Combat Effectiveness Evaluation of Armed Helicopter by Introducing AHP Entropy Weighting

Optimal Maintenance Model Based on Maintenance-Free Operating Period for Multi-Component Weapon Systems

/pdf/overview-on-combat-oriented-equipment-maintenance-support-3ovvxh2myo.pdf

Overview on Combat-oriented Equipment Maintenance Support Effectiveness Evaluation

Due to the lack of maintenance support samples, maintenance support effectiveness evaluation based on the deep neural network often faces the problem of small sample overfitting and low generalization ability. In this paper, a neural network evaluation model based on an improved generative adversarial network (GAN) and radial basis function (RBF) network is proposed to amplify maintenance support samples. It adds category constraint based on category probability vector reordering function to GAN loss function, avoids the simplification of generated sample categories, and enhances the quality of generated samples. It also designs a parameter initialization method based on parameter components equidistant variation for RBF network, which enhances the response of correct feature information and reduces the risk of training overfitting. The comparison results show that the mean square error (MSE) of the improved GAN-RBF model is , which is approximately 1/2 of the RBF model, 1/3 of the Elman model, and 1/5 of the BP model, while its complexity remains at a reasonable level. Compared with traditional neural network evaluation methods, the improved GAN-RBF model has higher evaluation accuracy, better solves the problem of poor generalization ability caused by insufficient training samples, and can be more effectively applied to maintenance support effectiveness evaluation. At the same time, it also provides a good reference for evaluation research in other fields.

/pdf/equipment-maintenance-support-effectiveness-evaluation-based-1jrkbgrv48.pdf

Equipment Maintenance Support Effectiveness Evaluation Based on Improved Generative Adversarial Network and Radial Basis Function Network

This paper studies the effectiveness evaluation of equipment maintenance support in the field of equipment support. Firstly, we propose a multi-layer equipment maintenance support efficiency evaluation index system, and model the problem as a weight distribution problem. Then we design a combination weighting method combining improved analytic hierarchy process (AHP) and principal component analysis (PCA) to effectively solve the problem of consistency of judgment matrix and subjective arbitrariness when there are too many indicators in AHP. We invited experts in the field of equipment maintenance support to use the 1-9 scale method to rate the importance of two indicators, and conducted 100 times of simulation of equipment maintenance support process through the distributed simulation system called Anylogic. The experimental results show that the subjective and objective combination weighting method solves the problem of effectiveness evaluation of equipment maintenance support, and is of great significance to the establishment and improvement of equipment maintenance support system.

Research on Effectiveness Evaluation of Equipment Maintenance Support Based on Simulation

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Research on Effectiveness Evaluation of Equipment Maintenance Support Based on Simulation