To solve the problems of combat mission predictions based on multi-instance genetic fuzzy systems

Abstract

Accurate prediction of the enemy’s combat missions in combat deductions is helpful to improve the quality of command decision. Intelligent combat mission prediction method is a technical means to adapt to the fast-paced complex modern war, and is an important part of intelligent command decision technology. In this paper, based on the solution of the problems of combat mission prediction, it is modeled as a multi-instance learning (MIL) problem according to the characteristics of the problem. To effectively integrate expert knowledge and combat deduction data, a MIL model called multi-instance genetic fuzzy system (MIGFS) is designed and implemented based on the genetic fuzzy systems (GFSs). The model is composed of multiple genetic sub-fuzzy systems (sub-FSs). By means of multi-tasking genetic fuzzy systems (MTGFSs) algorithm, multiple sub-FSs have been trained separately to solve the problem of excessive time consuming and high cost because of synchronously training too many sub-FSs. Furthermore, to complete the integration of instance prediction to bag prediction, the weighted average is used instead of the traditional max function, and the mutation problem of taking the max function is solved by learning the weight parameters of the sub-FSs. A more continuous and smooth result integration method is implemented, and the prediction accuracy is improved. Finally, a demonstrative experimental case of wargaming has been taken to illustrate that MIGFS can successfully apply MIL to combat deductions with a small amount of data, and effectively solve the problems of combat mission prediction, which demonstrates the feasibility and practicability of the proposed MIGFS model. This method can well model the problems of combat mission prediction into MIL problems, and solve the problems well, which is a good start.