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Design of nature-inspired heuristic paradigm for systems in nonlinear electrical circuits

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Abstract

In the present study, a novel application of nature-inspired heuristics is presented for problems in nonlinear circuit analysis using neural networks, particle swarm optimization (PSO), and interior-point algorithm (IPA) as well as integrated approach PSO–IPA. The governing system models of resistor–capacitor circuits with nonlinear capacitance as well as resistor–inductor circuits with nonlinear inductance are mathematically modeled through competency of neural networks and weights of these networks are trained for global search with PSO hybrid with IPA for speedy refinements. The designed technique is applied on a number of scenarios by taking different values of resistance, current, voltage inductance, and capacitance parameters in nonlinear electrical circuit models. Comparative study with Adams numerical solvers having matching of the order 10−04–10−07 and consistently attaining near-optimal gauges of performance indices based on root-mean-squared error, Theil’s inequality coefficient, and Nash–Sutcliffe efficiency metrics validate and verify the efficacy of the scheme.

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References

  1. Feali MS, Ahmadi A (2017) Transient response characteristic of memristor circuits and biological-like current spikes. Neural Comput Appl 28(11):3295–3305

    Google Scholar 

  2. Pershin Y, Di Ventra M (2010) Practical approach to programmable analog circuits with memristors. IEEE Trans Circuits Syst I Regul Pap 57(8):1857–1864

    MathSciNet  Google Scholar 

  3. Fidler JK (1991) Analogue IC design: the current-mode approach. IEE Rev 37(1):33

    Google Scholar 

  4. Abuelma’Ati MT (2010) New two CFOA-based sinusoidal RC oscillators with buffered outlet. Analog Integr Circuit Signal Process 66(3):475–482

    Google Scholar 

  5. Abro KA, Memon AA, Uqaili MA (2018) A comparative mathematical analysis of RL and RC electrical circuits via Atangana–Baleanu and Caputo–Fabrizio fractional derivatives. Eur Phys J Plus 133(3):113

    Google Scholar 

  6. Abouzied M, Ravichandran K, Sanchez-Sinencio E (2017) A fully integrated reconfigurable self-startup RF energy-harvesting system with storage capability. IEEE J Solid-State Circuits 52(3):704–719

    Google Scholar 

  7. Zhan D, Xu Q, Huang D, Sun H, Gao F, Zhang F (2017) Effects of composition on dielectric properties of (Ba, Ca)(Zr, Ti)O3 ceramics for energy storage capacitors. J Electron Mater 46(7):4503–4511

    Google Scholar 

  8. Wolf D, Sanders S (1996) Multiparameter homotopy methods for finding DC operating points of nonlinear circuits. IEEE Trans Circuits Syst I Fundam Theory Appl 43(10):824–838

    MathSciNet  Google Scholar 

  9. Vazquez-Leal H, Boubaker K, Hernandez-Martinez L, Huerta-Chua J (2013) Approximation for transient of nonlinear circuits using RHPM and BPES methods. J Electr Comput Eng 2013:1–6

    MathSciNet  Google Scholar 

  10. Nourifar M, Sani A, Keyhani A (2017) Efficient multi-step differential transform method: theory and its application to nonlinear oscillators. Commun Nonlinear Sci Numer Simul 53:154–183

    MathSciNet  Google Scholar 

  11. Butcher J (1996) A history of Runge–Kutta methods. Appl Numer Math 20(3):247–260

    MathSciNet  MATH  Google Scholar 

  12. Lü X, Cui M (2010) Existence and numerical method for nonlinear third-order boundary value problem in the reproducing kernel space. Bound Value Probl 2010(1):459754

    MathSciNet  MATH  Google Scholar 

  13. Lodhi S, Manzar MA, Raja MAZ (2019) Fractional neural network models for nonlinear Riccati systems. Neural Comput Appl 31(Suppl 1):S359–S378

    Google Scholar 

  14. Raja MAZ, Samar R, Manzar MA, Shah SM (2017) Design of unsupervised fractional neural network model optimized with interior point algorithm for solving Bagley–Torvik equation. Math Comput Simul 132:139–158

    MathSciNet  Google Scholar 

  15. Munir A, Manzar MA, Khan NA, Raja MAZ (2019) Intelligent computing approach to analyze the dynamics of wire coating with Oldroyd 8-constant fluid. Neural Comput Appl 31(3):751–775

    Google Scholar 

  16. Mehmood A et al (2018) Intelligent computing to analyze the dynamics of magnetohydrodynamic flow over stretchable rotating disk model. Appl Soft Comput 67:8–28

    Google Scholar 

  17. Ahmad I et al (2018) Intelligent computing to solve fifth-order boundary value problem arising in induction motor models. Neural Comput Appl 29(7):449–466

    Google Scholar 

  18. Raja MAZ, Shah AA, Mehmood A, Chaudhary NI, Aslam MS (2018) Bio-inspired computational heuristics for parameter estimation of nonlinear Hammerstein controlled autoregressive system. Neural Comput Appl 29(12):1455–1474

