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- W2391016811 abstract "For the problem that the fully coupled BP neural network suffers the slow convergence rate to solve the large scale complex problems,a structure model of function-dividing BP neural network architecture is presented.By using the physical characteristics of the RBF neurons,the input sample space is decomposed,and different sub-samples space is sent to different sub-module of BP neural network to learn automatically.Compared with the fully coupled BP neural network,the searching space of weight in the learning process of neural network is reduced,the learning speed and network's generalization performance are improved,and the characteristics of the human brain in the learning proces of knowledge accumulation are reflected.Experiments of 3D Mexican hat function approximation and two-spiral classification show that the neural network of function-dividing BP neural network can solve the problem that the fully coupled BP neural network can not solve perfectly." @default.
- W2391016811 created "2016-06-24" @default.
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- W2391016811 date "2011-01-01" @default.
- W2391016811 modified "2023-09-24" @default.
- W2391016811 title "Structure model of function-dividing design for BP neural network" @default.
- W2391016811 hasPublicationYear "2011" @default.
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