@article {10.3844/ajassp.2009.89.92,
article_type = {journal},
title = {RBFNN Model for Predicting Nonlinear Response of Uniformly Loaded Paddle Cantilever },
author = {Abdullah, Abdullah H.},
volume = {6},
year = {2009},
month = {Jan},
pages = {89-92},
doi = {10.3844/ajassp.2009.89.92},
url = {https://thescipub.com/abstract/ajassp.2009.89.92},
abstract = {The Radial basis Function neural network (RBFNN) model has been developed for the prediction of nonlinear response for paddle Cantilever with built-in edges and different sizes, thickness and uniform loads. Learning data was performed by using a nonlinear finite element program, incremental stages of the nonlinear finite element analysis were generated by using 25 schemes of built paddle Cantilevers with different thickness and uniform distributed loads. The neural network model has 5 input nodes representing the uniform distributed load and paddle size, length, width and thickness, eight nodes at hidden layer and one output node representing the max. deflection response (1500×1 represent the deflection response of load). Regression analysis between finite element results and values predicted by the neural network model shows the least error.},
journal = {American Journal of Applied Sciences},
publisher = {Science Publications}
}