Limit cycle oscillation and dynamical scenarios in piecewise-smooth nonlinear systems with two-sided constraints

Cao, Dong-Xing; Zhou, Xin-Xing; Guo, Xiang-Ying; Song, Ni

doi:10.1007/s11071-024-09589-6

Nonlinear Dyn

2024

DOI: 10.1007/s11071-024-09589-6

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Limit cycle oscillation and dynamical scenarios in piecewise-smooth nonlinear systems with two-sided constraints

Dong-Xing Cao,

Xin-Xing Zhou,

Xiang-Ying Guo

et al.

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Cited by 2 publications

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Note on prescribed-time stability of impulsive piecewise-smooth differential systems and application in networks

Li,

Zhang,

Feng

et al. 2024

NHM

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<p>We explored the prescribed-time stability (PTSt) of impulsive piecewise smooth differential systems (IPSDS) based on the Lyapunov theory and set-valued analysis technology, allowing flexibility in selecting the settling time as desired. Furthermore, by developing a feedback controller, we employed the theoretical results to evaluate the synchronization behavior of impulsive piecewise-smooth network systems (IPSNS) within a prescribed time frame and obtained novel criteria to guarantee the synchronization objective. A numerical example was presented to validate the accuracy of the results.</p>

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Note on prescribed-time stability of impulsive piecewise-smooth differential systems and application in networks

Li,

Zhang,

Feng

et al. 2024

NHM

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New energy output prediction and demand response optimization based on LSTM-BN

Wang,

Shi,

Nan

et al. 2024

Int. J. Renew. Energy Dev.

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The study proposed a new energy output prediction model based on long and short-term memory network (LSTM)-Bayesian network (BN) by combining the benefits of BN in uncertainty quantification with the processing power of LSTM network to address the issue of volatility and uncertainty of new energy output. Meanwhile, by introducing a price-based demand response mechanism, users were incentivized to increase electricity consumption when the new energy generation was in excess and reduce electricity consumption during the peak period, so as to realize the flexible regulation of loads and the efficient utilization of new energy. The new energy output prediction model developed in the study had the highest degree of match between the anticipated and actual values in various data sets, as demonstrated by the experimental findings, which were above 0.99. In the Google Earth Engin and GEFCom2014 datasets, the operation solution speed was quick and stabilized after 64 and 80 iterations, respectively. Additionally, the model’s predicted and actual curve values almost matched, and the actual new energy output power predication's largest prediction error was less than 1%. The implementation of a price-based demand response approach to control customers' power consumption behavior yielded a net benefit of up to 4.45 million yuan for the customers in the target area, based on the precise prediction of new energy output power. The aforementioned findings demonstrated that the LSTM-BN-based new energy output prediction model is capable of precisely projecting new energy output and efficiently matching supply and demand through a price-based demand response mechanism to increase the rate at which new energy is consumed instantly.

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Limit cycle oscillation and dynamical scenarios in piecewise-smooth nonlinear systems with two-sided constraints

Cited by 2 publications

References 54 publications

Note on prescribed-time stability of impulsive piecewise-smooth differential systems and application in networks

Note on prescribed-time stability of impulsive piecewise-smooth differential systems and application in networks

New energy output prediction and demand response optimization based on LSTM-BN

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