title: A hybrid ART-GRNN online learning neural network with a epsilon -insensitive loss function.
abstract: In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models.
authors.affiliation: College of Engineering, Universiti Tenaga Nasional, Selangor, Malaysia. yapkeem@uniten.edu.my
authors.forename: Keem Siah
authors.initials: KS
authors.lastname: Yap
authors.forename: Chee Peng
authors.initials: CP
authors.lastname: Lim
authors.forename: Izham Zainal
authors.initials: IZ
authors.lastname: Abidin
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*