- 期刊
Intelligent Forecasting System Using Grey Model Combined with Neural Network
並列摘要
This paper proposes an intelligent forecasting system based on a feedforward-neural-network-aided grey model (FNAGM), which integrates a first-order single variable grey model (GM(1,1)) and a feedforward neural network. There are three phases in the system process, including initialization phase, GM(1,1) prediction phase and FNAGM prediction phase. First, some parameters required in the FNAGM are chosen in the initialization phase. Then, a one-step-ahead predictive value is generated in the GM(1,1) prediction phase. Finally, a feedforward neural network is used to learn the prediction error of the GM(1,1) and compensate it in the FNAGM prediction phase. Significantly, an on-line batch training is adopted to adjust the network according to the Levenberg-Marquardt algorithm in real-time. From the simulation results, the proposed intelligent forecasting system indeed improves the prediction error of the GM(1,1) and obtains more accurate prediction than other numerical methods.
被引用紀錄
延伸閱讀
- Sun, Y. (2014). A Hybrid Approach by Integrating Brain Storm Optimization Algorithm with Grey Neural Network for Stock Index Forecasting. Abstract and Applied Analysis, 2014(), 861-870-1292. https://doi.org/10.1155/2014/759862
- 陳昱斳、陳文輝（2018）。The Study of Short-Term Load Forecasting Using Deep Neural Networks。先進工程學刊，13(3)，131-136。https://www.airitilibrary.com/Article/Detail?DocID=a0000255-201810-201901090012-201901090012-131-136
- 鄭勝文（2011）。Short-Term Load Forecasting Based on Improved Grey Prediction Approach〔碩士論文，國立臺北科技大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0006-2101201116592000
- 吳菁菁（1992）。A Multilayer Neural Network Model for Short-Term Load Forecasting〔碩士論文，元智大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0009-0112200611291262
- Liu, L., Wang, Q., Liu, M., & Li, L. (2014). An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic Dataset. Abstract and Applied Analysis, 2014(), 708-717-1070. https://doi.org/10.1155/2014/641514