Artificial neural network for discrete model order reduction with substructure preservation
Artificial neural network for discrete model order reduction with substructure preservation
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DOI：
10.1016/j.apm.2011.03.028
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发表时间：
2011-09
影响因子：
5
通讯作者：
O. Alsmadi;Z. Abo-Hammour;A. Al-Smadi
中科院分区：
文献类型：
--
作者：
O. Alsmadi;Z. Abo-Hammour;A. Al-Smadi
提出了一种基于人工神经网络（ANN）预测的模型降阶（莫尔）新技术。基于人工神经网络的莫尔比可以应用于不同规模的系统与子结构的保护。在所提出的技术中，人工神经网络实现预测的降阶模型的未知元素。预测的人工神经网络架构的基础上最小化的成本函数获得的实际和期望的系统行为之间的差异。神经网络预测过程中进行，同时保持全阶子结构的简化模型。建议基于人工神经网络的模型降阶方法相比，最近发表的工作莫尔技术。仿真结果验证了新的莫尔技术的有效性。
This paper presents a new technique for model order reduction (MOR) that is based on an artificial neural network (ANN) prediction. The ANN-based MOR can be applied for different scale systems with substructure preservation. In the proposed technique, the ANN is implemented for predicting the unknown elements of the reduced order model. Prediction of the ANN architecture is based on minimizing the cost function obtained by the difference between the actual and desired system behaviour. The ANN prediction process is pursued while maintaining the full order substructure in the reduced model. The proposed ANN-based model order reduction method is compared to recently published work on MOR techniques. Simulation results verify the validity of the new MOR technique.