Research On Several Kinds Of Uncertain Rough Decision

Decision-making under uncertainty (DMU) is an important branch of modern decision science. Its theory and method are widely applied to many areas, such as management, economics, military affairs, medicine and so forth. The rough set is a data driven method dealing with uncertain and inconsistent information, which had become a powerful tool. Rough set and the uncertain multiple attribute decision-making theory had been fusioned in recent years. It has developed into a high-profile field of uncertain decision methods as:uncertain multiple attribute rough decision-making theory (UMARDM), which has been wildely used in intelligent control, enterprise financial crisis prediction model, automatic text classification, and so on, it has been successfully used in group decision making too.Decision is based on people’s awareness to the object of study, due to the prevalence of uncertain problems in reality and perception of finiteness, the descriptions of the research object is often fuzzy and uncertain, so the corresponding description of data has a variety of data types, such as:real number, language accurately and intuitionistic fuzzy Numbers, etc. At the same time people’s perception is often based on the research object of two contrasts. That is, people always through some comparable relationship between two objects, combined with people’s mental decision system, eventually get choice and decision making. Which of the two relations between the two is a kind of binary relation, so in uncertain decision making, in the common binary relation:equivalence relation, general binary relation, intuitionistic fuzzy relations, in combination with proper uncertain decision making tool, to study various realistic decision problem has important theoretical significance and practical application value.In this paper, under the equivalence relation, general binary relation and intuitionistic fuzzy relations of decision-making theory, its theoretical research was applied to traffic problems.The main results got in this thesis are summarized as follows:1. In the aspects of Uncertain Multiple Attribute Rough Decision-making theory (UMARDM) based on equivalence relation, in order to overcome the shortcomings of traditional reduction algorithm which under-utilization for data distribution information, proposed a combination of discretization and attribute reduction method, making more full use of the information in the decision-making system.First of all, through the discernibility matrix, defined the importance of attribute values, got discretization algorithms. Then through the definition of class, attribute, discussed the importance of attributes, get the heuristic algorithm of attribute reduction. It realized a discretization and attribute reduction combining in the heuristic algorithm. Other algorithms on UCI data sets experiment was compared with, the results shew that this method can make full use of the distribution characteristics of the data set itself, and can get better effects of attribute reduction. Especially the more number of data in the database, the more it was outstanding. Due to considering the importance of attribute values, so this method is not only applicable to the condition attribute values distribution of scattered, also can apply to condition attribute values which are centered.2. In the aspects of UMARDM based on general binary relation, defined S-Rough set under the general binary relations, which could dynamically use the corrosion and expansion operator to process the edge of images.The larger neighborhood operator was used to test, if the operator can completely cover image elements, choosed the larger neighborhood operator for expansion and corrosion calculations. If the operator cannot completely cover image elements, used the expansion and corrosion with smaller neighborhood operator. Finally, it wsa applied to the corrosion and expansion process of image edge. Through the image processing results, verified the flexibility and effectiveness of the algorithm.3. In the aspects of UMARDM based on intuitionistic fuzzy relations, put forward a domain knowledge oriented group decision-making method, which combined domain knowledge of experts with the data of intuitionistic fuzzy entropy, especially for satisfaction and dissatisfaction are close to 0.5, in this case, the traditional method based on distance often cannot be solved.Intuitionistic fuzzy information entropy was used to get weights of attributes. Then the AHP method was used to get expert weight. Finally, the aggregation operator was proposed to sort and used example to illustrate the effectiveness of the method. It provided the beneficial reference for other group decision-making problems which satisfaction and dissatisfaction are close to 0.5.4. In the aspects of matrix games based on intuitionistic fuzzy relations, proposed a linear programming method in solving a double matrix game model based on intuitionistic fuzzy sets (IFPBiG), get a more general method of matrix game problem with nonzero-sum, the rationalities of both sides can assume differently, it generalized the existing method of simplified rational assumption.Firstly, the IFPBiG was established. Then two kinds of nonlinear programming algorithm to solve the model were discussed, by using the fixed point theory proved the existence of Nash equilibrium. Finally a fast linear programming algorithm for solving IFPBiG was put forward. It allowed expressing human rationality with different values, which could be used as a more general case compared with classic rational assumption of game.The results of this article enriched the theory of multi-attributes rough decision-making, provided a new effective method for different types of uncertainty decision-making problem.