基于无气味卡尔曼滤波的GMDH神经网络设计:在隧道炉中的应用

2011 16th International Conference on Methods & Models in Automation & Robotics Pub Date : 2011-09-29 DOI:10.1109/MMAR.2011.6031345

M. Luzar, M. Mrugalski, M. Witczak, J. Korbicz

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引用次数: 3

摘要

提出了一种基于数据处理成组方法的动态系统识别方法。特别提出了一种新的动态神经元极点表示结构。此外，提出了一种新的基于无气味卡尔曼滤波的训练算法。本工作的最后一部分包含一个关于应用所提出的方法来识别隧道炉的说明性例子。

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An unscented Kalman filter approach to designing GMDH neural networks: Application to the tunnel furnace

This paper presents an identification method of dynamic systems based on the Group Method of Data Handing. In particular, a new structure of the dynamic neuron in pole representation is proposed. Moreover, a new training algorithm based on the Unscented Kalman Filter is presented. The final part of this work contains an illustrative example regarding the application of the proposed approach to an identification of the tunnel furnace.

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2011 16th International Conference on Methods & Models in Automation & Robotics

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