Numerical treatment of nonlinear singular Flierl–Petviashivili systems using neural networks models

2017 ◽  
Vol 31 (7) ◽  
pp. 2371-2394 ◽  
Author(s):  
Muhammad Asif Zahoor Raja ◽  
Junaid Ali Khan ◽  
Aneela Zameer ◽  
Najeeb Alam Khan ◽  
Muhammad Anwaar Manzar
Keyword(s):  
Neural Networks ◽  
Numerical Treatment

Numerical treatment for solving one-dimensional Bratu problem using neural networks

2012 ◽  
Vol 24 (3-4) ◽  
pp. 549-561 ◽  
Author(s):  
Muhammad Asif Zahoor Raja ◽  
Siraj-ul-Islam Ahmad
Keyword(s):  
Neural Networks ◽  
Numerical Treatment ◽  
One Dimensional ◽  
Bratu Problem

Numerical Treatment for Painlevé Equation I Using Neural Networks and Stochastic Solvers

2013 ◽  
pp. 103-117 ◽  
Author(s):  
Muhammad Asif Zahoor Raja ◽  
Junaid Ali Khan ◽  
Siraj-ul-Islam Ahmad ◽  
Ijaz Mansoor Qureshi
Keyword(s):  
Neural Networks ◽  
Painlevé Equation ◽  
Numerical Treatment ◽  
Painleve Equation

Stochastic numerical treatment for solving Falkner–Skan equations using feedforward neural networks

2016 ◽  
Vol 28 (S1) ◽  
pp. 1131-1144 ◽  
Author(s):  
Iftikhar Ahmad ◽  
Siraj-ul-Islam Ahmad ◽  
Muhammad Bilal ◽  
Nabeela Anwar
Keyword(s):  
Neural Networks ◽  
Feedforward Neural Networks ◽  
Numerical Treatment

scholarly journals Competency of Neural Networks for the Numerical Treatment of Nonlinear Host-Vector-Predator Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zulqurnain Sabir ◽  
Muhammad Umar ◽  
Ghulam Mujtaba Shah ◽  
Hafiz Abdul Wahab ◽  
Yolanda Guerrero Sánchez
Keyword(s):  
Neural Networks ◽  
Mean Square ◽  
Numerical Treatment ◽  
Testing Sample ◽  
Sample Data ◽  
Different Types ◽  
The Mean ◽  
Predator Model ◽  
Regression Correlation ◽  
And Training

The aim of this work is to introduce a stochastic solver based on the Levenberg-Marquardt backpropagation neural networks (LMBNNs) for the nonlinear host-vector-predator model. The nonlinear host-vector-predator model is dependent upon five classes, susceptible/infected populations of host plant, susceptible/infected vectors population, and population of predator. The numerical performances through the LMBNN solver are observed for three different types of the nonlinear host-vector-predator model using the authentication, testing, sample data, and training. The proportions of these data are chosen as a larger part, i.e., 80% for training and 10% for validation and testing, respectively. The nonlinear host-vector-predator model is numerically treated through the LMBNNs, and comparative investigations have been performed using the reference solutions. The obtained results of the model are presented using the LMBNNs to reduce the mean square error (MSE). For the competence, exactness, consistency, and efficacy of the LMBNNs, the numerical results using the proportional measures through the MSE, error histograms (EHs), and regression/correlation are performed.

Determination of S-N curves with the application of artificial neural networks

1999 ◽  
Vol 22 (8) ◽  
pp. 723-728 ◽  
Author(s):  
Artymiak ◽  
Bukowski ◽  
Feliks ◽  
Narberhaus ◽  
Zenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural

Review of Pattern Recognition by Self-Organizing Neural Networks.

1995 ◽  
Vol 40 (11) ◽  
pp. 1110-1110
Author(s):  
Stephen James Thomas
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Self Organizing
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