Fuzzy relation-based polynomial neural networks based on hybrid optimization

Wei Huang; Sung Kwun Oh

doi:10.1007/978-3-642-31346-2_11

Fuzzy relation-based polynomial neural networks based on hybrid optimization

Wei Huang^*, Sung Kwun Oh

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper introduces hybrid optimized fuzzy relation-based polynomial neural network (HOFRPNN), a novel architecture that is constructed by using a combination of fuzzy rule-based models, polynomial neural networks (PNNs) and a hybrid optimization algorithm. The proposed hybrid optimization algorithm is developed by a combination of a space search algorithm and an improved complex method. The structure of HOFRPNN comprises of a synergistic usage of fuzzy-rule-based polynomial neuron that are essentially fuzzy rule-based models and polynomial neural networks that is an extended group method of data handling (GMDH). The architecture of HOFRPNN is an essentially modified PNN whose basic nodes are fuzzy-rule-based polynomial neurons rather than conventional polynomial neurons. Moreover, the hybrid optimization algorithm is utilized to optimize the structure topology of HOFRPNN. A comparative study demonstrates that the proposed model exhibits higher accuracy and superb predictive capability when compared with some previous models reported in the literature.

Original language	English
Title of host publication	Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings
Pages	90-97
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-31346-2_11
Publication status	Published - 2012
Externally published	Yes
Event	9th International Symposium on Neural Networks, ISNN 2012 - Shenyang, China Duration: 11 Jul 2012 → 14 Jul 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7367 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Symposium on Neural Networks, ISNN 2012
Country/Territory	China
City	Shenyang
Period	11/07/12 → 14/07/12

Keywords

Hybrid optimized fuzzy relation-based polynomial neural network (HOFRPNN)
fuzzy rule-based models
hybrid optimization
polynomial neural networks (PNNs)

Access to Document

10.1007/978-3-642-31346-2_11

Cite this

Huang, W., & Oh, S. K. (2012). Fuzzy relation-based polynomial neural networks based on hybrid optimization. In Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings (PART 1 ed., pp. 90-97). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7367 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-31346-2_11

Huang, Wei ; Oh, Sung Kwun. / Fuzzy relation-based polynomial neural networks based on hybrid optimization. Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings. PART 1. ed. 2012. pp. 90-97 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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abstract = "This paper introduces hybrid optimized fuzzy relation-based polynomial neural network (HOFRPNN), a novel architecture that is constructed by using a combination of fuzzy rule-based models, polynomial neural networks (PNNs) and a hybrid optimization algorithm. The proposed hybrid optimization algorithm is developed by a combination of a space search algorithm and an improved complex method. The structure of HOFRPNN comprises of a synergistic usage of fuzzy-rule-based polynomial neuron that are essentially fuzzy rule-based models and polynomial neural networks that is an extended group method of data handling (GMDH). The architecture of HOFRPNN is an essentially modified PNN whose basic nodes are fuzzy-rule-based polynomial neurons rather than conventional polynomial neurons. Moreover, the hybrid optimization algorithm is utilized to optimize the structure topology of HOFRPNN. A comparative study demonstrates that the proposed model exhibits higher accuracy and superb predictive capability when compared with some previous models reported in the literature.",

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author = "Wei Huang and Oh, {Sung Kwun}",

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Huang, W & Oh, SK 2012, Fuzzy relation-based polynomial neural networks based on hybrid optimization. in Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7367 LNCS, pp. 90-97, 9th International Symposium on Neural Networks, ISNN 2012, Shenyang, China, 11/07/12. https://doi.org/10.1007/978-3-642-31346-2_11

Fuzzy relation-based polynomial neural networks based on hybrid optimization. / Huang, Wei; Oh, Sung Kwun.
Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings. PART 1. ed. 2012. p. 90-97 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7367 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper introduces hybrid optimized fuzzy relation-based polynomial neural network (HOFRPNN), a novel architecture that is constructed by using a combination of fuzzy rule-based models, polynomial neural networks (PNNs) and a hybrid optimization algorithm. The proposed hybrid optimization algorithm is developed by a combination of a space search algorithm and an improved complex method. The structure of HOFRPNN comprises of a synergistic usage of fuzzy-rule-based polynomial neuron that are essentially fuzzy rule-based models and polynomial neural networks that is an extended group method of data handling (GMDH). The architecture of HOFRPNN is an essentially modified PNN whose basic nodes are fuzzy-rule-based polynomial neurons rather than conventional polynomial neurons. Moreover, the hybrid optimization algorithm is utilized to optimize the structure topology of HOFRPNN. A comparative study demonstrates that the proposed model exhibits higher accuracy and superb predictive capability when compared with some previous models reported in the literature.

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Huang W, Oh SK. Fuzzy relation-based polynomial neural networks based on hybrid optimization. In Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings. PART 1 ed. 2012. p. 90-97. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-31346-2_11

Fuzzy relation-based polynomial neural networks based on hybrid optimization

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