dc.contributor.authorSavitha, R.
dc.contributor.authorSuresh, Sundaram
dc.contributor.authorSundararajan, Narasimhan
dc.identifier.citationSavitha, R., Suresh, S., & Sundararajan, N. (2012). Metacognitive learning in a fully complex-valued radial basis function neural network. Neural computation, 24(5), 1297-1328.en_US
dc.description.abstractRecent studies on human learning reveal that self-regulated learning in a metacognitive framework is the best strategy for efficient learning. As the machine learning algorithms are inspired by the principles of human learning, one needs to incorporate the concept of metacognition to develop efficient machine learning algorithms. In this letter we present a metacognitive learning framework that controls the learning process of a fully complex-valued radial basis function network and is referred to as a metacognitive fully complex-valued radial basis function (Mc-FCRBF) network. Mc-FCRBF has two components: a cognitive component containing the FC-RBF network and a metacognitive component, which regulates the learning process of FC-RBF. In every epoch, when a sample is presented to Mc-FCRBF, the metacognitive component decides what to learn, when to learn, and how to learn based on the knowledge acquired by the FC-RBF network and the new information contained in the sample. The Mc-FCRBF learning algorithm is described in detail, and both its approximation and classification abilities are evaluated using a set of benchmark and practical problems. Performance results indicate the superior approximation and classification performance of Mc-FCRBF compared to existing methods in the literature.en_US
dc.relation.ispartofseriesNeural computationen_US
dc.rights© 2011 Massachusetts Institute of Technology. This paper was published in Neural Computation and is made available as an electronic reprint (preprint) with permission of Massachusetts Institute of Technology. The paper can be found at the following official DOI: [http://dx.doi.org/10.1162/NECO_a_00254]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.en_US
dc.titleMetacognitive learning in a fully complex-valued radial basis function neural networken_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US

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