In this paper, a neural network-based procedure is suggested to produce estimated solutions (controllers) for the second-order nonlinear partial differential equations (PDEs). This concept is laid down so as to produce a prevalent approximation on the basis of the learning method which is at par with quasi-Newton rule. The proposed neural network contains the regularizing parameters (weights and biases), that can be utilized for making the error function least. Besides, an advanced technique is presented for resolving PDEs based on the usage of Bernstein polynomial. Numerical experiments alongside comparisons show the fantastic capacity of the proposed techniques.
|Title of host publication||Advances in Computational Intelligence Systems|
|Subtitle of host publication||Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK|
|Editors||Ahmad Lotfi, Hamid Bouchachia, Alexander Gegov, Caroline Langensiepen, Martin McGinnity|
|Publication status||Published - Sep 2018|
|Event||18th UK Workshop on Computational Intelligence - Nottingham, United Kingdom|
Duration: 5 Sep 2018 → 7 Sep 2018
|Name||Advances in Intelligent Systems and Computing|
|Workshop||18th UK Workshop on Computational Intelligence|
|Period||5/09/18 → 7/09/18|
- Neural Network
- Bernstein Polynomial
- Partial Differential Equations.