Integrated computational intelligent paradigm for nonlinear electric circuit models using neural networks, genetic algorithms and sequential quadratic programming

Abstract

In this paper, a novel application of biologically inspired computing paradigm is presented for solving initial value problem (IVP) of electric circuits based on nonlinear RL model by exploiting the competency of accurate modeling with feed forward artificial neural network (FF-ANN), global search efficacy of Genetic algorithms (GA), and rapid local search with sequential quadratic programming (SQP). The fitness function for IVP of associated nonlinear RL circuit is developed by exploiting the approximation theory in mean squared error sense using an approximate FF-ANN model. Training of the networks is conducted by integrated computational heuristic based on GA aided with SQP, i.e., GA-SQP. The designed methodology is evaluated to variants of nonlinear RL systems based on both AC and DC excitations for number of scenarios with different voltages, resistances and inductance parameters. The comparative studies of the proposed results with Adams numerical solutions in terms of various performance measures verify the accuracy of the scheme. Results of statistics based on Monte-Carlo simulations validate the accuracy, convergence, stability and robustness of the designed scheme for solving problem in nonlinear circuit theory.

Design of Neural Network With Levenberg-Marquardt and Bayesian Regularization Backpropagation for Solving Pantograph Delay Differential Equations

“…The AI based numerical approaches have been used broadly for solution of differential equations arising in diffrenet applications [26][27][28][29][30][31]. A few recent studies of paramount significance reported include Van-der-Pol oscillatory nonlinear systems [32][33], solution of mathematical model in nonlinear optics [34], models of electrically conducting solids [35][36], analysis of nonlinear reactive transport model [37], fuel ignition model in combustion theory [38], Jeffery Hamel flow models [39][40], thin film flow models [41][42], mathematical models involving Carbon nanotubes [43][44], astrophysics [45][46], nonlinear circuit theory models [47][48], dusty plasma [49][50], atomic physics [51][52], heartbeat dynamic models [53], HIV infection spread models [54][55], piezoelectric transducer modeling [56], energy [57][58], wind power [59][60], and financial models [61][62]. Beside these stiff nonlinear fractional dynamic modeling with Riccati fractional differential equations (FrDEs) [63][64] and Bagley-Torvik FrDEs [65] The organizational plan of this study comprises of the following: In the second section, necessary infor...…”

Design of Morlet Wavelet Neural Network for Solving a Class of Singular Pantograph Nonlinear Differential Models

“…It can be applied to optimize both constrained and unconstrained types of problems. Recently, GA is applied in many famous applications of heart diagnosis system [42], nonlinear electric circuit models [43], nonlinear astrophysics based singular system [44], Painlevé equation-II based nonlinear optic models [45], prediction model of air blast [46], Thomas Fermi model [47], and monorail vehicle system [48]. These potential applications of GA inspired the authors to solve the singular pantograph differential model to attain the decision variables of MWNNs.…”

Intelligent computing with Levenberg–Marquardt artificial neural networks for nonlinear system of COVID-19 epidemic model for future generation disease control

“…The strength of artificial intelligent (AI) based computing solvers has been exploited by the research community on large scale to obtain the approximated solutions of many problems arises in broad fields of applied science and technology. Some potential, recent reported studies having paramount significance including Van-der-Pol oscillatory systems, optics, electrically conducting solids, reactive transport system, nanofluidics, nanotechnology, fluid dynamics, astrophysics, circuit theory, plasma, atomic physics, bioinformatics, energy, power and functional mathematics see [24][25][26][27][28][29][30][31][32][33][34] and references cited therein. The said information is the motivational affinities to investigate in AI base numerical computing solver for the COVID-19 model.…”