Dynamic Neural Networks for Modeling and Control of Nonlinear Systems
Dynamic Neural Networks for Modeling and Control of Nonlinear Systems
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用于非线性系统建模和控制的动态神经网络
DOI：
10.1080/10798587.2000.10642843
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
B. Aazhang
中科院分区：
文献类型：
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作者：
F. Pourboghrat;H. Pongpairoj;Ziqian Liu;F. Farid;F. Pourboghrat;B. Aazhang
摘要本文研究了一类非线性系统的动态神经网络（DNN）的设计问题。本文的主要贡献是开发了一种DNN估计器，该估计器具有用于未知（黑箱）动态非线性系统在线建模的稳定训练技术。DNN作为系统的通用模型，可以在线训练，因此可以用于实现基于模型的自适应控制策略。网络的训练基于一种新的方案，该方案将DNN隐藏层的输出安排到一组基函数中。这允许导出用于DNN的权重的训练的稳定规则，并且不需要权重的随机初始化。
Abstract This paper considers the design of a dynamic neural network (DNN) for modeling of a class of nonlinear systems for the purpose of real-time control. The primary contribution of the paper is in developing a DNN estimator with a stable training technique for on-line modeling of unknown (black box) dynamic nonlinear systems. The DNN acts as a generic model of the system, which can be trained on-line and, hence, can be utilized for the implementation of an adaptive model-based control strategy. The training of the network is based on a novel scheme that arranges the outputs of the hidden layer of the DNN into a set of basis functions. This allows for the derivation of a stable rule for the training of the DNN's weights and does not require random initialization of the weights.