Journal: Nanomaterials

Article Title: Quantitative Features Analysis of Water Carrying Nanoparticles of Alumina over a Uniform Surface

doi: 10.3390/nano12050878

Figure Lengend Snippet: Showing the model which is generated through nftool command in matlab window.

Article Snippet: We train a neural network for each case by using the “nftool” command in the Matlab environment, setting the input and output data for the network, and using the LMB-NN method to find the best solution for the present system of ODEs.

Techniques: Generated

Journal: Polymers

Article Title: Short-Beam Shear Strength of New-Generation Glass Fiber-Reinforced Polymer Bars Under Harsh Environment: Experimental Study and Artificial Neural Network Prediction Model.

doi: 10.3390/polym16233358

Figure Lengend Snippet: Figure 21. Structure of ANN used in MATLAB (w = weight; b = bias).

Article Snippet: In the final testing phase, a separate test subset is used to objecti l of t e fully trained model. For this study, the neural network toolbox in MATLAB (“nftool”) was employ d t develop a feed-forward back-propagation model using the Bay sian regularization algorithm.

Techniques:

Journal: Nanomaterials

Article Title: Quantitative Features Analysis of Water Carrying Nanoparticles of Alumina over a Uniform Surface

doi: 10.3390/nano12050878

Figure Lengend Snippet: Showing the model which is generated through nftool command in matlab window.

Article Snippet: The model we obtained for our problem of the neural networks using the nftool command in the Matlab window is shown in .

Techniques: Generated