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虚拟仪表设计的核心就是根据实际生产过程各工艺参数实现数学建模,本文通过分析虚拟仪表设计中的几种数学建模技术,以聚合物黏度测量为例,验证了RBF神经网络软测量建模方法的应用,结果表明用Adaboost算法集成RBF神经网络可提高网络的分类精度,。
The core of virtual instrument design is to realize mathematical modeling according to the actual production process parameters. In this paper, by analyzing several mathematical modeling techniques in the design of virtual instruments, taking polymer viscosity measurement as an example, this paper verifies the modeling of RBF neural network soft sensor The results show that using Adaboost algorithm to integrate RBF neural network can improve the classification accuracy of the network.