    Google Scholar 

  19. Zameer A et al (2017) Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks. Energy Convers Manag 134:361–372

    Google Scholar 

  20. Sabir Z et al (2018) Neuro-heuristics for nonlinear singular Thomas–Fermi systems. Appl Soft Comput 65:152–169

    Google Scholar 

  21. Raja MAZ, Shah FH, Syam MI (2018) Intelligent computing approach to solve the nonlinear Van der Pol system for heartbeat model. Neural Comput Appl 30(12):3651–3675

    Google Scholar 

  22. Raja MAZ, Mehmood A, Niazi SA, Shah SM (2018) Computational intelligence methodology for the analysis of RC circuit modelled with nonlinear differential order system. Neural Comput Appl 30(6):1905–1924

    Google Scholar 

  23. Köksal M, Herdem S (2002) Analysis of nonlinear circuits by using differential Taylor transform. Comput Electr Eng 28(6):513–525

    MATH  Google Scholar 

  24. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Perth, Australia, vol 4. IEEE Service Center, Piscataway, pp 1942–1948

  25. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57

    Google Scholar 

  26. Raja MAZ (2014) Solution of the one-dimensional Bratu equation arising in the fuel ignition model using ANN optimised with PSO and SQP. Connect Sci 26(3):195–214

    MathSciNet  Google Scholar 

  27. Dara S, Banka H, Annavarapu C (2017) A rough based hybrid binary PSO algorithm for flat feature selection and classification in gene expression data. Ann Data Sci 4(3):341–360

    Google Scholar 

  28. Khan JA et al (2015) Design and application of nature inspired computing approach for nonlinear stiff oscillatory problems. Neural Comput Appl 26(7):1763–1780

    Google Scholar 

  29. Zhang Y, Wang S, Sui Y, Yang M, Liu B, Cheng H, Sun J, Jia W, Phillips P, Gorriz J (2018) Multivariate approach for Alzheimer’s disease detection using stationary wavelet entropy and predator–prey particle swarm optimization. J Alzheimer’s Dis 65(3):855–869

    Google Scholar 

  30. Raja MAZ, Samar R, Alaidarous ES, Shivanian E (2016) Bio-inspired computing platform for reliable solution of Bratu-type equations arising in the modeling of electrically conducting solids. Appl Math Model 40(11–12):5964–5977

    MathSciNet  MATH  Google Scholar 

  31. Raja MAZ, Samar R (2014) Solution of the 2-dimensional Bratu problem using neural network, swarm intelligence and sequential quadratic programming. Neural Comput Appl 25(7–8):1723–1739

    Google Scholar 

  32. Raja MAZ, Samar R, Rashidi MM (2014) Application of three unsupervised neural network models to singular nonlinear BVP of transformed 2D Bratu equation. Neural Comput Appl 25(7–8):1585–1601

    Google Scholar 

  33. Raja MAZ, Ahmad SI (2014) Numerical treatment for solving one-dimensional Bratu problem using neural networks. Neural Comput Appl 24(3–4):549–561

    Google Scholar 

  34. Karmarkar N (1984) A new polynomial time algorithm for linear programming. Combinatorica 4:373–395

    MathSciNet  MATH  Google Scholar 

  35. Wright SJ (1997) Primal–dual interior-point methods. SIAM, Philadelphia. ISBN 0-89871-382-X

    MATH  Google Scholar 

  36. Wright MH (2005) The interior-point revolution in optimization: history, recent developments, and lasting consequences. Bull Am Math Soc (NS) 42:39–56

    MathSciNet  MATH  Google Scholar 

  37. Yana W, Wenb L, Li W, Chunga CY, Wong KP (2011) Decomposition–coordination interior point method and its application to multi-area optimal reactive power flow. Int J Electr Power Energy Syst 33(1):55–60

    Google Scholar 

  38. Duvvuru N, Swarup KS (2011) A hybrid interior point assisted differential evolution algorithm for economic dispatch. IEEE Trans. Power Syst 26(2):541–549

    Google Scholar 

  39. Kheir NA, Holmes WM (1978) On validating simulation models of missile systems. Simulation 30(4):117–128

    Google Scholar 

  40. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10(3):282–290. https://doi.org/10.1016/0022-1694(70)90255-6

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan

    Ammara Mehmood

  2. Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan

    Aneela Zameer

  3. School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, Australia

    Muhammad Saeed Aslam

  4. Department of Electrical Engineering, COMSATS Institute of Information Technology, Attock Campus, Attock, Pakistan

    Muhammad Asif Zahoor Raja

Authors
  1. Ammara Mehmood
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  2. Aneela Zameer
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  3. Muhammad Saeed Aslam
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  4. Muhammad Asif Zahoor Raja
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Correspondence to Muhammad Saeed Aslam.

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Mehmood, A., Zameer, A., Aslam, M.S. et al. Design of nature-inspired heuristic paradigm for systems in nonlinear electrical circuits. Neural Comput & Applic 32, 7121–7137 (2020). https://doi.org/10.1007/s00521-019-04197-7

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  • DOI: https://doi.org/10.1007/s00521-019-04197-7

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