A Sigma-Pi-Sigma Neural Network Model with Graph Regularity Term

Qianru Huang; Qingmei Dong; Yunlong Liu; Deqing Ji; Qinwei Fan

doi:10.54097/xwvpkd67

Authors

Qianru Huang
Qingmei Dong
Yunlong Liu
Deqing Ji
Qinwei Fan

DOI:

https://doi.org/10.54097/xwvpkd67

Keywords:

Sigma-Pi-Sigma Neural Network, Graph Regularization, Squared Error

Abstract

In recent years, Sigma-Pi-Sigma neural network (SPSNN) as a special kind of higher-order neural network has attracted wide attention for its fast convergence speed and good approximation ability. However, an inappropriate number of hidden layer neurons may also lead to model underfitting or overfitting, which affects the performance and generalization ability of the model. Therefore, we propose a Sigma-Pi-Sigma neural network with graph regularity by adding a graph regularity term to the network. The results show that the proposed algorithm performs well in terms of training accuracy, testing accuracy and efficiency.

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