Multi-Stream Extended Kalman Filter Training of Neural Networks on a SIMD Parallel Machine
Abstract
The extended Kalman filter (EKF) algorithm has been shown to be advantageous for neural network trainings. This paper presents a method to do the EFK training on a SIMB parallel machine. We use multi-stream decoupled extended Kalman filter (DEKF) training algorithm which can provide more improved trained network weights and efficient use of the parallel resource. The performance of the parallel DEKF training algorithm is studied and simulation results for the estimation of the wind power using neural networks are provided.
Recommended Citation
S. Li et al., "Multi-Stream Extended Kalman Filter Training of Neural Networks on a SIMD Parallel Machine," Intelligent Engineering Systems Through Artificial Neural Networks, American Society of Mechanical Engineers (ASME), Nov 1999.
Meeting Name
Artificial Neural Networks in Engineering Conference, ANNIE '99 (1999: Nov. 7-10, St. Louis, MO)
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Artificial Intelligence; Neural Networks
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 1999 American Society of Mechanical Engineers (ASME), All rights reserved.
Publication Date
10 Nov 1999
