A hierarchical knowledge-based environment for linguistic modeling: models and iterative methodology
Fuzzy Sets and Systems - Theme: Learning and modeling
Modeling of hierarchical fuzzy systems
Fuzzy Sets and Systems - Theme: Learning and modeling
System identification using hierarchical fuzzy neural networks with stable learning algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
IEEE Transactions on Fuzzy Systems
Decomposition of a complex fuzzy controller for the truck-and-trailer reverse parking problem
Mathematical and Computer Modelling: An International Journal
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Rule base simplification by using a similarity measure of fuzzy sets
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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A review of the basic ideas of fuzzy systems modeling is provided. We introduce a hierarchical-type fuzzy systems model called a hierarchical prioritized structure (HPS) and review its structure, operation, and the interlevel aggregation algorithm. We then turn to the issue of constructing the HPS. Consideration is first given to the case in which rules are provided by an expert. Detailed consideration is given to the problem of completing incomplete priorities by use of the principle of maximal buoyancy. A mathematical programming method is introduced for the implementation of this approach. The issue of tuning hierarchical models is addressed. We next introduce a dynamic approach to the formulation of an HPS directly from data that enables us to continually update our model as more observations become available. This approach allows a system's builder to start with a default model and include exceptions