A hybrid neural network model for noisy data regression

Author(s): Lee, E.W.M | Chee, P.L. | Lo, S.M. | Yuen, R.K.K. |

Year: 2004

Citation: IEEE Transactions on Systems, Man, and Cybernetics, Part B, Volume 34, Issue 2, April 2004 Page(s): 951 - 960

Abstract: A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.

Topics: Machine Learning, Models: ART 2 / Fuzzy ART,

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