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A Radial Basis Function network training algorithm using a non-symmetric partition of the input space - Application to a Model Predictive Control configuration

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A Radial Basis Function network training algorithm using a non-symmetric partition of the input space - Application to a Model Predictive Control configuration (EN)
Ninos, K (EN)
Sarimveis, H (EN)
Alexandridis, A (EN)
journalArticle (EN)
2014-03-01T01:35:00Z
2011 (EN)
This work presents the non-symmetric fuzzy means algorithm which is a new methodology for training Radial Basis Function neural network models. The method is based on a non-symmetric fuzzy partition of the space of input variables which results to networks with smaller structures and better approximation capabilities compared to other state-of-the-art training procedures. The lower modeling error and the smaller size of the produced models become particularly important when they are used in online applications. This is demonstrated by integrating the model produced by the proposed algorithm in a Model Predictive Control configuration, resulting in better control performance and shorter computational times. (C) 2011 Elsevier Ltd. All rights reserved. (EN)
Computer Science, Interdisciplinary Applications (EN)
Computer Science, Software Engineering (EN)
Radial basis function (EN)
Fuzzy partition (EN)
Model predictive control (EN)
Learning algorithms (EN)
Radial basis functions (EN)
Neural networks (EN)
Predictive control systems (EN)
Radial basis function networks (EN)
Fuzzy means (EN)
Mackey-Glass (EN)
Continuous stirred tank reactor (EN)
Non-symmetric partition (EN)
Advances in Engineering Software (EN)
English
ELSEVIER SCI LTD (EN)
Εθνικό Μετσόβιο Πολυτεχνείο
Ψηφιακό Αποθετήριο της Κεντρικής Βιβλιοθήκης του ΕΜΠ | Dspace@NTUA
Κεντρική Βιβλιοθήκη Ε.Μ.Π.
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