On constructing parsimonious type-2 fuzzy logic systems via influential rule selection
IEEE Transactions on Fuzzy Systems
Type-1 OWA operator based non-stationary fuzzy decision support systems for breast cancer treatments
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Computation of satisfiability degree based on CNF
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
A feedback based CRI approach to fuzzy reasoning
Applied Soft Computing
Expert Systems with Applications: An International Journal
Fuzzy rule interpolation based on the ratio of fuzziness of interval type-2 fuzzy sets
Expert Systems with Applications: An International Journal
An extended sliding mode learning algorithm for type-2 fuzzy neural networks
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Rule base identification in fuzzy networks by Boolean matrix equations
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper presents a case study in which the introduction of vagueness or uncertainty into the membership functions of a fuzzy system was investigated in order to model the variation exhibited by experts in a medical decision-making context. A conventional (type-1) fuzzy expert system had previously been developed to assess the health of infants immediately after birth by analysis of the biochemical status of blood taken from infants' umbilical cords. Variation in decision making was introduced into the fuzzy expert system by means of membership functions which altered in small, predetermined manners over time. Three types of variation in membership functions were investigated: i) variation in the centre points, ii) variation in the widths, and iii) the addition of "white noise". Different levels (amounts) of uniformly distributed random variation were investigated for each of these types. Monte Carlo simulations were carried out to propagate the variation through the inferencing process in order to determine distributions of the conclusions reached. Interval valued type-2 fuzzy systems were also implemented to investigate the boundaries of variability in decisions. The results obtained were compared to the experts' decisions in order to determine which type and size of membership function variability best matched the experts' variability. The novel reasoning technique introduced in this study is termed nonstationary fuzzy reasoning