Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99120
Title: A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system
Authors: Suresh, Sundaram
Subramanian, K.
Issue Date: 2012
Source: Subramanian, K.,& Suresh, S. (2012). A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system. Applied Soft Computing, 12(11), 3603-3614.
Series/Report no.: Applied soft computing
Abstract: In this paper, we present a meta-cognitive sequential learning algorithm for a neuro-fuzzy inference system, referred to as, ‘Meta-Cognitive Neuro-Fuzzy Inference System’ (McFIS). McFIS has two components, viz., a cognitive component and a meta-cognitive component. The cognitive component employed is a Takagi–Sugeno–Kang type-0 neuro-fuzzy inference system. A self-regulatory learning mechanism that controls the learning process of the cognitive component, by deciding what-to-learn, when-to-learn and how-to-learn from sequential training data, forms the meta-cognitive component. McFIS realizes the above decision by employing sample deletion, sample reserve and sample learning strategy, respectively. The meta-cognitive component use the instantaneous error of the sample and spherical potential of the rule antecedents to select the best training strategy for the current sample. Also, in sample learning strategy, when a new rule is added the rule consequent is assigned such that the localization property of Gaussian rule is fully exploited. The performance of McFIS is evaluated on four regression and eight classification problems. The performance comparison shows the superior generalization performance of McFIS compared to existing algorithms.
URI: https://hdl.handle.net/10356/99120
http://hdl.handle.net/10220/12556
ISSN: 1568-4946
DOI: http://dx.doi.org/10.1016/j.asoc.2012.06.012
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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