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Development of a Hybrid Artificial Neural Network Model and its Application to Data Regression


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Abstract

Simulating the behaviour of a nonlinear system from its historical noise corrupted data is one of the major applications of the Artificial Neural Network (ANN). The objective of this paper is to develop an autonomous incremental growth neural network model for carrying out the tasks of regression and classification in noisy environment. A hybrid ANN model called GRNNFA is proposed. It is a fusion of the Fuzzy ART (FA) and the General Regression Neural Network (GRNN) and facilitates the removal of the noise embedded in the training samples. The performance of the GRNNFA model was examined by two benchmarking problems which are the Approximation-Of-Noisy-Mapping and the Fisher’s Iris Data. The results demonstrate the superior performance of the GRNNFA model working in the noisy environments.


Keywords


Pages

Total Pages: 14
Pages: 319-332

DOI
10.1080/10798587.2012.10643246


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Published

Volume: 18
Issue: 4
Year: 2012

Cite this document


References

Rosenblatt, F. Principles of Neurodynamics, Spartan Books, New York, 1962.

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Moore, B. “ART 1 and pattern clustering” Proceedings of the 1988 Connectionist Models Summer School, San Mateo, CA, Morgan Kaufmann Publishers, pp.174–185, 1988.

Kosko, Bart. "Fuzzy Entropy and Conditioning." Information Sciences 40.2 (1986): 165-174. Crossref. Web. https://doi.org/10.1016/0020-0255(86)90006-X

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

TWO YEAR CITATIONS PER DOCUMENT (SJR DATA): 0.993 (2018)
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."





Journal: 1995-Present


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