It is a new research topic to create a rational judgment matrix using the cognition theory because of the construction of judgment matrix in AHP involving the decision-maker's cognitive activities. Owing to the pr...It is a new research topic to create a rational judgment matrix using the cognition theory because of the construction of judgment matrix in AHP involving the decision-maker's cognitive activities. Owing to the presence of uncertain information in the decision procedure, the improper use of the uncertain information will doubtless cause weight changes. In this paper, we add a feedforward process prior to constructing the judgment matrix so that the decision maker can use both the certain and uncertain information to get the initial uncertain rough judgment matrix, and then convert it into a fuzzy matrix. Consequently, it will be better for decision maker to obtain the rough set of order equivalent classes through the decision graph. According to the qualitative analysis, the decision maker can easily construct the final judgment matrix instructed by the rough set created earlier.展开更多
The concept of deep learning has been applied to many domains, but the definition of a suitable problem depth has not been sufficiently explored. In this study, we propose a new Hierarchical Covering Algorithm （HCA）...The concept of deep learning has been applied to many domains, but the definition of a suitable problem depth has not been sufficiently explored. In this study, we propose a new Hierarchical Covering Algorithm （HCA） method to determine the levels of a hierarchical structure based on the Covering Algorithm （CA）. The CA constructs neural networks based on samples＇ own characteristics, and can effectively handle multi-category classification and large-scale data. Further, we abstract characters based on the CA to automatically embody the feature of a deep structure. We apply CA to construct hidden nodes at the lower level, and define a fuzzy equivalence relation R on upper spaces to form a hierarchical architecture based on fuzzy quotient space theory. The covering tree naturally becomes from R. HCA experiments performed on MNIST dataset show that the covering tree embodies the deep architecture of the problem, and the effects of a deep structure are shown to be better than having a single level.展开更多
基金This work is supported in part by the National Natural Sciences Fund Council, P. R. China, under Grant No. NSFC 6027047
文摘It is a new research topic to create a rational judgment matrix using the cognition theory because of the construction of judgment matrix in AHP involving the decision-maker's cognitive activities. Owing to the presence of uncertain information in the decision procedure, the improper use of the uncertain information will doubtless cause weight changes. In this paper, we add a feedforward process prior to constructing the judgment matrix so that the decision maker can use both the certain and uncertain information to get the initial uncertain rough judgment matrix, and then convert it into a fuzzy matrix. Consequently, it will be better for decision maker to obtain the rough set of order equivalent classes through the decision graph. According to the qualitative analysis, the decision maker can easily construct the final judgment matrix instructed by the rough set created earlier.
基金supported by the National Key Basic Research and Development(973)Program of China(No.2007CB311003)the National Natural Science Foundation of China(Nos.61073117 and 61175046)+1 种基金the Young Science Foundation of Anhui University(No.KJQN1118)the Outstanding Young Talents Higher Education Institutions of Anhui Province(No.2011SQRL129ZD)
文摘The concept of deep learning has been applied to many domains, but the definition of a suitable problem depth has not been sufficiently explored. In this study, we propose a new Hierarchical Covering Algorithm （HCA） method to determine the levels of a hierarchical structure based on the Covering Algorithm （CA）. The CA constructs neural networks based on samples＇ own characteristics, and can effectively handle multi-category classification and large-scale data. Further, we abstract characters based on the CA to automatically embody the feature of a deep structure. We apply CA to construct hidden nodes at the lower level, and define a fuzzy equivalence relation R on upper spaces to form a hierarchical architecture based on fuzzy quotient space theory. The covering tree naturally becomes from R. HCA experiments performed on MNIST dataset show that the covering tree embodies the deep architecture of the problem, and the effects of a deep structure are shown to be better than having a single level.
