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- W2367733655 abstract "This paper presents a new algorithm for feed forward neural networks based on a new optimal target function constructed according to Young inequality in the conjugate of convex function. The optimal target function is a convex function to the weight values between neurons in different layers for aptotic outputs of hidden layers;and it is a convex function to outputs of hidden layers for aptotic weight values between neurons in different layers .The target function's optimal velocity is high,so it can boost learning efficiency of feed forward neural networks. Simulation shows that the new algorithm can accelerate the convergence rate and reduce the error compared with the existing algorithms such as backpropagation(BP) algorithm,BP algorithm with momentum factor and existing layer wise algorithms.It achieves more effective results for applying fast algorithm to deposit simulation forecasting." @default.
- W2367733655 created "2016-06-24" @default.
- W2367733655 creator A5073594880 @default.
- W2367733655 date "2004-01-01" @default.
- W2367733655 modified "2023-09-23" @default.
- W2367733655 title "A Convex Optimal Algorithm for Feed Forward Neural Networks and Its Simulation Application" @default.
- W2367733655 hasPublicationYear "2004" @default.
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