- Title
- Canonical duality for radial basis neural networks
- Creator
- Latorre, Vittorio; Gao, David
- Date
- 2013
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/54172
- Identifier
- vital:5131
- Identifier
-
https://doi.org/10.1007/978-3-642-37502-6_139
- Identifier
- ISSN:21945357 (ISSN); 9783642375019 (ISBN)
- Abstract
- Radial Basis Function Neural Networks (RBF NN) are a tool largely used for regression problems. The principal drawback of this kind of predictive tool is that the optimization problem solved to train the network can be non-convex. On the other hand Canonical Duality Theory offers a powerful procedure to reformulate general non-convex problems in dual forms so that it is possible to find optimal solutions and to get deep insights into the nature of the challenging problems. By combining the canonical duality theory with the RBF NN, this paper presents a potentially useful method for solving challenging problems in real-world applications. © Springer-Verlag Berlin Heidelberg 2013. Proceedings of the Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013.
- Relation
- Advances in Intelligent Systems and Computing Vol. 212, no. (2013), p. 1189-1197
- Rights
- Copyright 2013 Springer-Verlag Berlin Heidelberg
- Rights
- This metadata is freely available under a CCO license
- Subject
- Canonical duality; Neural network; Radial basis functions; Canonical duality theories; Nonconvex problem; Optimization problems; Radial basis function neural networks; Radial basis neural networks; Regression problem; Computation theory; Neural networks; Radial basis function networks; Problem solving
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