Construction of neural network-based prediction intervals using particle swarm optimization

Quan, Hao, Srinivasan, Dipti and Khosravi, Abbas 2012, Construction of neural network-based prediction intervals using particle swarm optimization, in IJCNN/WCCI 2012 : Proceedings of the 2012 International Joint Conference on Neural Networks, IEEE, Piscataway, N. J., pp. 647-653.

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Title Construction of neural network-based prediction intervals using particle swarm optimization
Author(s) Quan, Hao
Srinivasan, Dipti
Khosravi, Abbas
Conference name International Joint Conference on Neural Networks (2012 : Brisbane, Qld.)
Conference location Brisbane, Qld.
Conference dates 10-15 Jun. 2012
Title of proceedings IJCNN/WCCI 2012 : Proceedings of the 2012 International Joint Conference on Neural Networks
Editor(s) [Unknown]
Publication date 2012
Conference series International Joint Conference on Neural Networks
Start page 647
End page 653
Total pages 7
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) coverage probabilities
data distribution
multi-objective optimization problem
mutation operators
network-based
neural network model
optimization problems
prediction interval
upper Bound
Summary Prediction intervals (PIs) are excellent tools for quantification of uncertainties associated with point forecasts and predictions. This paper adopts and develops the lower upper bound estimation (LUBE) method for construction of PIs using neural network (NN) models. This method is fast and simple and does not require calculation of heavy matrices, as required by traditional methods. Besides, it makes no assumption about the data distribution. A new width-based index is proposed to quantitatively check how much PIs are informative. Using this measure and the coverage probability of PIs, a multi-objective optimization problem is formulated to train NN models in the LUBE method. The optimization problem is then transformed into a training problem through definition of a PI-based cost function. Particle swarm optimization (PSO) with the mutation operator is used to minimize the cost function. Experiments with synthetic and real-world case studies indicate that the proposed PSO-based LUBE method can construct higher quality PIs in a simpler and faster manner.
ISBN 9781467314893
ISSN 2161-4393
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 850509 Wind Energy
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30049582

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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