Adaptive Critic Designs and their Implementations on Different Neural Network Architectures

Jung Wook Park, G. K. Venayagamoorthy, Ronald G. Harley

Research output: Contribution to conferencePaper

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages1879-1884
Number of pages6
Publication statusPublished - 2003
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: 2003 Jul 202003 Jul 24

Other

OtherInternational Joint Conference on Neural Networks 2003
CountryUnited States
CityPortland, OR
Period03/7/2003/7/24

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Adaptive Critic Designs and their Implementations on Different Neural Network Architectures'. Together they form a unique fingerprint.

  • Cite this

    Park, J. W., Venayagamoorthy, G. K., & Harley, R. G. (2003). Adaptive Critic Designs and their Implementations on Different Neural Network Architectures. 1879-1884. Paper presented at International Joint Conference on Neural Networks 2003, Portland, OR, United States.