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The Helmholtz theorem confirms that any vector field can be decomposed into gradient and rotational field. The supply and transmission of energy occur during the propagation of electromagnetic wave accompanied by the variation of electromagnetic field, thus the dynamical oscillators and neurons can absorb and release energy in the presence of complex electromagnetic condition. Indeed, the energy in nonlinear circuit is often time-varying when the capacitor is charged or discharged, and the occurrence of electromagnetic induction is available. Those nonlinear oscillating circuits can be mapped into dynamical systems by using scale transformation. Based on mean field theory, the energy exchange and transmission between electronic field and magnetic field can be estimated by appropriate nonlinear dynamical equations for oscillating circuits. In this paper, we investigate the calculation of Hamilton energy for a class of dimensionless dynamical systems based on Helmholtz's theorem. Furthermore, the scale transformation can be used to develop dynamical equations for the realistic nonlinear oscillating circuit, so the Hamilton energy function could be obtained effectively. These results can be greatly useful for self-adaptively controlling dynamical systems.
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Keywords:
- Helmholtz theorem /
- energy function /
- oscillating system /
- chaotic system
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[2] Wang C N, Chu R T, Ma J, Huang L 2015 Complexity 21 370
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[4] Li Q D, Zeng H Z, Li J 2015 Nonlinear Dyn. 79 2295
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[9] Wu X Y, Ma J, Yuan L H, Liu Y 2014 Nonlinear Dyn. 75 113
[10] Babacan Y, Kaçar F, Grkan K 2016 Neurocomputing 203 86
[11] Li J J, Tang J, Ma J, Du M M, Wang R, Wu Y 2016 Sci. Rep. 6 32343
[12] Lv M, Ma J 2016 Neurocomputing 205 375
[13] Ma J, Qin H X, Song X L, Chu R T 2015 Int. J. Mod. Phys. B 29 1450239
[14] Song X L, Wang C N, Ma J, Tang J 2015 Sci. China:Technol. Sci. 58 1007
[15] Hindmarsh J L, Rose R M 1982 Nature 296 162
[16] Song X L, Jin W Y, Ma J 2015 Chin. Phys. B 24 128710
[17] Ma J, Tang J 2015 Sci. China:Technol. Sci. 58 2038
[18] Lv M, Ma J 2016 Nonlinear Dyn. 85 1479
[19] Kobe D H 1986 Am. J. Phys. 54 552
[20] Sarasola C, Torrealdea F J, d'Anjou A, Moujahid A, Graña M 2004 Phys. Rev. E 69 011606
[21] Pinto R D, Varona P, Volkovskii A R, Szcs A, Abarbanel H D I, Rabinovich M I 2000 Phys. Rev. E 62 2644
[22] Torrealdea F J, d'Anjou A, Graña M, Sarasola C 2006 Phys. Rev. E 74 011905
[23] Li F, Yao C G 2016 Nonlinear Dyn. 84 2305
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[1] Wang H Q, Yu L C, Chen Y 2009 Acta Phys. Sin. 58 5070 (in Chinese)[王慧巧, 俞连春, 陈勇2009物理学报 58 5070]
[2] Wang C N, Chu R T, Ma J, Huang L 2015 Complexity 21 370
[3] Wu H G, Bao B C, Liu Z, Xu, Q, Jiang P 2016 Nonlinear Dyn. 83 893
[4] Li Q D, Zeng H Z, Li J 2015 Nonlinear Dyn. 79 2295
[5] Liang Y, Yu D S, Chen H 2013 Acta Phys. Sin. 62 158501 (in Chinese)[梁燕, 于东升, 陈昊2013物理学报 62 158501]
[6] Li Z J, Zeng Y C, Tang Z P 2014 Acta Phys. Sin. 63 098510 (in Chinese)[李志军, 曾以成, 谭志平2014物理学报 63 098501]
[7] Neumann E, Pikovsky A 2003 Eur. Phys. J. B 34 293
[8] Ren G D, Tang J, Ma J, Xu Y 2015 Commun. Nonlinear Sci. Numer. Simulat. 29 170
[9] Wu X Y, Ma J, Yuan L H, Liu Y 2014 Nonlinear Dyn. 75 113
[10] Babacan Y, Kaçar F, Grkan K 2016 Neurocomputing 203 86
[11] Li J J, Tang J, Ma J, Du M M, Wang R, Wu Y 2016 Sci. Rep. 6 32343
[12] Lv M, Ma J 2016 Neurocomputing 205 375
[13] Ma J, Qin H X, Song X L, Chu R T 2015 Int. J. Mod. Phys. B 29 1450239
[14] Song X L, Wang C N, Ma J, Tang J 2015 Sci. China:Technol. Sci. 58 1007
[15] Hindmarsh J L, Rose R M 1982 Nature 296 162
[16] Song X L, Jin W Y, Ma J 2015 Chin. Phys. B 24 128710
[17] Ma J, Tang J 2015 Sci. China:Technol. Sci. 58 2038
[18] Lv M, Ma J 2016 Nonlinear Dyn. 85 1479
[19] Kobe D H 1986 Am. J. Phys. 54 552
[20] Sarasola C, Torrealdea F J, d'Anjou A, Moujahid A, Graña M 2004 Phys. Rev. E 69 011606
[21] Pinto R D, Varona P, Volkovskii A R, Szcs A, Abarbanel H D I, Rabinovich M I 2000 Phys. Rev. E 62 2644
[22] Torrealdea F J, d'Anjou A, Graña M, Sarasola C 2006 Phys. Rev. E 74 011905
[23] Li F, Yao C G 2016 Nonlinear Dyn. 84 2305
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