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

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 e...

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Bibliographic Details
Published in:Neural computing & applications 2020-07, Vol.32 (14), p.10337-10357
Main Authors: Mehmood, Ammara, Zameer, Aneela, Ling, Sai Ho, Rehman, Ata ur, Raja, Muhammad Asif Zahoor
Format: Article
Language:English
Subjects:
Accuracy
Artificial Intelligence
Artificial neural networks
Boundary value problems
Circuit design
Circuits
Comparative studies
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer simulation
Data Mining and Knowledge Discovery
Genetic algorithms
Image Processing and Computer Vision
Inductance
Monte Carlo simulation
Neural networks
Nonlinear systems
Original Article
Probability and Statistics in Computer Science
Quadratic programming
RL circuits
Robustness (mathematics)
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Summary: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 Adam’s 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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04573-3