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Sustainability 2017, 9(6), 982; doi:10.3390/su9060982

Comparison between Inverse Model and Chaos Time Series Inverse Model for Long-Term Prediction

Division of Architecture, Architectural Engineering and Civil Engineering, Sunmoon University, Asan, Chungnam 336-708, Korea
Academic Editor: Marc A. Rosen
Received: 20 April 2017 / Revised: 22 May 2017 / Accepted: 6 June 2017 / Published: 7 June 2017
(This article belongs to the Section Sustainable Engineering and Science)
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Abstract

This paper presents an inverse model using chaotic behaviour. The chaos time series inverse model, which uses coupling methods between an inverse model and chaos theory can reconstruct a deterministic and low-dimensional phase space by transforming irregular behaviours of nonlinear time-varying systems into a strange attractor (e.g., a Rossler attractor or a Lorenz attractor), and it can then predict future states. For this study, the author used a training dataset measured in an existing high-rise building and examined the predictive abilities of the chaos time series inverse model modelled into phase spaces with strange attractors in comparison with those of the Support Vector Regression (SVR) out of the inverse model. The paper discusses the effective use of the chaos time series inverse model for multi-step ahead prediction. View Full-Text
Keywords: chaos; inverse model; support vector; model predictive control; building simulation chaos; inverse model; support vector; model predictive control; building simulation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kim, Y.-J. Comparison between Inverse Model and Chaos Time Series Inverse Model for Long-Term Prediction. Sustainability 2017, 9, 982.

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