A clustering algorithm for fuzzy model identification
Fuzzy Sets and Systems
A neural network classifier with disjunctive fuzzy information
Neural Networks
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
About the use of fuzzy clustering techniques for fuzzy model identification
Fuzzy Sets and Systems
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
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new method for constructing membership functions and fuzzy rulesfrom training examples
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An efficient fuzzy classifier with feature selection based on fuzzyentropy
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
A new approach to fuzzy-neural system modeling
IEEE Transactions on Fuzzy Systems
A multilayered neuro-fuzzy classifier with self-organizing properties
Fuzzy Sets and Systems
A CBR-based fuzzy decision tree approach for database classification
Expert Systems with Applications: An International Journal
A diversity-driven structure learning algorithm for building hierarchical neuro-fuzzy classifiers
Information Sciences: an International Journal
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In this paper a modified algorithm is suggested for developing polynomial neural network (PNN) models. Optimal partial description (PD) modeling is introduced at each layer of the PNN expansion, a task accomplished using the orthogonal least squares (OLS) method. Based on the initial PD models determined by the polynomial order and the number of PD inputs, OLS selects the most significant regressor terms reducing the output error variance. The method produces PNN models exhibiting a high level of accuracy and superior generalization capabilities. Additionally, parsimonious models are obtained comprising a considerably smaller number of parameters compared to the ones generated by means of the conventional PNN algorithm. Three benchmark examples are elaborated, including modeling of the gas furnace process as well as the iris and wine classification problems. Extensive simulation results and comparison with other methods in the literature, demonstrate the effectiveness of the suggested modeling approach.