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- W2756537853 abstract "Time series forecasting plays a vital role in stable developments of economy and industry, the precaution of natural hazard, even adjustment of energy policy. Back Propagation Neural Network (BPNN) is always viewed as a predictor which powers in dealing with nonlinear problems associated with time series forecasting. However, BPNN easily falls into local minimum. To overcome this disadvantage, a novel hybrid approach called RBM-BSASA-BP is presented. Restricted Boltzmann Machine (RBM) firstly provides rough parameters for BPNN via pre-training process. The improved Backtracking Search Algorithm with Simulated Annealing (BSASA) is then employed to fine-tune the parameters for the purpose of seeking out the optimal weights and biases. Finally, optimized BPNN is utilized to accomplish forecasting tasks. Verified by artificial time series and real-world time series, the proposed hybrid approach defeats the state-of-the-art methods and it is proved to be a promising forecasting algorithm." @default.
- W2756537853 created "2017-10-06" @default.
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- W2756537853 date "2017-08-01" @default.
- W2756537853 modified "2023-09-27" @default.
- W2756537853 title "RBM-Based Back Propagation Neural Network with BSASA Optimization for Time Series Forecasting" @default.
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- W2756537853 doi "https://doi.org/10.1109/ihmsc.2017.163" @default.
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