Abstract:The social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situation. However, most of the data‐driven models currently used assume that the input data is complete, which has obvious limitations in real‐world applications. This paper suggests an IVS‐GAN model to assess power system stabilit… Show more
This paper researches the problem of Beyond Visual Range (BVR) air combat maneuver intention recognition. To achieve efficient and accurate intention recognition, an Attention enhanced Tuna Swarm Optimization-Parallel Bidirectional Gated Recurrent Unit network (A-TSO-PBiGRU) is proposed, which constructs a novel Parallel BiGRU (PBiGRU). Firstly, PBiGRU has a parallel network structure, whose proportion of forward and backward network can be adjusted by forward coefficient and backward coefficient. Secondly, to achieve object-oriented adjustment of forward and backward coefficients, the tuna swarm optimization algorithm is introduced and the negative log-likelihood estimation loss function is used as the objective function, it realizes the dynamic combination of sequence guidance and reverse correction. Finally, the attention mechanism is used to obtain more useful information to improve the recognition accuracy. Through offline recognition experiment, it is proved that A-TSO-PBiGRU can effectively improve the convergence speed and recognition accuracy compared with GRU-related networks. Compared with the other six comparison algorithms, maneuver intention recognition accuracy also has significant advantages. In the online recognition experiment, maneuver intention recognition accuracy of A-TSO-PBiGRU is 93.7%, it shows excellent maneuver intention recognition ability.
This paper researches the problem of Beyond Visual Range (BVR) air combat maneuver intention recognition. To achieve efficient and accurate intention recognition, an Attention enhanced Tuna Swarm Optimization-Parallel Bidirectional Gated Recurrent Unit network (A-TSO-PBiGRU) is proposed, which constructs a novel Parallel BiGRU (PBiGRU). Firstly, PBiGRU has a parallel network structure, whose proportion of forward and backward network can be adjusted by forward coefficient and backward coefficient. Secondly, to achieve object-oriented adjustment of forward and backward coefficients, the tuna swarm optimization algorithm is introduced and the negative log-likelihood estimation loss function is used as the objective function, it realizes the dynamic combination of sequence guidance and reverse correction. Finally, the attention mechanism is used to obtain more useful information to improve the recognition accuracy. Through offline recognition experiment, it is proved that A-TSO-PBiGRU can effectively improve the convergence speed and recognition accuracy compared with GRU-related networks. Compared with the other six comparison algorithms, maneuver intention recognition accuracy also has significant advantages. In the online recognition experiment, maneuver intention recognition accuracy of A-TSO-PBiGRU is 93.7%, it shows excellent maneuver intention recognition ability.
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