TY - GEN
T1 - Fuzzy relation-based polynomial neural networks based on hybrid optimization
AU - Huang, Wei
AU - Oh, Sung Kwun
PY - 2012
Y1 - 2012
N2 - 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.
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.
KW - Hybrid optimized fuzzy relation-based polynomial neural network (HOFRPNN)
KW - fuzzy rule-based models
KW - hybrid optimization
KW - polynomial neural networks (PNNs)
UR - http://www.scopus.com/inward/record.url?scp=84865145811&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-31346-2_11
DO - 10.1007/978-3-642-31346-2_11
M3 - Conference contribution
AN - SCOPUS:84865145811
SN - 9783642313455
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 90
EP - 97
BT - Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings
T2 - 9th International Symposium on Neural Networks, ISNN 2012
Y2 - 11 July 2012 through 14 July 2012
ER -