TWD-SFNN: Three-way decisions with a single hidden layer feedforward neural network

Neural networks have a strong self-learning ability and a wide range of applications. The current neural network models mainly determine the number of hidden layer nodes using empirical formulas, which lack theoretical guidance and can easily lead to poor learning performance. To improve the performance of the neural network model, inspired by the three-way decisions method, this paper proposes a model called three-way decisions with a single hidden layer feedforward neural network (TWD-SFNN). TWD-SFNN adopts threeway decisions to find the number of hidden layer nodes for a neural network in a dynamic way. TWD-SFNN has three key issues: discretizing the datasets, adjusting the learning process of the network, and evaluating the learning results of the network. TWD-SFNN adopts the k-means++ algorithm to discretize the datasets, employs the Adam algorithm to adjust the learning process of the network, and uses a confusion matrix to evaluate the learning results of the network. Therefore, the topological structure of the neural network is obtained. The experimental results verify that the network structure of TWD-SFNN is more compact than those of the SFNN models that use empirical formulas to determine the number of hidden layer nodes, and the generalization ability of TWD-SFNN is better than the state-of-the-art classification models. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

The paper introduces a new neural network model, TWD-SFNN, to enhance the learning performance and generalization ability of neural networks. By dynamically determining the number of hidden layer nodes using the three-way decisions method, the model outperforms traditional models according to experimental results.