Multilayer feedforward networks are universal approximators
Neural Networks
Neurocomputing
A simply identified Sugeno-type fuzzy model via double clustering
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on modeling with soft-computing
Predicting a chaotic time series using a fuzzy neural network
Information Sciences: an International Journal
Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Nonlinear system modeling by competitive learning and adaptivefuzzy inference system
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A new approach to fuzzy-neural system modeling
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
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
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Improvement of HSOFPNN using evolutionary algorithm
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Relation-based neurofuzzy networks with evolutionary data granulation
Mathematical and Computer Modelling: An International Journal
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We propose a hybrid architecture based on a combination of fuzzy systems and polynomial neural networks. The resulting Hybrid Fuzzy Polynomial Neural Networks (HFPNN) dwells on the ideas of fuzzy rule-based computing and polynomial neural networks. The structure of the network comprises of fuzzy polynomial neurons (FPNs) forming the nodes of the first (input) layer of the HFPNN and polynomial neurons (PNs) that are located in the consecutive layers of the network. In the FPN (that forms a fuzzy inference system), the generic rules assume the form "if A then y = P(x)" where A is a fuzzy relation in the condition space while P(x) is a polynomial standing in the conclusion part of the rule. The conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as constant, linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are considered. Each PN of the network realizes a polynomial type of partial description (PD) of the mapping between input and out variables. HFPNN is a flexible neural architecture whose structure is based on the Group Method of Data Handling (GMDH) and developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated in a dynamic way. The experimental part of the study involves two representative numerical examples such as chaotic time series and Box-Jenkins gas furnace data.