By means of fuzzy set theory as well as Takagi-Sugeno and Mamdani fuzzy controller, this paper presents the investigation of a Resort Management System implemented by a combination of a T-S model and a Mamdani model. It demonstrates the procedure of the specific premise parameters identification and consequence parameters identification performed by regression knowledge in the T-S model, and the process of the fuzzification, the rule base creation and the defuzzification with COG technique in the Mamdani model. Therefore, an aggregation between T-S controller and Mamdani controller applied in the field of management by a novel angle is illustrated, which, as a result, devotes an improved management system that shares great convenience in the control process when combined with mathematics. Moreover, a modification of the conventional Takagi-Sugeno and Mamdani controller is demonstrated in conjunction with fuzzy operations t-norms and OWA by adjusting the -value, which is used in the calculation of final outputs in the T-S model and the computation of rule consequences in the Mamdani model. The algebraic intersection, bounded intersection as well as the -parameter t-norm are the t-norms which are going to be introduced. Besides, we have tested that t-norms generate the same alpha values when the membership degrees meet the boundary with the value of 1 or 0 while OWA can still yield a well-balanced result different from the one computing by minimum operation. Nevertheless both t-norms and OWA are able to shift the alpha-value in a well-adjusted way when the membership degrees lie in the interval [0,1]. A tendency has been shown that alpha-value tends to decrease by means of t-norms and OWA operations and consequently, the final outputs appear to be reduced.

As we all know the classical set theory has a deep-rooted influence in the traditional mathematics. According to the two-valued logic, an element can belong to a set or cannot. In the former case, the element’s membership degree will be assigned to one, whereas in the latter case it takes the zero value. With other words, a feeling of imprecision or fuzziness in the two-valued logic does not exist. With the rapid development of science and technology, more and more scientists have gradually come to realize the vital importance of the multi-valued logic. Thus, in 1965, Professor Lotfi A. Zadeh from Berkeley University put forward the concept of a fuzzy set. In less than 60 years, people became more and more familiar with fuzzy set theory. The theory of fuzzy sets has been turned to be a favor applied to many fields. The study aims to apply some classical and extensional methods of fuzzy set theory in life expectancy and treatment prognoses for cancer patients. The research is based on real-life problems encountered in clinical works by physicians. From the introductory items of the fuzzy set theory to the medical applications, a collection of detailed analysis of fuzzy set theory and its extensions are presented in the thesis. Concretely speaking, the Mamdani fuzzy control systems and the Sugeno controller have been applied to predict the survival length of gastric cancer patients. In order to keep the gastric cancer patients, already examined, away from the unnecessary suffering from surgical operation, the fuzzy c-means clustering analysis has been adopted to investigate the possibilities for operation contra to nonoperation. Furthermore, the approach of point set approximation has been adopted to estimate the operation possibilities against to nonoperation for an arbitrary gastric cancer patient. In addition, in the domain of multi-expert decision-making, the probabilistic model, the model of 2-tuple linguistic representations and the hesitant fuzzy linguistic term sets (HFLTS) have been utilized to select the most consensual treatment scheme(s) for two separate prostate cancer patients. The obtained results have supplied the physicians with reliable and helpful information. Therefore, the research work can be seen as the mathematical complements to the physicians’ queries.