Network intrusion detection is a research hotspot of network security technology. To solve this problem, a network intrusion detection algorithm based on GrC-CVM is proposed. Firstly, through the idea of hierarchical granulation of Granular computing (GrC), the attribute features of the sample are reduced, the minimum feature set is found, and redundant attributes are removed. Then, using the advantages of Core Vector Machine (CVM) in processing big data, the intrusion detection model is constructed by using the reduced feature set. Finally, the model is tested by using test set samples and compared with other relevant algorithms. The experimental results show that the algorithm can greatly improve the detection efficiency while ensuring the detection accuracy, and is superior to other algorithm models, and can be used as a network intrusion detection tool.
This paper mainly studies the motion consensus and formation control of robot swarm, and designs a new formation control and trajectory tracking algorithm based on artificial potential field method and implicit leaders. The algorithm can control the swarm to approach the desired formation and realize collision avoidance between robots. By "implicitly" integrating the leaders into the swarm, the proposed algorithm can not only guarantee a swarm of robots to move along predefined formation and track scheduled trajectory with certain velocity, dynamic leader reassignment is also realized, thus enhancing the adaptability and expansibility of the swarm. Numerical simulation results verify the correctness of the theoretical analysis and the effectiveness of the algorithm.
Robot path planning is to find the best path for robot movement considering the interference of surrounding obstacles. In this paper, the feasible region is rasterized and the robot path planning problem is transformed into abstract space that can be dealt with. In order to improve the computational efficiency, the improved particle swarm optimization algorithm combined with artificial potential field method was used to find the best moving path in the grid environment. Bessel curve is used for final smoothing operation to solve the problem of multiple path breaks in grid environment and obtain the final moving path. An example shows that this method can overcome the shortcomings of traditional algorithms and intelligent algorithms in robot path planning, and has strong search and convergence ability.
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