Open Access

ARTICLE

Alternative Method of Constructing Granular Neural Networks

by Yushan Yin¹, Witold Pedrycz^1,2, Zhiwu Li^1,*

1 School of Electro-Mechanical Engineering, Xidian University, Xi’an, 710071, China
2 Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6R 2V4, Canada

* Corresponding Author: Zhiwu Li. Email:

Computers, Materials & Continua 2024, 79(1), 623-650. https://doi.org/10.32604/cmc.2024.048787

Received 18 December 2023; Accepted 21 February 2024; Issue published 25 April 2024

Abstract

Utilizing granular computing to enhance artificial neural network architecture, a new type of network emerges—the granular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The ability to process both numerical and granular data, leading to improved interpretability. This paper proposes a novel design method for constructing GNNs, drawing inspiration from existing interval-valued neural networks built upon NNNs. However, unlike the proposed algorithm in this work, which employs interval values or triangular fuzzy numbers for connections, existing methods rely on a pre-defined numerical network. This new method utilizes a uniform distribution of information granularity to granulate connections with unknown parameters, resulting in independent GNN structures. To quantify the granularity output of the network, the product of two common performance indices is adopted: The coverage of numerical data and the specificity of information granules. Optimizing this combined performance index helps determine the optimal parameters for the network. Finally, the paper presents the complete model construction and validates its feasibility through experiments on datasets from the UCI Machine Learning Repository. The results demonstrate the proposed algorithm’s effectiveness and promising performance.

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