Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
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
Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Self-Organizing Methods in Modeling: Gmdh Type Algorithms
The design of self-organizing polynomial neural networks
Information Sciences—Informatics and Computer Science: An International Journal
Hybrid fuzzy polynomial neural networks
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Hybrid identification in fuzzy-neural networks
Fuzzy Sets and Systems - Theme: Learning and modeling
Knowledge structure, knowledge granulation and knowledge distance in a knowledge base
International Journal of Approximate Reasoning
Linguistic models as a framework of user-centric system modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Fuzzy Systems
Improving the interpretability of TSK fuzzy models by combining global learning and local learning
IEEE Transactions on Fuzzy Systems
Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling
IEEE Transactions on Fuzzy Systems
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
High-speed face recognition based on discrete cosine transform and RBF neural networks
IEEE Transactions on Neural Networks
A novel radial basis function neural network for discriminant analysis
IEEE Transactions on Neural Networks
On global-local artificial neural networks for function approximation
IEEE Transactions on Neural Networks
A Hybrid Forward Algorithm for RBF Neural Network Construction
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Research on domain ontology in different granulations based on concept lattice
Knowledge-Based Systems
Context FCM-based radial basis function neural networks with the aid of fuzzy clustering
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Evolutionary optimization-based tuning of low-cost fuzzy controllers for servo systems
Knowledge-Based Systems
Differential evolution with local information for neuro-fuzzy systems optimisation
Knowledge-Based Systems
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In this paper, we introduce a new category of fuzzy models called a fuzzy ensemble of parallel polynomial neural network (FEP^2N^2), which consist of a series of polynomial neural networks weighted by activation levels of information granules formed with the use of fuzzy clustering. The two underlying design mechanisms of the proposed networks rely on information granules resulting from the use of fuzzy C-means clustering (FCM) and take advantage of polynomial neural networks (PNNs). The resulting model comes in the form of parallel polynomial neural networks. In the design procedure, in order to estimate the optimal values of the coefficients of polynomial neural networks we use a weighted least square estimation algorithm. We incorporate various types of structures as the consequent part of the fuzzy model when using the learning algorithm. Among the diverse structures being available, we consider polynomial neural networks, which exhibit highly nonlinear characteristics when being viewed as local learning models. We use FCM to form information granules and to overcome the high dimensionality problem. We adopt PNNs to find the optimal local models, which can describe the relationship between the input variables and output variable within some local region of the input space. We show that the generalization capabilities as well as the approximation abilities of the proposed model are improved as a result of using polynomial neural networks. The performance of the network is quantified through experimentation in which we use a number of benchmarks already exploited within the realm of fuzzy or neurofuzzy modeling.