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

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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