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- W2352665826 abstract "A prediction model based on improved back propagation wavelet neural network is established for traffic congestion of wireless network optimal control problem.Considering that traditional back propagation wavelet neural network is easy to take local convergence and has slowly learning convergent velocity,a method based on adaptive learning rate is used to optimize it in accelerating the learning convergence velocity.To prevent the network into local minimum and the larger error,an improved back propagation wavelet neural network(IBPWNN) adjustments learning rate dynamic in the wavelet network during training.The speed and prediction precision of IBPWNN is compared with that of back propagation neural network(BPNN) of in the case of network traffic prediction.Theoretical analysis and simulation result show that IBPWNN is an effective algorithm.It not only is simple and efficient like BPWNN algorithm but also can increase the learning speed and prediction precision,which avoids the problem that BP neural network is easy to be trapped in local minima and its convergence speed is slow.It is an effective network optimization algorithm." @default.
- W2352665826 created "2016-06-24" @default.
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- W2352665826 date "2010-01-01" @default.
- W2352665826 modified "2023-09-28" @default.
- W2352665826 title "Study on Network Traffic Prediction Base on Improved Back Propagation Wavelet Neural Network" @default.
- W2352665826 hasPublicationYear "2010" @default.
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