Comparison of three unsupervised neural network models for first Painlevé Transcendent
Published 2014 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Comparison of three unsupervised neural network models for first Painlevé Transcendent
Authors
Keywords
Painlevé Transcendents, Artificial neural network, Sequential quadratic programming, Nonlinear differential equations, Activation functions, Unsupervised learning
Journal
NEURAL COMPUTING & APPLICATIONS
Volume 26, Issue 5, Pages 1055-1071
Publisher
Springer Nature
Online
2014-12-12
DOI
10.1007/s00521-014-1774-y
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Exactly satisfying initial conditions neural network models for numerical treatment of first Painlevé equation
- (2015) Muhammad Asif Zahoor Raja et al. APPLIED SOFT COMPUTING
- Stochastic numerical treatment for thin film flow of third grade fluid using unsupervised neural networks
- (2015) Muhammad Asif Zahoor Raja et al. Journal of the Taiwan Institute of Chemical Engineers
- Numerical treatment for boundary value problems of Pantograph functional differential equation using computational intelligence algorithms
- (2014) Muhammad Asif Zahoor Raja APPLIED SOFT COMPUTING
- Unsupervised neural networks for solving Troesch's problem
- (2014) Muhammad Asif Zahoor Raja Chinese Physics B
- Solution of the one-dimensional Bratu equation arising in the fuel ignition model using ANN optimised with PSO and SQP
- (2014) Muhammad Asif Zahoor Raja CONNECTION SCIENCE
- Stochastic numerical treatment for solving Troesch’s problem
- (2014) Muhammad Asif Zahoor Raja INFORMATION SCIENCES
- Numerical treatment of nonlinear MHD Jeffery–Hamel problems using stochastic algorithms
- (2013) Muhammad Asif Zahoor Raja et al. COMPUTERS & FLUIDS
- Numerical treatment for nonlinear MHD Jeffery–Hamel problem using neural networks optimized with interior point algorithm
- (2013) Muhammad Asif Zahoor Raja et al. NEUROCOMPUTING
- Neural network optimized with evolutionary computing technique for solving the 2-dimensional Bratu problem
- (2012) Muhammad Asif Zahoor Raja et al. NEURAL COMPUTING & APPLICATIONS
- Numerical treatment for solving one-dimensional Bratu problem using neural networks
- (2012) Muhammad Asif Zahoor Raja et al. NEURAL COMPUTING & APPLICATIONS
- A new stochastic approach for solution of Riccati differential equation of fractional order
- (2011) Muhammad Asif Zahoor Raja et al. ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE
- Numerical treatment of nonlinear Emden–Fowler equation using stochastic technique
- (2011) Junaid Ali Khan et al. ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE
- Novel Approach for a van der Pol Oscillator in the Continuous Time Domain
- (2011) Junaid Ali Khan et al. CHINESE PHYSICS LETTERS
- Stochastic Computational Approach for Complex Nonlinear Ordinary Differential Equations
- (2011) Junaid Ali Khan et al. CHINESE PHYSICS LETTERS
- Multilayer perceptrons and radial basis function neural network methods for the solution of differential equations: A survey
- (2011) Manoj Kumar et al. COMPUTERS & MATHEMATICS WITH APPLICATIONS
- Solution of Fractional Order System of Bagley-Torvik Equation Using Evolutionary Computational Intelligence
- (2011) Muhammad Asif Zahoor Raja et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- On tronquée solutions of the first Painlevé hierarchy
- (2010) Dan Dai et al. JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS
- Unsupervised adaptive neural-fuzzy inference system for solving differential equations
- (2009) Hadi Sadoghi Yazdi et al. APPLIED SOFT COMPUTING
- Solving initial-boundary value problems for systems of partial differential equations using neural networks and optimization techniques
- (2009) R. Shekari Beidokhti et al. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreDiscover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversation