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Keywords = ACPSO

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23 pages, 4329 KiB  
Article
Integrated Aircraft Engine Energy Management Based on Game Theory
by Hong Zhang, Chenyang Luo, Xiangping Li, Runcun Li and Zhilong Fan
Aerospace 2025, 12(4), 328; https://doi.org/10.3390/aerospace12040328 - 10 Apr 2025
Viewed by 1639
Abstract
The current generation of integrated power systems is represented by the Adaptive Power and Thermal Management System (APTMS). The coupled performance between the APTMS and the aircraft engine significantly increases the difficulty of energy management and optimization. This article establishes an energy-coupled Amesim [...] Read more.
The current generation of integrated power systems is represented by the Adaptive Power and Thermal Management System (APTMS). The coupled performance between the APTMS and the aircraft engine significantly increases the difficulty of energy management and optimization. This article establishes an energy-coupled Amesim model of the APTMS and the aircraft engine to analyze performance conflicts. Energy optimization based on the Stackelberg game model is established, with the aircraft engine as the leader and the APTMS as the follower. The Adaptive Chaotic Particle Swarm Optimization (ACPSO) algorithm is introduced to search for the game equilibrium solution. Simulation results indicate that this energy management strategy can achieve equilibrium and alleviate performance conflict. In flight, the optimal strategy depends on thrust–fuel flow characteristics and cooling power demand. Finally, compared with the multi-objective optimization algorithm MOPSO and the non-cooperative Cournot game model, the advantages of this energy management system based on the Stackelberg game are verified. Full article
(This article belongs to the Special Issue Aircraft Design and System Optimization)
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23 pages, 4841 KiB  
Article
Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization
by Yudong Zhang and Lenan Wu
Sensors 2011, 11(5), 4721-4743; https://doi.org/10.3390/s110504721 - 2 May 2011
Cited by 106 | Viewed by 10115
Abstract
This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). [...] Read more.
This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Finally, a two-hidden-layer forward neural network (NN) was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO). K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP), adaptive BP (ABP), momentum BP (MBP), Particle Swarm Optimization (PSO), and Resilient back-propagation (RPROP) methods. Moreover, the computation time for each pixel is only 1.08 × 10−7 s. Full article
(This article belongs to the Section Physical Sensors)
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