Back propagation neural network with adaptive differential evolution algorithm for time series forecasting
L Wang, Y Zeng, T Chen - Expert Systems with Applications, 2015 - Elsevier
L Wang, Y Zeng, T Chen
Expert Systems with Applications, 2015•ElsevierThe back propagation neural network (BPNN) can easily fall into the local minimum point in
time series forecasting. A hybrid approach that combines the adaptive differential evolution
(ADE) algorithm with BPNN, called ADE–BPNN, is designed to improve the forecasting
accuracy of BPNN. ADE is first applied to search for the global initial connection weights and
thresholds of BPNN. Then, BPNN is employed to thoroughly search for the optimal weights
and thresholds. Two comparative real-life series data sets are used to verify the feasibility …
time series forecasting. A hybrid approach that combines the adaptive differential evolution
(ADE) algorithm with BPNN, called ADE–BPNN, is designed to improve the forecasting
accuracy of BPNN. ADE is first applied to search for the global initial connection weights and
thresholds of BPNN. Then, BPNN is employed to thoroughly search for the optimal weights
and thresholds. Two comparative real-life series data sets are used to verify the feasibility …
Abstract
The back propagation neural network (BPNN) can easily fall into the local minimum point in time series forecasting. A hybrid approach that combines the adaptive differential evolution (ADE) algorithm with BPNN, called ADE–BPNN, is designed to improve the forecasting accuracy of BPNN. ADE is first applied to search for the global initial connection weights and thresholds of BPNN. Then, BPNN is employed to thoroughly search for the optimal weights and thresholds. Two comparative real-life series data sets are used to verify the feasibility and effectiveness of the hybrid method. The proposed ADE–BPNN can effectively improve forecasting accuracy relative to basic BPNN, autoregressive integrated moving average model (ARIMA), and other hybrid models.
Elsevier
