Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling

@article{Zhou2008LowlevelIA,
  title={Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling},
  author={Shang-Ming Zhou and John Q. Gan},
  journal={Fuzzy Sets and Systems},
  year={2008},
  volume={159},
  pages={3091-3131}
}
This paper aims at providing an in-depth overview of designing interpretable fuzzy inference models from data within a unified framework. The objective of complex system modelling is to develop reliable and understandable models for human being to get insights into complex real-world systems whose first-principle models are unknown. Because system behaviour can be described naturally as a series of linguistic rules, data-driven fuzzymodelling becomes an attractive and widely used paradigm for… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 104 references

Support vector learning for fuzzy rule-based classification systems

IEEE Trans. Fuzzy Systems • 2003
View 5 Excerpts
Highly Influenced

The controller output error method

H. C. Anderson
Ph.D. Dissertation, University of Queensland, Australia • 1998
View 5 Excerpts
Highly Influenced

Fitting autoregressive models for prediction

H. Akaike
Ann. Inst. of Statist. Math. 21 • 1969
View 6 Excerpts
Highly Influenced

THEORY OF REPRODUCING KERNELS(')

N. ARONSZAJN
2010
View 1 Excerpt

Author ’ s reply ( to comments on the benchmarks in ‘ a proposal for improving the accuracy of linguistic modelling ’ and related articles )

F. Herrera Cordón
Soft Comput . • 2007