Deb Alok Kanti, Gopal Madan, Chandra Suresh
Department of Electrical Engineering, Indian Institute of Technology (IIT), New Delhi 110016, India.
IEEE Trans Neural Netw. 2007 Jul;18(4):1016-30. doi: 10.1109/TNN.2007.899255.
In this paper, we use the approach of adaptive critic design (ACD) for control, specifically, the action-dependent heuristic dynamic programming (ADHDP) method. A least squares support vector machine (SVM) regressor has been used for generating the control actions, while an SVM-based tree-type neural network (NN) is used as the critic. After a failure occurs, the critic and action are retrained in tandem using the failure data. Failure data is binary classification data, where the number of failure states are very few as compared to the number of no-failure states. The difficulty of conventional multilayer feedforward NNs in learning this type of classification data has been overcome by using the SVM-based tree-type NN, which due to its feature to add neurons to learn misclassified data, has the capability to learn any binary classification data without a priori choice of the number of neurons or the structure of the network. The capability of the trained controller to handle unforeseen situations is demonstrated.
在本文中，我们使用自适应评判设计（ACD）方法进行控制，具体而言，是基于动作的启发式动态规划（ADHDP）方法。采用最小二乘支持向量机（SVM）回归器来生成控制动作，同时将基于SVM的树型神经网络（NN）用作评判器。故障发生后，利用故障数据对评判器和动作进行联合重新训练。故障数据是二元分类数据，与无故障状态的数量相比，故障状态的数量非常少。通过使用基于SVM的树型神经网络克服了传统多层前馈神经网络在学习这类分类数据时的困难，该树型神经网络由于具有添加神经元以学习误分类数据的特性，能够在无需事先选择神经元数量或网络结构的情况下学习任何二元分类数据。展示了训练有素的控制器处理意外情况的能力。
