A control system and method for a fully electric driven aircraft tractor
By using technical means such as adversarial perturbation generation adversarial network, quantum dynamic evolution neural network and feature refinement auto-coding neural network in the control system of all-electric drive aircraft tractors, the problems of weak data processing capabilities and low decision-making accuracy of existing systems are solved, and stronger data processing capabilities and higher decision-making accuracy are achieved.
Patent Information
- Application Number
- CN202411590058.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-08
- Publication Date
- 2025-03-25
- Estimated Expiration
- 2044-11-08
AI Technical Summary
The data processing capability of the existing fully electric-driven aircraft tractor control system is weak, making it difficult to effectively process complex and high-dimensional driving data, resulting in poor feature extraction effect and loss of information or retaining irrelevant information during feature dimensionality reduction, thereby reducing decision-making accuracy.
The generative adversarial network algorithm based on adversarial perturbation is used to generate and expand driving data, combine the neural network algorithm with quantum dynamic evolution for feature extraction, and use the auto-encoded neural network based on feature refinement for feature dimensionality reduction. At the same time, an integrated gradient boosting tree algorithm based on dynamic decision boundaries is used in the tractor control module for classification decisions.
It significantly improves data processing capabilities and decision-making accuracy, can effectively capture important information in complex driving data, reduce the risk of information loss, and improve the generalization ability of the model and the ability to distinguish complex samples.