Abstract
In traditional data-driven deep learning guidance, variations in guidance tasks may result in sharp performance degradation in terms of accuracy and efficiency of pretrained models. However, obtaining a guidance model adaptable to new tasks requires a substantial investment of training data and time. To address this issue, this paper proposes a small-data-driven multitask computational guidance (SMCG) algorithm utilizing a novel gated progressive neural network structure. The SMCG closely emulates the human learning process. Specifically, it progressively acquires knowledge from simple to complex tasks (from easy to difficult), applies previously learned knowledge from old tasks to new ones (reusing old knowledge), and achieves continuous learning for new tasks by adding new columns (sustainable learning). Simulation results demonstrate that SMCG can effectively reuse expertise acquired from previous tasks in new tasks, thereby reducing the required training data and enhancing the adaptability of the guidance model to new tasks.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.