Zobrazit minimální záznam

dc.contributor.authorOjha, Varun Kumar
dc.contributor.authorSnášel, Václav
dc.contributor.authorAbraham, Ajith
dc.date.accessioned2018-04-11T08:26:47Z
dc.date.available2018-04-11T08:26:47Z
dc.date.issued2018
dc.identifier.citationIEEE Transactions on Fuzzy Systems. 2018, vol. 26, issue 2, p. 915-936.cs
dc.identifier.issn1063-6706
dc.identifier.issn1941-0034
dc.identifier.urihttp://hdl.handle.net/10084/125815
dc.description.abstractThis paper proposes a design of hierarchical fuzzy inference tree (HFIT). An HFIT produces an optimum tree-like structure, i.e., a natural hierarchical structure that accommodates simplicity by combining several low-dimensional fuzzy inference systems (FISs). Such a natural hierarchical structure provides a high degree of approximation accuracy. The construction of the HFIT takes place in two phases. First, a nondominated sorting-based multiobjective genetic programming (MOGP) is applied to obtain a simple tree structure (a low complexity model) with a high accuracy. Second, the differential evolution algorithm is applied to optimize the obtained tree's parameters. In the derived tree, each node acquires a different input's combination, where the evolutionary process governs the input's combination. Hence, HFIT nodes are heterogeneous in nature, which leads to a high diversity among the rules generated by the HFIT. Additionally, the HFIT provides an automatic feature selection because it uses MOGP for the tree's structural optimization that accepts inputs only relevant to the knowledge contained in data. The HFIT was studied in the context of both type-1 and type-2 FISs, and its performance was evaluated through six application problems. Moreover, the proposed multiobjective HFIT was compared both theoretically and empirically with recently proposed FISs methods from the literature, such as McIT2FIS, TSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the obtained results, it was found that the HFIT provided less complex and highly accurate models compared to the models produced by the most of other methods. Hence, the proposed HFIT is an efficient and competitive alternative to the other FISs for function approximation and feature selection.cs
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofseriesIEEE Transactions on Fuzzy Systemscs
dc.relation.urihttps://doi.org/10.1109/TFUZZ.2017.2698399cs
dc.rights© 2018, IEEEcs
dc.subjectapproximationcs
dc.subjectdifferential evolution (DE)cs
dc.subjectfeature selectioncs
dc.subjecthierarchical fuzzy inference system (HFIS)cs
dc.subjectmultiobjective genetic programming (MOGP)cs
dc.titleMultiobjective programming for type-2 hierarchical fuzzy inference treescs
dc.typearticlecs
dc.identifier.doi10.1109/TFUZZ.2017.2698399
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume26cs
dc.description.issue2cs
dc.description.lastpage936cs
dc.description.firstpage915cs
dc.identifier.wos000428613500040


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Zobrazit minimální záznam