A Hybrid Art-grnn Online Learning Neural Network with a -insensitive Loss Function

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.

[1]  Narayan Srinivasa,et al.  Learning and generalization of noisy mappings using a modified PROBART neural network , 1997, IEEE Trans. Signal Process..

[2]  Razvan Andonie,et al.  Fuzzy ARTMAP with input relevances , 2006, IEEE Transactions on Neural Networks.

[3]  Chee Kheong Siew,et al.  Real-time learning capability of neural networks , 2006, IEEE Trans. Neural Networks.

[4]  James R. Williamson,et al.  Gaussian ARTMAP: A Neural Network for Fast Incremental Learning of Noisy Multidimensional Maps , 1996, Neural Networks.

[5]  Hans-Peter Kriegel,et al.  Towards an effective cooperation of the user and the computer for classification , 2000, KDD '00.

[6]  Robert F. Harrison,et al.  A modified fuzzy ARTMAP architecture for the approximation of noisy mappings , 1995, Neural Networks.

[7]  B. Efron.  Nonparametric standard errors and confidence intervals , 1981 .

[8]  John C. Platt.  A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.

[9]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[10]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[11]  Visakan Kadirkamanathan,et al.  A Function Estimation Approach to Sequential Learning with Neural Networks , 1993, Neural Computation.

[12]  C. P. Lim,et al.  A hybrid neural network for time series prediction , 2006 .

[13]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[14]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[15]  Narasimhan Sundararajan,et al.  An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[17]  Paramasivan Saratchandran,et al.  Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm , 1998, IEEE Trans. Neural Networks.

[18]  Wei Chu,et al.  Bayesian support vector regression using a unified loss function , 2004, IEEE Transactions on Neural Networks.

[19]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[20]  B. Moore,et al.  ART1 and pattern clustering , 1989 .

[21]  Narasimhan Sundararajan,et al.  A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation , 2005, IEEE Transactions on Neural Networks.

[22]  Paseo Alfonso Xiii,et al.  MicroARTMAP: use of Mutual Information for Category Reduction in Fuzzy ARTMAP , 2000 .

[23]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[24]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[25]  Khashayar Khorasani,et al.  Constructive feedforward neural networks using Hermite polynomial activation functions , 2005, IEEE Transactions on Neural Networks.

[26]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[27]  Jianhua Xu,et al.  Kernels based on weighted Levenshtein distance , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[28]  Chee Peng Lim,et al.  A hybrid neural network model for noisy data regression , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[29]  C.P. Lim,et al.  Short Term Load Forecasting Using a Hybrid Neural Network , 2006, 2006 IEEE International Power and Energy Conference.

[30]  Lakhmi C. Jain,et al.  Simulation and reinforcement learning with soccer agents , 2008, Multiagent Grid Syst..