Computer network traffic recognition based on improved support vector machine is a defect of current mainstream network traffic algorithm, designed a network traffic prediction algorithm based on improved support vector machine. This paper mainly introduces the computer network traffic identification method based on improved support vector machine. This article mainly analyzes the related content of network traffic prediction, including the linear and nonlinear characteristics of network traffic, the theoretical basis of network traffic prediction and the method of obtaining traffic data. This paper studies support vector machine theory and least square support vector machine, and proposes an improved algorithm for least square support vector machine. The purpose of this article is to design a network traffic identification and analysis system. On the one hand, by monitoring the network traffic, we will be able to grasp the operation of the entire network in real time;on the other hand, the system statistically analyzes the results of different stages, We have a more comprehensive understanding of the operational efficiency of network resources, network performance and the rationality of network configuration. The experimental results in this paper show that the recognition efficiency of traffic based on the improved support vector machine method has been significantly improved. Under this method, the security problem of traffic has been increased by 14%, and the efficiency of traffic has been increased by 24%. The improved support vector machine will be the future computer network The development trend of traffic identification direction.

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This paper takes the application of complex network theory approach in cognitive wireless networks as the basic background, and discusses the key technologies of cognitive wireless networks based on complex network theory. The basic goal is to optimize the cognitive wireless network's end-to-end performance.. This paper aims at the theoretical and applied research needs of cognitive wireless networks. The results of previous pre-researches are the basic starting point to study the relationship between network transport performance optimization, topology control and routing strategies based on complex network theory in cognitive wireless networks. The basic breakthroughs provide new research methods, research ideas and theories for the study of topology control theory and routing strategies in cognitive wireless networks. With the rapid development of wireless communication technology, a new generation of wireless networks is gradually moving toward diversification, isomerization, and intelligence. An important development trend of the future network is broadband and diversification of services. The main reasons for limiting it are the shortage of spectrum resources and the lack of flexibility of the existing fixed spectrum management modes. The dynamically changing heterogeneous network environment further exacerbates the contradiction between resource shortages and business requirements, thus seriously restricting the deployment and operation of existing and future wireless networks. However, actual measurement data shows that the wireless spectrum is underutilized most of the time and in some areas. In order to increase the utilization of the existing spectrum, Dr. Mitola proposed cognitive radio technology in 1999. Cognitive radios use "secondary use" of licensed spectrum. Dynamic spectrum access allows non-authorized users to perceive and access current idle frequency bands. Cognitive radio networks (CRNs) adopting cognitive radio technology not only include cognitive radio technology, but also put forward more and higher requirements at the network level. The essence is to incorporate cognitive characteristics into the overall wireless communication network. Research Significance At present, there are many researchers researching cognitive wireless networks, CORVUS in Berkeley, University of California, KNOWS system developed by the University of Maryland and Microsoft Research, OCRA project proposed by George Tech, and DRiVE project in EU. With the development of project research, the technology has made some progress in spectrum sensing, network framework, and protocol design. The IEEE has also established an IEEE 802.22 working group for the standardization of this technology. China also attaches great importance to cognitive wireless technology. Both the 863 plan in 2005 and the 973 plan in 2009 support the research of wireless cognitive technology. Compared with traditional wireless networks, cognitive wireless networks also have some new problems that need further exploration and in-depth research. For...

Abstract. Granular Computing is a new method of simulating human thinking and solving complex problems in the current field of computational intelligence research. It covers theories, methods and techniques of all relevant granularity, which is the powerful tool of studying complex problem solving, massive data mining and fuzzy information processing and so on. The main idea of granular computing approach is to solve problems at different levels of granularity, reflecting the intelligence in human problem solving process to a great extent. With the deepening of granular computing research work, different theoretical models of granular computing have been acquired from different angles, the major granular computing model includes theoretical model of fuzzy set, theory model of rough set and theory model of commercial space. This paper analyzes the theoretical basis of existing granular computing model and centers on granular computing theory study in the hierarchical order, and has conducted systematic analysis and research for the construction of hierarchical knowledge granularity space, the uncertainty of hierarchical knowledge granularity spatial structure, the uncertainty of rough sets under hierarchical knowledge granular space and knowledge acquisition based on hierarchical knowledge granularity and so on.

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