Introduction to non-linear optimization
Introduction to non-linear optimization
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Interactive system identification: prospects and pitfalls
Interactive system identification: prospects and pitfalls
An introduction to fuzzy control
An introduction to fuzzy control
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Parameter conditions for monotonic Takagi-Sugeno-Kang fuzzy system
Fuzzy Sets and Systems - Fuzzy systems
On the monotonicity of hierarchical sum--product fuzzy systems
Fuzzy Sets and Systems
On the monotonicity of fuzzy-inference methods related to T-S inference method
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Hybrid approaches for approximate reasoning
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We consider the situation where a non-linear physical system is identified from input-output data. In case no specific physical structural knowledge about the system is available, parameterized grey-box models cannot be used. Identification in black-box type of model structures is then the only alternative, and general approaches like neural nets, neuro-fuzzy models, etc., have to be applied. However, certain non-structural knowledge about the system is sometimes available. It could be known, e.g., that the step response is monotonic, or that the steady-state gain curve is monotonic. The main question is then how to utilize and maintain such information in an otherwise black-box framework. In this paper we show how this can be done, by applying a specific fuzzy model structure, with strict parametric constraints. The usefulness of the approach is illustrated by experiments on real-world data.