**August 22, 2022**. Read More

10.5120/ijca2020920568 |

Mohamed Loay Dahhan and Yasser Almoussa. Reducing the Complexity of the Multilayer Perceptron Network using the Loading Matrix. *International Journal of Computer Applications* 175(10):40-48, August 2020. BibTeX

@article{10.5120/ijca2020920568, author = {Mohamed Loay Dahhan and Yasser Almoussa}, title = {Reducing the Complexity of the Multilayer Perceptron Network using the Loading Matrix}, journal = {International Journal of Computer Applications}, issue_date = {August 2020}, volume = {175}, number = {10}, month = {Aug}, year = {2020}, issn = {0975-8887}, pages = {40-48}, numpages = {9}, url = {http://www.ijcaonline.org/archives/volume175/number10/31492-2020920568}, doi = {10.5120/ijca2020920568}, publisher = {Foundation of Computer Science (FCS), NY, USA}, address = {New York, USA} }

### Abstract

In this paper, Researchers present three models of a Multilayer Perceptron Network (MLPs) based on the Factor Analysis with Principal Components method (PC) to reduce the degree of complexity of the neural network. In the first model, a neural network was built with all the variables in the input layer. In the second model, the results of the FA were adopted instead of the basic variables in the input layer, and in the third model, the Loading matrix was used to determine the number of nodes in the hidden layer and the weights that are associated with the input layer. Then compared the results of the models by determining the number of network weights that reflect the complexity of the network, in addition to the time of building and training the model and the accuracy of classification. The results of applying the models to a hypothetical database for the purposes of scientific research titled Bank Marketing showed that the model that inserted the factors in the hidden layer and preserved the high loading factors only is the best model in terms of low degree of complexity and maintaining classification accuracy.

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### Keywords

Multilayer Perceptron Network MLP, Factor Analysis FA, Principle Component Analysis PCA, Complexity, Loading Matrix