Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Implementation of fuzzy logic systems and neural networks in industry
Computers in Industry
Modeling and control of hierarchical systems with fuzzy systems
Automatica (Journal of IFAC)
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Intelligent systems: architectures and perspectives
Recent advances in intelligent paradigms and applications
A neuro fuzzy logic approach to material processing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A systematic neuro-fuzzy modeling framework with application tomaterial property prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Deriving prediction intervals for neuro-fuzzy networks
Mathematical and Computer Modelling: An International Journal
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This paper describes a neuro-fuzzy modeling framework for predicting the properties of ashes originated from combustion processes for electric generation. The prediction problem is tackled by means of a neuro-fuzzy system in which a neural network and a fuzzy system are combined in a fused architecture, so that the structure and the parameters of the fuzzy rule base are determined via a two-phase learning of the neural network. The modeling framework is composed of two modeling strategies that enable development of both MIMO and MISO neuro-fuzzy models. Experimental results demonstrate that models derived by the proposed framework delivered satisfactory results in spite of the significant complexity of the considered problem.