An online self-organizing radial basis function neural network based on Gaussian Membership

Radial basis function neural network (RBFNN) is one of the most popular neural networks, and an appropriate selection of its structure and learning algorithms is crucial for its performance. Aiming to alleviate the sensitivity of the RBFNN to its parameters and improve the overall performance of the...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Vol. 55; no. 7; p. 523
Main Authors: Jia, Lijie, Li, Wenjing, Qiao, Junfei, Zhang, Xinliang
Format: Journal Article
Language:English
Published: New York Springer US 01.04.2025
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Convergence
Machine learning
Machines
Manufacturing
Mechanical Engineering
Neural networks
Nonlinear systems
Parameter sensitivity
Performance enhancement
Processes
Radial basis function
Similarity
Gaussian Membership
Online fixed mini-batch gradient algorithm
Self-organizing algorithm
Radial basis function neural network
ISSN:0924-669X, 1573-7497
Online Access:Get full text
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Summary:Radial basis function neural network (RBFNN) is one of the most popular neural networks, and an appropriate selection of its structure and learning algorithms is crucial for its performance. Aiming to alleviate the sensitivity of the RBFNN to its parameters and improve the overall performance of the network, this study proposes a Gaussian Membership-based online self-organizing RBF neural network (GM-OSRBFNN). First, the Gaussian Membership is introduced to enhance network insensitivity to network parameters and used as a similarity metric to indicate the similarity between the sample to a hidden neuron and that between hidden neurons. Second, the similarity metric is used to design the neuron addition and merging rules to achieve a self-organizing network structure, and error constraints are introduced to the neuron addition rule; also, the noisy neuron deletion rule is defined to make the network structure more compact. In addition, an online fixed mini-batch gradient algorithm is used for online learning of network parameters, which can guarantee fast and stable convergence of the network. Finally, the proposed GM-OSRBFNN is tested on common nonlinear system modeling problems to verify its effectiveness. The experimental results show that compared to the existing models, the GM-OSRBFNN can achieve competitive prediction performance with a more compact network structure, faster convergence speed, and, more importantly, better insensitivity to network parameters.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-024-05989-8