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- W3091898731 abstract "This article describes the novel application of Diagonal Recurrent Neural Network (DRNN) for solving the sales forecasting problem. The proposed model is having temporal features since it consists of feedback adjustable connections that provide it with the memory feature. Gradient descent based Dynamic Back-Propagation (DBP) method is applied for generating the tuning equations. The performance evaluation of the results obtained from the proposed model is compared with that of the well-known models such as Multi-Layered Perceptron (MLP) containing single hidden layer, Radial Basis Function Network (RBFN) and Legendre-Functional Link Neural Network (LeFLNN). The simulation results obtained shows the superior performance of the DRNN model over the other considered neural network models." @default.
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- W3091898731 date "2020-10-09" @default.
- W3091898731 modified "2023-09-26" @default.
- W3091898731 title "Diagonal Recurrent Neural Network Based Prediction Model for Sales Forecasting" @default.
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- W3091898731 doi "https://doi.org/10.1007/978-981-15-7571-6_29" @default.
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