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- W2388994550 abstract "In order to design a radial basis function neural network(RBFNN) with good approximate capability,an adaptive hybrid learning algorithm with two-level hierarchy is proposed.At the lower level in this method,intelligence optimization algorithm combined with decaying gradient descent information is used to training parameters of RBFNN with fixed structure,which integrates the advantages of gradient descend method and intelligence optimization method.At the upper level,hidden nodes number is adjusted dynamically used an optimal stopping rule based on the result from lower level process,which guarantee the result to converge at the global optimum with high probability.Structure adjusting operators are added to realize a more parsimonious network.At last,realization details are provided,and simulation results show the effectiveness of the proposed method." @default.
- W2388994550 created "2016-06-24" @default.
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- W2388994550 date "2010-01-01" @default.
- W2388994550 modified "2023-09-23" @default.
- W2388994550 title "Adaptive Hybrid Learning Algorithm for Radial Basis Function Neural Network" @default.
- W2388994550 hasPublicationYear "2010" @default.
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