The design of nonlinear optimal neurocontrollers based on the Adaptive Critic Designs (ACDs) family of algorithms has recently attracted interest. This paper presents a summary of these algorithms, and compares their performance when implemented on two different types of artificial neural networks, namely the multilayer perceptron neural network (MLPNN) and the radial basis function neural network (RBFNN). As an example for the application of the ACDs, the control of synchronous generator on an electric power grid is considered and results are presented to compare the different ACD family members and their implementations on different neural network architectures.
J. Park et al., "Adaptive Critic Designs and Their Implementations on Different Neural Network Architectures," Proceedings of the International Joint Conference on Neural Networks, 2003, Institute of Electrical and Electronics Engineers (IEEE), Jan 2003.
The definitive version is available at http://dx.doi.org/10.1109/IJCNN.2003.1223694
International Joint Conference on Neural Networks, 2003
Electrical and Computer Engineering
Keywords and Phrases
Adaptive Critic Design; Control System Synthesis; Dynamic Programming; Electric Power Grid; Heuristic Programming; Learning (Artificial Intelligence); Multilayer Perceptron Neural Network; Multilayer Perceptrons; Neural Net Architecture; Neural Network Architecture; Neurocontrollers; Nonlinear Optimal Neurocontrollers; Optimal Control; Power System Control; Radial Basis Function Networks; Radial Basis Function Neural Network; Synchronous Generator
International Standard Serial Number (ISSN)
Article - Conference proceedings
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