Network selection method based on intuitionistic fuzzy set multi-attribute decision-making

Abstract

The invention discloses a network selection method based on intuitionistic fuzzy set multi-attribute decision-making. The method comprises the following steps: determining an intuitionistic fuzzy set decision-making matrix; converting each attribute value in the intuitionistic fuzzy set decision-making matrix into a trapezoidal fuzzy number to obtain a trapezoidal fuzzy number decision-making matrix, and simultaneously obtaining a membership function of each trapezoidal fuzzy number; by use of a distance formula of an intuitionistic fuzzy set, converting the distance relation of the intuitionistic fuzzy set into an area relation of the membership function of a corresponding trapezoidal fuzzy number set, and according to the trapezoidal fuzzy number decision-making matrix, obtaining a total deviation between each network to be selected and all other networks to be selected under each attribute; based on a deviation maximum idea, establishing a weight model of attributes, and according to a total deviation value, obtaining a weight value of each attribute; and by use of an intuitionistic fuzzy set weighed averaging operator (IFWA), calculating integrated attribute values of the networks to be selected, and selecting an optimum network from the networks to be selected. According to the invention, the problem of network selection in a heterogeneous network environment can be effectively solved.

Description

technical field [0001] The invention relates to a network selection method based on intuitionistic fuzzy set multi-attribute decision-making, and belongs to the technical field of computer networks. Background technique [0002] With the rapid development of wireless communication technology, various wireless communication systems provide users with a variety of heterogeneous network environments, including wireless personal area network (such as Bluetooth), wireless local area network (such as Wi-Fi), wireless metropolitan area network ( Such as Wimax), public mobile communication networks (such as GSM, GPRS, UMTS), etc., make users face multiple choices when choosing to access the network. Since the above-mentioned various networks have great differences in coverage, data transmission rate, average packet delay, service price, mobility support capability, etc., when accessing or switching networks, it is necessary to first solve the problem of network selection. It enable...

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Problems solved by technology

[0006] However, the above-mentioned intuitionistic fuzzy set multi-attribute decision-making method is not suitable for network selection decision-making in a heterogeneous network environment, because: for the first method, the expert scoring method is difficult to apply to In the actual decision-making process, the square root method and the binary comparison rule assume that the relationship between the various factors is a linear relationship, and a simple weighted average method is used to determine the weight. Different, different dimensions, different order of magnitude, this assumption is not always true; for the second method, TOPSIS, VIKOR, and GRA methods have constants that need to be determined manually in the process of calculating weights, and the network environment is dynamically changing in real time In this method, it is difficult for decision makers to determine these constants in real time, and the distance formula used in this method only takes the parameters of the intuitionistic fuzzy set as a single value into the calculation, and due to the complexity of the network environment, the network to be selected is not suitable for the relevant The membership degree and non-membership degree of the attribute cannot be represented by a single value to represent the fuzzy information, so the above distance formula cannot distinguish the uncertain information of different intuitionistic fuzzy sets well in some cases; for The third method determines the weight according to the entropy of each attribute, and does not consider the differences of different candidate networks under the same attribute. If the entropy of a certain network attribute is large, but the differences of all networks under this attribute is very small, then the final difference in the comprehensive attribute values ​​of each network is small, which is not conducive to the final network selection decision

[0007] Therefore, the existing intuitionistic fuzzy set multi-attribute decision-making method cannot accurately measure the distance of the intuitionistic fuzzy set, and cannot dynamically determine the weight according to the real-time attribute value, and the weight value obtained is sometimes not conducive to the ranking and decision-making of the candidate network. , so it cannot effectively solve the problem of network selection

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