TY - GEN
T1 - Hybrid optimized polynomial neural networks with polynomial neurons and fuzzy polynomial neurons
AU - Wang, Dan
AU - Ji, Donghong
AU - Huang, Wei
PY - 2012
Y1 - 2012
N2 - This paper introduces a hybrid optimized polynomial neural network (HOPNN), 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 Powell's method. The structure of this HOPNN comprises of a synergistic usage of fuzzy-relation-based polynomial neurons and polynomial neural network. The fuzzy-relation-based polynomial neurons are fuzzy rule-based models, while the polynomial neural network is an extended group method of data handling (GMDH). The architecture of HOPNN is essentially modified PNNs whose basic nodes of the first (input) layer are fuzzy-rule-based polynomial neurons rather than conventional polynomial neurons. Moreover, the proposed hybrid optimization algorithm is exploited to optimize the structure topology of HOPNN. The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.
AB - This paper introduces a hybrid optimized polynomial neural network (HOPNN), 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 Powell's method. The structure of this HOPNN comprises of a synergistic usage of fuzzy-relation-based polynomial neurons and polynomial neural network. The fuzzy-relation-based polynomial neurons are fuzzy rule-based models, while the polynomial neural network is an extended group method of data handling (GMDH). The architecture of HOPNN is essentially modified PNNs whose basic nodes of the first (input) layer are fuzzy-rule-based polynomial neurons rather than conventional polynomial neurons. Moreover, the proposed hybrid optimization algorithm is exploited to optimize the structure topology of HOPNN. The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.
KW - Hybrid optimized polynomial neural network
KW - fuzzy rule-based models
KW - hybrid optimization
KW - polynomial neural networks
UR - http://www.scopus.com/inward/record.url?scp=84867672812&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33269-2_10
DO - 10.1007/978-3-642-33269-2_10
M3 - Conference contribution
AN - SCOPUS:84867672812
SN - 9783642332685
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 73
EP - 80
BT - Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings
T2 - 22nd International Conference on Artificial Neural Networks, ICANN 2012
Y2 - 11 September 2012 through 14 September 2012
ER -