An Intelligent Mission Planning Model for the Air Strike Operations against Islands Based on Neural Network and Simulation. (4th January 2022)
- Record Type:
- Journal Article
- Title:
- An Intelligent Mission Planning Model for the Air Strike Operations against Islands Based on Neural Network and Simulation. (4th January 2022)
- Main Title:
- An Intelligent Mission Planning Model for the Air Strike Operations against Islands Based on Neural Network and Simulation
- Authors:
- Song, Zhihua
Zhang, Han
Zhao, Yongmei
Dong, Tao
Zhang, Fa - Other Names:
- Ma Lianbo Academic Editor.
- Abstract:
- Abstract : Mission planning of air strike operations is hard because it has to give instructions to a large number of units during a relatively long period of time in an uncertain environment. If some instruction parameters can be calculated by an intelligent agent, better strategies can be found more quickly. In a specific combat scenario of air strike operations against islands, an intelligent model is proposed to improve the performance and flexibility of mission planning. The proposed intelligent mission planning model is based on rule-based decision and uses a fully connected recurrent neural network to calculate some of the decision parameters. The proposed intelligent mission planning model shows better results as compared to rule-based decision making with randomized parameters, and it performs as good as experts in the test set of the specific combat scenario.
- Is Part Of:
- Discrete dynamics in nature and society. Volume 2022(2022)
- Journal:
- Discrete dynamics in nature and society
- Issue:
- Volume 2022(2022)
- Issue Display:
- Volume 2022, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 2022
- Issue:
- 2022
- Issue Sort Value:
- 2022-2022-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-01-04
- Subjects:
- System analysis -- Periodicals
Dynamics -- Periodicals
Chaotic behavior in systems -- Periodicals
Differentiable dynamical systems -- Periodicals
003.05 - Journal URLs:
- https://www.hindawi.com/journals/ddns/ ↗
- DOI:
- 10.1155/2022/8172907 ↗
- Languages:
- English
- ISSNs:
- 1026-0226
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 20545.xml