Designing a Battlefield Fire Support System Using Adaptive Neuro-Fuzzy Inference System Based Model

Kerim Goztepe

doi:10.14429/dsj.63.3716

Kerim Goztepe Army War College, Yenilevent-34330, İstanbul, Turkey

DOI: https://doi.org/10.14429/dsj.63.3716

Keywords: Fire support, adaptive neuro-fuzzy inference system, ANFIS, fuzzy logic, neural network

Abstract

Fire support of the maneuver operation is a continuous process. It begins with the receiving the task by the maneuver commander and continues until the mission is completed. Yet it is a key issue in combat in the way gain success. Therefore, a real-time mannered solution to fire support problem is a vital component of tactical warfare to the sequence that auxiliary forces or logistic support arrives at the theatre. A new method for deciding on combat fire support is proposed using adaptive neuro-fuzzy inference system (ANFIS) in this paper. This study addresses the design of an ANFIS as an efficient tool for real-time decision-making in order to produce the best fire support plan in battlefield. Initially, criteria that are determined for the problem are formed by applying ANFIS method. Then, the ANFIS structure is built up by using the data related to selected criteria. The proposed method is illustrated by a sample fire support planning in combat. Results showed us that ANFIS is valid especially for small unit fire support planning and is useful to decrease the decision time in battlefield.

Defence Science Journal, 2013, 63(5), pp.497-501, DOI:http://dx.doi.org/10.14429/dsj.63.3716

Author Biography

Kerim Goztepe, Army War College, Yenilevent-34330, İstanbul, Turkey

Dr Kerim Goztepe received the BS (System Engineering) from Turkish Military Academy, in 1998 and MS (Engineering Management) from Marmara University, in 2003. He has obtained his PhD in Industrial Engineering, Institute of Science and Technology at Sakarya University, in 2010. He is an author of 22 refereed papers, and associate editor of International Journal of Cryptography and Security. He is interested in fuzzy logic, neural network, multicriteria decision making and cyber security for open source systems.