A hybrid ART-GRNN online learning neural network with a epsilon -insensitive loss function

Author(s): Lim, C.P. | Abidin, I. Z. | Yap, K.S. |

Year: 2008

Citation: IEEE Trans Neural Netw Volume: 19 Issue: 9 Pages: 1641-6

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.

Topics: Machine Learning, Models: ARTMAP, Fuzzy ARTMAP,

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Cross References


  1. Gaussian ARTMAP: A neural network for past incremental learning of noisy multidimensional maps
    A new neural network architecture for incremental supervised learning of analog multidimensional maps is introduced. The architecture, called Gaussian ARTMAP, is a synthesis of a Gaussian classifier and an adaptive resonance ... Article Details

  2. Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system
    A Fuzzy Adaptive Resonance Theory (ART) model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations ... Article Details

  3. Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps
    A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may ... Article Details


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