Research In Simulation And Applications To Marine System Of Complex Networks

Experienced development processes of graph theory caused by the seven bridges problems, ER random graph model, small-world network and scale-free network, complex network is widely used and has a tremendous impact on human life. However, in the analysis and application, some complex networks are not dynamic enough, and unreliable data will cause misjudgment, so it is extremely important to correctly analyze and apply. The main directions are random and deterministic mixed, large scale and super network. Since the application of computer network systems, complex networks have penetrated into many fields of engineering, including marine system.This paper introduces the definition of complex networks, describes a complex network of content and complexity, explains the complex network of three basic concepts:characteristic path length, clustering coefficient and degree distribution, and gives their calculation process. The generation method, statistical properties and simulation process of the most researched networks-the nearest neighbor coupled network, random graph model, WS small-world network, NW small-world network and BA scale-free network are given. Using MATLAB simulation, the corresponding network maps, characteristics distribution diagrams and the three network characteristics parameter values-characteristic path length, clustering coefficient and average degree-are got. And characteristics parameter values of affiliation network in a more than 3000KW non-automated ship's engine department with the minimum crew configuration which has 7 vertices and 18 arcs are calculated as an example. Conclusions show that the network under strict subordination less prone to clustering, but clustering coefficient has already been at the edge. Neural network fault diagnosis method has the advantages of reliable data and low false positives, and the disadvantage of limited identified dimension. Using community structure search of complex network, the thermal parameters related with the main engine fault are classified. Select a representative parameter for each classification to be used in fault diagnosis. So neural network diagnostics are optimized. Compare the result of fault diagnosis before dimension reduction with after dimension reduction. the three powerful functions of network analysis software Pajek-visualization, abstraction, and high-speed computing-and basic operation methods, such as the calculation of degrees, the distance between vertices, the k-nearest neighbor, the clustering coefficient and degree distribution are introduced. Using Pajek, a network of 13 sub-systems in the marine system is analyzed. The defined macro and the analysis report after running the macro are given, which indicates that the engine prones to more faults and generator faults prones to cause other system faults. This makes it easy to further detailed research in marine systems.Owing to time constraints, only applications of a complex network in the fault diagnosis of the marine system and network analysis of subsystems in the marine system are completed. The following three aspects need further study:In application of network analysis, more data is needed to analysis subsystems so that a super-network is formed; on-line fault diagnosis of the marine system; MATLAB and Pajek data transconductance.