872-9_3
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DOI: https://doi.org/10.1007/978-3-319-19872-9_3
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