Structure identification of fuzzy model
Fuzzy Sets and Systems
Fuzzy constraint processing
Grey-box modelling and identification using physical knowledge and Bayesian techniques
Automatica (Journal of IFAC)
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks
Fuzzy Sets and Systems
Industrial Applications of Fuzzy Control
Industrial Applications of Fuzzy Control
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
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A novel framework for fuzzy modeling and model-based control design is described. Based on the theory of fuzzy constraint processing, the fuzzy model can be viewed as a generalized Takagi-Sugeno (TS) fuzzy model with fuzzy functional consequences. It uses multivariate antecedent membership functions obtained by granular-prototype fuzzy clustering methods and consequent fuzzy equations obtained by fuzzy regression techniques. Constrained optimization is used to estimate the consequent parameters, where the constraints are based on control-relevant a priori knowledge about the modeled process. The fuzzy-constraint-based approach provides the following features. 1) The knowledge base of a constraint-based fuzzy model can incorporate information with various types of fuzzy predicates. Consequently, it is easy to provide a fusion of different types of knowledge. The knowledge can be from data-driven approaches and/or from control-relevant physical models. 2) A corresponding inference mechanism for the proposed model can deal with heterogeneous information granules. 3) Both numerical and linguistic inputs can be accepted for predicting new outputs.The proposed techniques are demonstrated by means of two examples: a nonlinear function-fitting problem and the well-known Box-Jenkins gas furnace process. The first example shows that the proposed model uses fewer fuzzy predicates achieving similar results with the traditional rule-based approach, while the second shows the performance can be significantly improved when the control-relevant constraints are considered.