Orthogonal and successive projection methods for the learning of neurofuzzy GMDH
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on modeling with soft-computing
The design of self-organizing polynomial neural networks
Information Sciences—Informatics and Computer Science: An International Journal
Linguistic models as a framework of user-centric system modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling
IEEE Transactions on Fuzzy Systems
Comparison of adaptive methods for function estimation from samples
IEEE Transactions on Neural Networks
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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.