Title | ||
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SVM-Based Tree-Type Neural Networks as a Critic in Adaptive Critic Designs for Control |
Abstract | ||
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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. |
Year | DOI | Venue |
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2007 | 10.1109/TNN.2007.899255 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
learning artificial intelligence,inverted pendulum,artificial neural networks,neural network,linear program,intelligent control,least squares support vector machine,binary classification,algorithms,support vector machines,adaptive control,support vector machine,svm,dynamic programming,neural networks,computer simulation,linear programming,feedback | Intelligent control,Binary classification,Least squares support vector machine,Computer science,Support vector machine,Data type,Artificial intelligence,Binary data,Adaptive control,Artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
18 | 4 | 1045-9227 |
Citations | PageRank | References |
14 | 0.79 | 15 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Alok Kanti Deb | 1 | 14 | 0.79 |
Jayadeva | 2 | 788 | 38.14 |
Madan Gopal | 3 | 16 | 1.18 |
S. Chandra | 4 | 77 | 6.42 |