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首页> 外文会议>IEEE international conference on computer science and information technology;ICCSIT 2010 >Lifetime Prediction Model of Cylinder Based on Genetic Support Vector Regression
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Lifetime Prediction Model of Cylinder Based on Genetic Support Vector Regression

机译：基于遗传支持向量回归的气缸寿命预测模型

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In order to solve the problem of BP neural network, genetic support vector regression is presented to predict the lifetime of cylinder. Support vector regression (SVR) is a novel prediction algorithm based on structure risk minimization principles, which can lead to great generalization ability. In the genetic support vector regression model, the genetic algorithm is used to optimize the parameters of support vector regression. That's because that the generalization ability of support vector regression is controlled by its parameters. The wear rate data of 20 mileages are employed to study the lifetime prediction of cylinder by the GSVR model. In order to prove the superiority of GSVR in lifetime prediction of cylinder, the RBF neural network and BP neural network are employed to compare with GSVR. The results of the experiments show that the lifetime prediction model of cylinder by the GSVR is better than that of RBF neural network, BP neural network.
机译：为了解决BP神经网络的问题，提出了遗传支持向量回归来预测汽缸的寿命。支持向量回归（SVR）是一种基于结构风险最小化原理的新颖预测算法，可以带来很大的泛化能力。在遗传支持向量回归模型中，遗传算法用于优化支持向量回归的参数。这是因为支持向量回归的泛化能力受其参数控制。利用20英里的磨损率数据，通过GSVR模型研究气缸的寿命预测。为了证明GSVR在汽缸寿命预测中的优越性，采用RBF神经网络和BP神经网络与GSVR进行比较。实验结果表明，基于GSVR的汽缸寿命预测模型优于RBF神经网络，BP神经网络。

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