Analysis and implementation of computer network graph based on iterative control algorithm theory

• Published in: Soft computing (Berlin, Germany) Vol. 27; no. 23; pp. 18113 - 18128
• Main Authors: Zhang, Jinfang, Rong, Jingyi, Zhang, Chunqian, Li, Yajuan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023; Springer Nature B.V
• Subjects: Algorithms; Application of Soft Computing; Artificial Intelligence; Clustering; Computational Intelligence; Computer networks; Control; Control algorithms; Control systems; Control theory; Controllers; Data analysis; Efficiency; Engineering; Graph representations; Graphical representations; Iterative methods; Lagrange multiplier; Linear control; Machine learning; Mathematical Logic and Foundations; Mechatronics; Modularity; Multiple objective analysis; Optimization; Performance indices; Perturbation; Process controls; Robotics; Robust control; Scientific visualization; Semantics; Systems design; Tracking control; Feature learning; Computer network graph; Iterative control; Network graph data
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-023-09222-5

This study aims to use the theory of iterative control algorithm to learn and extract the semantic structural features of large-scale network data by analyzing and exploring the semantic structural features of large-scale networks with the help of data visualization analysis methods. In addition, it conducts relevant research on the high-quality simplified representation of computer network graphs based on the effective extraction of structural features and combined with graph sampling techniques. An iterative learning control algorithm based on reference trajectory update is proposed for the tracking control problem of discrete linear control system output subject to non-repetitive perturbation. First, the controller is parametrically optimized by constructing a performance index function to track the system output quickly and accurately at the desired point of reference trajectory update. Second, when the system output is affected by a batch of non-repetitive perturbations, a new performance indicator function is further constructed by introducing a Lagrange multiplier algorithm to establish multi-objective performance indicators to optimize the robust iterative learning controller. Finally, the algorithm is applied to a computer network graph design system, and the simulation results verify the reasonableness and effectiveness of the algorithm. These results show that the suggested technique surpassed the existing approaches with accomplishments such as an average degree of 12.5%, a clustering coefficient of 65%, network efficiency of 91%, and higher modularity, community identification, and network embedding scores.