基于GAP-RBF神经网络的辊道窑烧成过程建模与优化

Liang Tang, Mingzhong Yang, Xiaomin Wang
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引用次数: 0

摘要

辊道窑烧成过程由多个子过程组成,子过程设定点与最终烧成质量之间存在未知的复杂非线性映射关系。针对这一需求,本文提出了一种基于特定神经元“显著性”的GAP方法对径向基函数(RBF)网络进行训练的算法。采用GAP方法训练网络的训练算法具有根据从实际过程中依次采集到的新数据进行构造和更新,从而动态优化各子过程的设定点等优点。仿真结果表明,该训练系统能够准确、可靠地工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling and optimization of the firing process for roller kiln using GAP-RBF neutral network
The firing process of roller kiln consists of several sub-processes and there exists unknown complex nonlinear mapping between the sub-process set points and the final firing quality. To meet this demand, a training algorithm for the radial basis function (RBF) network using GAP method based on the “significance” of a specified neuron is proposed in the paper. The training algorithm which uses GAP method to train the network has a number of advantages such as could be constructed and updated based on the new data sequentially collected from the real process in order to optimize the set point of each sub-process dynamically. Simulation results shows that this training system can work accurately and reliably.
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