Computational intelligence methodology for the analysis of RC circuit modelled with nonlinear differential order system

In this study, we solve nonlinear initial value problems arising in circuit analysis by applying bio-inspired computational intelligence technique using feed-forward artificial neural networks (ANNs) optimized with genetic algorithms (GAs), sequential quadratic programming (SQP), and their combined...

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Bibliographic Details
Published in:Neural computing & applications Vol. 30; no. 6; pp. 1905 - 1924
Main Authors: Raja, Muhammad Asif Zahoor, Mehmood, Ammara, Niazi, Shahab Ahmad, Shah, Syed Muslim
Format: Journal Article
Language:English
Published: London Springer London 01.09.2018
Springer Nature B.V
Subjects:
Artificial Intelligence
Artificial neural networks
Boundary value problems
Capacitance
Circuits
Computation
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Energy conservation
Error detection
Genetic algorithms
Image Processing and Computer Vision
Intelligence
Mathematical models
Neural networks
Original Article
Parameter estimation
Performance evaluation
Performance measurement
Probability and Statistics in Computer Science
Quadratic programming
RC circuits
Runge-Kutta method
Statistical analysis
Hybrid computing
Electric circuits
Sequential quadratic programming
Artificial neural networks
Genetic algorithms
ISSN:0941-0643, 1433-3058
Online Access:Get full text
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Summary:In this study, we solve nonlinear initial value problems arising in circuit analysis by applying bio-inspired computational intelligence technique using feed-forward artificial neural networks (ANNs) optimized with genetic algorithms (GAs), sequential quadratic programming (SQP), and their combined scheme. The system of resister–capacitor (RC) circuit having nonlinear capacitance is mathematically modelled with unsupervised ANNs by defining an energy function in mean-square error (MSE) sense. The objectives are to minimize the MSE for which the parameters of the networks are estimated initially with GA-based global search and in steady state with SQP algorithm for efficient local search. We consider a set of scenarios to evaluate the performance of the proposed scheme for different resistance and capacitance values along with current variations in the nonlinear RC circuit system. The results are compared with well-established fully explicit Runge–Kutta numerical solver in order to verify the accuracy of the applied bio-inspired heuristics. To prove the worth of the scheme, a comprehensive statistical analysis is provided for the performance metrics based on root MSE, mean absolute error, Theil’s inequality coefficient, Nash–Sutcliffe efficiency, variance account for, and the coefficient of determination ( R 2 ).
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-016-2806-6