Expert Systems with Applications: An International Journal
Fuzzy polynomial neural networks for approximation of the compressive strength of concrete
Applied Soft Computing
Information granulation as a basis of fuzzy modeling
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Information Sciences: an International Journal
Editorial: Special issue on Industrial Applications of Neural Networks
Information Sciences: an International Journal
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Hybrid GMDH-type modeling for nonlinear systems: Synergism to intelligent identification
Advances in Engineering Software
Intelligent hybrid modelling towards the prognosis of abdominal pain
International Journal of Hybrid Intelligent Systems - CIMA-08
Self-organizing hybrid neurofuzzy networks
ICCS'03 Proceedings of the 2003 international conference on Computational science
Fuzzy combined polynomial neural networks
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
A diversity-driven structure learning algorithm for building hierarchical neuro-fuzzy classifiers
Information Sciences: an International Journal
Design of genetic fuzzy set-based polynomial neural networks with the aid of information granulation
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
The analysis and design of IG_gHSOFPNN by evolutionary optimization
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
The design of a fuzzy-neural network for ship collision avoidance
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons
Information Sciences: an International Journal
Design methodologies of fuzzy set-based fuzzy model based on GAs and information granulation
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Fuzzy relation-based polynomial neural networks based on hybrid optimization
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
The modified self-organizing fuzzy neural network model for adaptability evaluation
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Learning Fuzzy Network Using Sequence Bound Global Particle Swarm Optimizer
International Journal of Fuzzy System Applications
Neuro-fuzzy integrated system with its different domain applications
International Journal of Intelligent Systems Technologies and Applications
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We introduce a concept of fuzzy polynomial neural networks (FPNNs), a hybrid modeling architecture combining polynomial neural networks (PNNs) and fuzzy neural networks (FNNs). The development of the FPNNs dwells on the technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The structure of the FPNN results from a synergistic usage of FNN and PNN. FNNs contribute to the formation of the premise part of the rule-based structure of the FPNN. The consequence part of the FPNN is designed using PNNs. The structure of the PNN is not fixed in advance as it usually takes place in the case of conventional neural networks, but becomes organized dynamically to meet the required approximation error. We exploit a group method of data handling (GMDH) to produce this dynamic topology of the network. The performance of the FPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other similar fuzzy models.