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• W4205173386 startingPage "140" @default.
• W4205173386 abstract "A radial basis function neural network (RBFNN), with a strong function approximation ability, was proven to be an effective tool for nonlinear process modeling. However, in many instances, the sample set is limited and the model evaluation error is fixed, which makes it very difficult to construct an optimal network structure to ensure the generalization ability of the established nonlinear process model. To solve this problem, a novel RBFNN with a high generation performance (RBFNN-GP), is proposed in this paper. The proposed RBFNN-GP consists of three contributions. First, a local generalization error bound, introducing the sample mean and variance, is developed to acquire a small error bound to reduce the range of error. Second, the self-organizing structure method, based on a generalization error bound and network sensitivity, is established to obtain a suitable number of neurons to improve the generalization ability. Third, the convergence of this proposed RBFNN-GP is proved theoretically in the case of structure fixation and structure adjustment. Finally, the performance of the proposed RBFNN-GP is compared with some popular algorithms, using two numerical simulations and a practical application. The comparison results verified the effectiveness of RBFNN-GP." @default.
• W4205173386 created "2022-01-25" @default.
• W4205173386 creator A5013498703 @default.
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• W4205173386 date "2022-01-10" @default.
• W4205173386 modified "2023-09-26" @default.
• W4205173386 title "A Novel Radial Basis Function Neural Network with High Generalization Performance for Nonlinear Process Modelling" @default.
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• W4205173386 doi "https://doi.org/10.3390/pr10010140" @default.
• W4205173386 hasPublicationYear "2022" @default.
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