Structured Learning and Decomposition of Fuzzy Models for Robotic Control Applications
Journal of Intelligent and Robotic Systems
A method for fuzzy system identification based on clustering analysis
Systems Analysis Modelling Simulation
Fuzzy knowledge-based and model-based systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
An interactive framework for an analysis of ECG signals
Artificial Intelligence in Medicine
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We discuss a problem of rule-based fuzzy modeling of multiple-input single-output nonlinear relationships f: Rn→R. The model under investigation is viewed as a collection of conditional statements “if state Ω, then y=g i(x,at)”, i=1,2,...N with Ωi being a fuzzy relation defined in the space of the input variables. In contrast to the commonly encountered identification approach, based exclusively upon discrete experimental data, the one proposed in this study is concerned with the rule-based modeling exploiting the available nonlinear input-output relationship. The main thrust is in the development of a relevant fuzzy partition of the input variables. We introduce and study criteria of separability and variability as the key means guiding a distribution and granularity of the linguistic labels forming the condition part of the local models