Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/10838
Title: An intelligent quick prediction algorithm with applications in industrial control and loading problems
Authors: Yu, Y
Choi, TM 
Hui, CL 
Keywords: Hybrid model
quick intelligent prediction
Issue Date: 2012
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on automation science and engineering, 2012, v. 9, no. 2, 6080740, p. 276-287 How to cite?
Journal: IEEE transactions on automation science and engineering 
Abstract: The Artificial Neural Network (ANN) and its variations have been well-studied for their applications in the prediction of industrial control and loading problems. Despite showing satisfactory performance in terms of accuracy, the ANN models are notorious for being slow compared to, e.g., the traditional statistical models. This substantially hinders ANN model's real-world applications in control and loading prediction problems. Recently a novel learning approach of ANN called Extreme Learning Machine (ELM) has emerged and it is proven to be very fast compared with the traditional ANN. In this paper, an Intelligent Quick Prediction Algorithm (IQPA), which employs an extended ELM (ELME) in producing fast, stable, and accurate prediction results for control and loading problems, is devised. This algorithm is versatile in which it can be used for short, medium to long-term predictions with both time series and non-time series data. Publicly available power plant operations and aircraft control data are employed for conducting analysis with this proposed novel model. Experimental results show that IQPA is effective and efficient, and can finish the prediction task with accurate results within a prespecified time limit.
URI: http://hdl.handle.net/10397/10838
ISSN: 1545-5955
EISSN: 1558-3783
DOI: 10.1109/TASE.2011.2173800
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