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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Vydáno v:	Neural computing & applications Ročník 32; číslo 14; s. 10337 - 10357
Hlavní autoři:	Mehmood, Ammara, Zameer, Aneela, Ling, Sai Ho, Rehman, Ata ur, Raja, Muhammad Asif Zahoor
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.07.2020 Springer Nature B.V
Témata:	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) Sequential quadratic programming Nonlinear systems Nonlinear electric circuits Artificial neural networks Genetic algorithms
ISSN:	0941-0643, 1433-3058
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