NDLI: The Multi-Rule & Real-Time Training Neural Network Model for Time Series Forecasting Problem

Content Provider	IEEE Xplore Digital Library
Author	Xi-Zheng Zhang Li-Ning Xing
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci., Hunan Inst. of Eng., Xiangtan (Xi-Zheng Zhang)
Abstract	In view of the limitation of existing neural network model in solving time series forecasting problem, put forward a new multi-rule & real-time training neural network (MRRTTNN) model. The characteristics of this proposed model including (1) miniaturize the forecasting network, (2) train the network in real-time way, (3) adopt the average of abundant forecasting and (4) add some rules to assistant forecasting. Relative to the traditional neural network model, this model focus on dynamic training and dynamic forecasting, increase three rules (rule of dealing with abnormity, rule of retraining and rule of adopting the average) to assistant forecasting. Numerical example suggests the correctness and feasibility of this model. The contradistinctive result of this model and other five models indicates the validity and superiority of this model
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	3115
Ending Page	3118
File Size	318332
Page Count	4
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258401
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Predictive models Management training Artificial neural networks Technology forecasting Computer network management Information management Technology management Engineering management Equations multi-rule Time series forecasting neural network real-time training
Content Type	Text
Resource Type	Article

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