International Journal of Man-Machine Studies
Knowledge representation in fuzzy and rough controllers
Fundamenta Informaticae - Special issue: intelligent information systems
Comparison of neofuzzy and rough neural networks
Information Sciences: an International Journal
An application of rough set methods in control design
Fundamenta Informaticae - Special issue on Concurrency specification and programming (CS&P)
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Generating optimal adaptive fuzzy-neural models of dynamicalsystems with applications to control
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This paper describes a new approach to generate optimal fuzzy forecast model for Box and Jenkins' gas furnace from its Input/ Output data (I/O data) by fuzzy set theory and rough set theory (RST). Generally, the nonlinear mapping relations of I/O data can be expressed by fuzzy set theory and fuzzy logic, which are proven to be a nonlinear universal function approximator. One of the most distinguished features of RST is that it can directly extract knowledge from large amount of data without any transcendental knowledge. The fuzzy forecast model determination mainly includes 3 steps: firstly, express I/O data in fuzzy decision table. Secondly, quantitatively determine the best structure of the fuzzy forecast model by RST. The third step is to get optimal fuzzy rules from fuzzy decision table by RST reduction algorithm. Experimental results have shown the new algorithm is simple and intuitive. It is another successful application of RST in fuzzy identification.