Effectiveness Evaluation of Underwater Swarm Combat Based on FAHP and GA-Elman Neural Network

Abstract

In modern naval combat, vigorous development of underwater combat swarm is an inevitable way to develop naval informatization. In view of this new combat mode, it is essential to evaluate its operational effectiveness accurately and reliably. In this paper, to address the problem of underwater swarm operational effectiveness evaluation, we firstly, based on analysis of the construction of an operational effectiveness evaluation index system, determine the weight vector of the index by using Fuzzy Analytic Hierarchy Process (FAHP). Secondly, combining the respective advantages of the genetic algorithm (GA) and the Elman neural network, we use the genetic algorithm to optimize the weight and threshold of the Elman network globally in order to overcome the shortcoming that the network’s own weight updating method can easily fall into a local minimum. Then, an underwater swarm operational effectiveness evaluation model based on the GA-Elman neural network is constructed. Finally, an example is simulated to verify the model, the simulation results show that the model can accurately and effectively evaluate the operational effectiveness of the underwater swarm.