A dynamically generated fuzzy neural network and its application to torsional vibration control of tandem cold rolling mill spindles
A dynamically generated fuzzy neural network and its application to torsional vibration control of tandem cold rolling mill spindles
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DOI：
10.1016/s0952-1976(03)00006-x
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发表时间：
2002-12
影响因子：
8
通讯作者：
Lipo Wang;Y. Frayman
中科院分区：
文献类型：
--
作者：
Lipo Wang;Y. Frayman
基于Higgins和Goodman的模型，我们描述了一种动态生成的模糊神经网络(DGFNN)方法，该方法利用在线学习从输入输出数据进行控制。DGFNN具有从现有文献中提取或修改的以下强大特征：(1)从头开始创建小的FNN-不需要指定初始网络结构、初始隶属函数或初始权重，(2)不断组合和修剪模糊规则以在保持精度的同时最小化网络规模，检测并删除不相关的输入；以及(3)利用反向传播类型的算法训练隶属度函数和网络权重。将DGFNN控制器应用于冷连轧机主轴扭振控制的实际应用中。将DGFNN控制器的性能与传统的比例积分控制器和递归级联相关神经网络控制器的性能进行了比较。结果表明，在提高控制精度和鲁棒性的同时，DGFNN控制器在减小速度偏差和抑制主轴扭转振动方面取得了最好的效果，并且计算效率更高。
Based on the model of Higgins and Goodman, we describe a dynamically generated fuzzy neural network (DGFNN) approach to control, from input–output data, using on-line learning. The DGFNN is complete with the following powerful features drawn or modified from the existing literature: (1) a small FNN is created from scratch—there is no need to specify initial network architecture, initial membership functions, or initial weights, (2) fuzzy rules are constantly combined and pruned to minimize the size of the network while maintaining accuracy, irrelevant inputs are detected and deleted; and (3) membership functions and network weights are trained with a backpropagation-type algorithm. We apply the DGFNN controller to a real-world application of controlling the torsional vibration of tandem cold-rolling mill spindles with a simulated plant. The results of the DGFNN controller are compared with the performances of a conventional proportional-integral controller and a neural controller using recurrent cascade correlation with quickpropagation through time. We show that while both neural approaches increase the control precision and robustness, the DGFNN controller gives the best results for reducing the speed deviation and suppressing the torsional vibration of the spindles, as well as is more computationally efficient.