Combat simulation using continuous time neural networks

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Moghaddas Mohammad; BIGDELI HAMID

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: DEFENSIVE FUTURE STUDY Year:2018 | Volume:3 | Issue:10 Page(s): 7-19

Download Full-Text

Persian Verion

View:

274

Download:

Cites:

Information Journal Paper

Title

Combat simulation using continuous time neural networks

Author(s)

Moghaddas Mohammad | BIGDELI HAMID | Issue Writer Certificate

Keywords

Combat simulationQ1

Neural networkQ2

Multi-objective optimizationQ1

Abstract

This paper focuses on modeling the behavior of commanders in a Combat simulation. A military mission is often associated with multiple conflicting goals, including task success, completion time, enemies’ elimination, and own forces survival. In this paper, considering defensive and non-defensive scenarios, and using Multi-objective optimization, a model is presented in order to minimize own forces loss and to maximize enemies’ elimination. Also, based on the weighting method and the Karush-Kuhn-Tucker optimality conditions, a continuous time feedback Neural network model is designed for solving the proposed Multi-objective optimization problem. The main idea of the Neural network approach for the proposed Multi-objective optimization problem is to establish a dynamic system in the form of first order ordinary differential equations. The proposed Neural network does not require any adjustable parameter and its structure enables a simple hardware implementation. The proposed method can act as a consultant for the commander who decides for its forces. Finally, the validity and efficiency of the proposed model are demonstrated by an example.

Cites

No record.

References

No record.

Cite

APA: Copy

Moghaddas, Mohammad, & BIGDELI, HAMID. (2018). Combat simulation using continuous time neural networks. DEFENSIVE FUTURE STUDY, 3(10 ), 7-19. SID. https://sid.ir/paper/369330/en

Vancouver: Copy

Moghaddas Mohammad, BIGDELI HAMID. Combat simulation using continuous time neural networks. DEFENSIVE FUTURE STUDY[Internet]. 2018;3(10 ):7-19. Available from: https://sid.ir/paper/369330/en

IEEE: Copy

Mohammad Moghaddas, and HAMID BIGDELI, “Combat simulation using continuous time neural networks,” DEFENSIVE FUTURE STUDY, vol. 3, no. 10 , pp. 7–19, 2018, [Online]. Available: https://sid.ir/paper/369330/en

Related Journal Papers