dc.contributor.authorSubramanian, K.
dc.contributor.authorSavitha, R.
dc.contributor.authorSuresh, Sundaram
dc.date.accessioned2014-03-24T06:29:33Z
dc.date.available2014-03-24T06:29:33Z
dc.date.copyright2013en_US
dc.date.issued2013
dc.identifier.citationSubramanian, K., Savitha, R., & Suresh, S. (2013). A complex-valued neuro-fuzzy inference system and its learning mechanism. Neurocomputing, 123, 110-120.en_US
dc.identifier.issn0925-2312en_US
dc.identifier.urihttp://hdl.handle.net/10220/18958
dc.description.abstractIn this paper, we present a Complex-valued Neuro-Fuzzy Inference System (CNFIS) and develop its meta-cognitive learning algorithm. CNFIS has four layers-an input layer with m rules, a Gaussian layer with K rules, a normalization layer with K rules and an output layer with n rules. The rules in the Gaussian layer map the m-dimensional complex-valued input features to a K-dimensional real-valued space. Hence, we use the Wirtinger calculus to obtain the complex-valued gradients of the real-valued function in deriving the learning algorithm of CNFIS. Next, we also develop the meta-cognitive learning algorithm for CNFIS, referred to as, “Meta-cognitive Complex-valued Neuro-Fuzzy Inference System (MCNFIS)”. CNFIS is the cognitive component of MCNFIS and a self-regulatory learning mechanism that decides what-to-learn, how-to-learn, and when-to-learn in a meta-cognitive framework is its meta-cognitive component. Thus, for every epoch of the learning process, the meta-cognitive component decides if each sample in the training set must be deleted or used to update the parameters of CNFIS or to be reserved for future use. The performances of CNFIS and MCNFIS are studied on a set of approximation and real-valued classification problems, in comparison to existing complex-valued learning algorithms in the literature. First, we evaluate the approximation performances of CNFIS and MCNFIS on a synthetic complex-valued function approximation problem, an adaptive beam forming problem and a wind prediction problem. Finally, we study the decision making performance of CN- FIS and MCNFIS on a set of benchmark real-valued classification problems from the UCI machine learning repository. Performance study results on approximation and real-valued classification problems show that CNFIS and MCNFIS outperform existing algorithms in the literature.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesNeurocomputingen_US
dc.rights© 2013 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Neurocomputing, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [DOI: http://dx.doi.org/10.1016/j.neucom.2013.06.009].en_US
dc.subjectDRNTU::Engineering::Computer science and engineering
dc.titleA complex-valued neuro-fuzzy inference system and its learning mechanismen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.neucom.2013.06.009
dc.description.versionAccepted versionen_US


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