A New Sampled-Data Output-Feedback Controller Design of Nonlinear Systems via Fuzzy Affine Models

Ji, Wenqiang; Qiu, Jianbin; Lam, Hak‐Keung

doi:10.1109/tcyb.2020.2984331

Cited by 12 publications

(2 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, we plan to investigate how the weighting measure W could be modeled by λ-additive or ν-additive measures (see Dombi and Jónás (2019)). And, we plan to see whether the constructions of fuzzy affine models (see, e.g., Ji et al (2021Ji et al ( , 2022) can be adapted to quadratic scoring rules.…”

Section: Discussionmentioning

confidence: 99%

“…Regarding the connections between fuzzy logic and the quadratic scoring rules, it is worth mentioning that in fuzzy control, the fuzzy affine models utilize quadratic Lyapunov functions (see, e.g., Ji et al (2021Ji et al ( , 2022). In our approach, we approximate two particular quadratic functions, q 1 (r ) = 1 − (1 − r ) 2 and q 2 (r ) = (1 − r 2 ), r ∈ [0, 1], using the tau and eta functions, which are unary operators in fuzzy logic (see Dombi and Jónás (2021)).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An alternative approach to quadratic scoring rules using continuous-valued logic

Dombi

Jónás

2022

Soft Comput

View full text Add to dashboard Cite

Following tfhe seminal paper of Offerman et al. (2009), in this study, adaptations of constructions of continuous-valued logic to prospect theory are presented. Here, we demonstrate that the so-called kappa function and its special cases are viable alternatives to some elements of quadratic scoring rule prospects theory. After, we present the tau-eta scoring rule prospect and show that it may be treated as a generalization of the quadratic scoring rule prospect. Furthermore, we prove that if this new prospect for an uncertain event is evaluated using specific kappa functions as utility functions, then (1) the weighting measure of the event is a function of the optimal value of its reported probability, (2) the inverse of the latter function, and (3) the (risk-) corrected reported probability of the event, also as a function of the optimal value of its reported probability, all have a common formula. The parameters of the common formula are unambiguously determined by four tuning parameters. Lastly, we show that with our approach, by fitting one of the abovementioned functions to corresponding empirical data, we can immediately obtain the other two functions as well.

show abstract

Section: Discussionmentioning

confidence: 99%