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- W4213311168 abstract "Artificial neural network, as the basis of the continuous development of the current field of artificial intelligence, contains many types of algorithms, but most of them contain hidden layer. By understanding the prediction accuracy of the previous RBF neural network, it is easy to affect its own application efficiency and numerical changes due to the unsuitable central nodes of the randomly selected hidden layer. Therefore, under the background of new era, in order to better show the effectiveness of the application of artificial neural network, based on the understanding on the basis of application of RBF neural network, using the neighbor spread AP clustering algorithm to carry on the improvement and optimization, and after the new optimization method and its modeling process, according to the simulation analysis judgment finally optimization method is scientific and effective." @default.
- W4213311168 created "2022-02-24" @default.
- W4213311168 creator A5028772518 @default.
- W4213311168 date "2021-12-01" @default.
- W4213311168 modified "2023-10-16" @default.
- W4213311168 title "Study on Optimization Method of Hidden Layer Nodes and Training Times in Artificial Neural Network" @default.
- W4213311168 doi "https://doi.org/10.1109/iceitsa54226.2021.00081" @default.
- W4213311168 hasPublicationYear "2021" @default.
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