Abstract
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 is5.921×10−4, 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.
Highlights
As an important part of equipment maintenance support work, equipment maintenance support effectiveness evaluation has always been the research focus in the field of equipment support, which is mainly used to evaluate maintenance support strength, find out weaknesses in time, and formulate improvement plans, so as to improve the maintenance support effectiveness
To solve the effectiveness evaluation problem of complex, multifunctional, and poor samples, Liu et al [4] constructed a system effectiveness evaluation model based on grey radial basis function (RBF) neural network according to the three-layer structure of system effectiveness evaluation index system
Zhu et al [15] pointed out that the L2 loss function can effectively improve the distortion of samples generated by the generator due to the inaccurate gradient of generative adversarial network (GAN)’s discriminative model. erefore, this paper introduces the L2 loss function, defines a new class constraint on the basis of original antagonism constraint to determine the loss function of GAN synthetically, and ensures that GAN can generate ideal class samples without obvious distortion
Summary
As an important part of equipment maintenance support work, equipment maintenance support effectiveness evaluation has always been the research focus in the field of equipment support, which is mainly used to evaluate maintenance support strength, find out weaknesses in time, and formulate improvement plans, so as to improve the maintenance support effectiveness. E neural network has good characteristics of self-adaptive, self-learning, and strong fault tolerance, which can recognize new samples by learning database samples, and is widely used in prediction and evaluation [1,2,3]. Wang et al [2] proposed the joint operation effectiveness evaluation algorithm based on an adaptive wavelet neural network in combination with specific expert experience and an intelligent algorithm, aiming at the problems of subjectivity and uncertainty in joint operation effectiveness evaluation. In order to effectively evaluate and predict combat effectiveness of surface to air missile weapon system, Qiao and Zhao [5] reduced and decorrelated the original data through the principal component analysis method and trained the BP neural network with the principal component as the input
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