Muhammad Bilal
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Muhammad Bilal has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Iftikhar Ahmad
Department of Mathematics, University of Gujrat, Gujrat 50700, Pakistan
2 shared publications - Muhammad Asif Zahoor Raja
Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, R.O.C.
2 shared publications - Farooq Ashraf
Department of Mathematics, University of Gujrat, Gujrat, 50700, Pakistan
1 shared publication - Higinio Ramos
Scientific Computing Group, Universidad de Salamanca, Salamanca, Spain
1 shared publication - Muhammad Shoaib
Yuan Ze University, AI Center, Taoyuan 320, Taiwan
1 shared publication
Latest Publications
- 17 September 2020
- Published by Springer Nature in Neural Computing and Applications
- Vol. 33 (11) , 5753-5769
Integrated neuro-evolution-based computing solver for dynamics of nonlinear corneal shape model numerically
- 17 September 2020
- Published by Springer Nature in Neural Computing and Applications
- Vol. 33 (11) , 5753-5769
Integrated neuro-evolution-based computing solver for dynamics of nonlinear corneal shape model numerically
- Open Access
- 24 October 2016
- Published by Springer Nature in SpringerPlus
- Vol. 5 (1) , 1-23
Bio-inspired computational heuristics to study Lane–Emden systems arising in astrophysics model
This study reports novel hybrid computational methods for the solutions of nonlinear singular Lane–Emden type differential equation arising in astrophysics models by exploiting the strength of unsupervised neural network models and stochastic optimization techniques. In the scheme the neural network, sub-part of large field called soft computing, is exploited for modelling of the equation in an unsupervised manner. The proposed approximated solutions of higher order ordinary differential equation are calculated with the weights of neural networks trained with genetic algorithm, and pattern search hybrid with sequential quadratic programming for rapid local convergence. The results of proposed solvers for solving the nonlinear singular systems are in good agreements with the standard solutions. Accuracy and convergence the design schemes are demonstrated by the results of statistical performance measures based on the sufficient large number of independent runs.
- 24 October 2016
- Published by Springer Nature in SpringerPlus
- Vol. 5 (1) , 1-23
Open AccessBio-inspired computational heuristics to study Lane–Emden systems arising in astrophysics model
This study reports novel hybrid computational methods for the solutions of nonlinear singular Lane–Emden type differential equation arising in astrophysics models by exploiting the strength of unsupervised neural network models and stochastic optimization techniques. In the scheme the neural network, sub-part of large field called soft computing, is exploited for modelling of the equation in an unsupervised manner. The proposed approximated solutions of higher order ordinary differential equation are calculated with the weights of neural networks trained with genetic algorithm, and pattern search hybrid with sequential quadratic programming for rapid local convergence. The results of proposed solvers for solving the nonlinear singular systems are in good agreements with the standard solutions. Accuracy and convergence the design schemes are demonstrated by the results of statistical performance measures based on the sufficient large number of independent runs.