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Research for predicting the underwater acoustic performance of sandwich structural composite based on support vector machine

机译：基于支持向量机的夹层结构复合材料预测研究的研究

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Support vector machine(SVM) is a new learning machine based on the statistical learning theory. For it not only has solid theory, but also can solve many practical problems in optimizing area, such as small samples, over learning, high dimension and local minima. And has shown attractive potential and promising performance in a wide range of fields and applications. In this paper, the least square support vector machine(LS-SVM), which developed from normal SVM, has been used for predicting and modeling the underwater acoustic performance of sandwich composite plates, which were composed of fiber-reinforced plastics/acoustic rubber/fiber-reinforced plastics. Effective result indicate that LS-SVM is of potential application in sandwich structural composite design and acoustic material research.
机译：支持向量机（SVM）是一种基于统计学习理论的新学习机。因为它不仅具有稳固的理论，而且还可以解决优化领域的许多实际问题，例如小型样本，过度学习，高维和局部最小值。并且在各种领域和应用中表现出有吸引力和有希望的性能。在本文中，从正常SVM开发的最小二乘支持向量机（LS-SVM）已用于预测和建模夹层复合板的水下声学性能，由纤维增强塑料/声学橡胶组成。纤维增强塑料。有效的结果表明，LS-SVM在夹层结构复合设计和声学研究中的潜在应用。

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