Modelling and Simulation of Carts System &amp; Energy Employment

Rahman, Jamshaid Ul; Sana, Gul; Iqbal, Azhar

doi:10.18831/djphys.org/2018011001

Cited by 2 publications

(1 citation statement)

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“…However, understanding the dynamics of these systems is imperative for effectively designing and optimizing them to cater diverse applications [3]. Efficient modeling and simulation [4,5] techniques have a high demand in many fields including engineering, physics and biology to understand and predict the behavior of complex systems depending upon time [6,7]. Numerical techniques such as Euler's [8],…”

Section: Introductionmentioning

confidence: 99%

An Oscillatory Deep Neural Network for Coupled Electrical Circuits

Rahman,

Makhdoom,

Rashid

et al. 2024

Preprint

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Electronic systems share an indispensable role in almost every modern industry and are therefore continuously evolving into more advanced and complex versions. Consequently, such systems need to be tackled with some cutting-edge techniques. Among a number of analytical and numerical techniques of this era, Artificial Neural Networks (ANNs) have grabbed attention due to their universality and robustness on assigned tasks. In this work, an oscillatory Deep Neural Network (DNN) model has been proposed with an oscillatory activation function and specific layers’ structure to learn the dynamics of coupled LC-series circuits. The DNN model being suggested is flexible, easy to implement, and capable of diligently recovering the vibrating patterns of underlying dynamical systems. Outputs from the network are being compared with the results of LSODA numerical solvers. An error analysis for different time spans has also being performed, validating the successful recovery of solutions to the modeled problem, which is evident to the competency of proposed technique.

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Section: Introductionmentioning

confidence: 99%

An Oscillatory Deep Neural Network for Coupled Electrical Circuits

Rahman,

Makhdoom,

Rashid

et al. 2024

Preprint

View full text Add to dashboard Cite

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Mathematical modeling and simulation of biophysics systems using neural network

Rahman

Makhdoom

Ali

et al. 2023

Int. J. Mod. Phys. B

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Many of the real-life problems including dynamical structures can be modeled in the shape of differential equations. A number of analytic and numerical methods are being proposed for the solution of these differential equations but for some instances, we may come across a few limitations attached to them. However, theoretically strong and computationally favorable tools such as artificial neural networks can be utilized to approximate the solutions of these differential equations. In this work, we developed mathematical models for some biophysics systems on the basis of their dynamical behavior and opted a neural network having single hidden layer of 50 neurons and Broyden–Fletcher–Goldfarb–Shanno algorithm as an optimizer to simulate the results for population of micro-organisms. The graphical representations of the results obtained both from the neural network and analytic methods are compared for different parameters and we got almost the same results.

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Modelling and Simulation of Carts System & Energy Employment

Cited by 2 publications

References 0 publications

An Oscillatory Deep Neural Network for Coupled Electrical Circuits

An Oscillatory Deep Neural Network for Coupled Electrical Circuits

Mathematical modeling and simulation of biophysics systems using neural network

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