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9 October 2023 An online modeling approach based upon improved self-organizing radial basis function neural network
Dong Deng, Qibing Jin, Yang Zhang
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Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127912H (2023) https://doi.org/10.1117/12.3005154
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China
Abstract
In this paper, we propose an improved online self-organizing radial basis function neural network (IOS-RBF) modeling method which can dynamically add or merge hidden neuron while tuning the parameters. First, the initial center of the network is determined using the sample output clustering method, which is able to utilize the prior information contained in the output data. Second, a new neuron self-organization adjustment strategy is proposed, which is able to dynamically optimize the network structure according to the generalization ability of the network and the correlation between the nodes, and then, the network parameters are trained using the sliding window Levenberg-Marquardt (LM) algorithm to accelerate the network convergence speed. Finally, the effectiveness of IOS-RBF is demonstrated by two benchmark simulation experiments.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Dong Deng, Qibing Jin, and Yang Zhang "An online modeling approach based upon improved self-organizing radial basis function neural network", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127912H (9 October 2023); https://doi.org/10.1117/12.3005154
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KEYWORDS
Neural networks
Modeling
Neurons
Systems modeling
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