Abstract
The winner-take-all (WTA) network is useful in database management, very large scale integration (VLSI) design, and digital processing. The synthesis procedure of WTA on single-layer fully connected architecture with sigmoid transfer function is still not fully explored. We discuss the use of simultaneous recurrent networks (SRNs) trained by Kalman filter algorithms for the task of finding the maximum among N numbers. The simulation demonstrates the effectiveness of our training approach under conditions of a shared-weight SRN architecture. A more general SRN also succeeds in solving a real classification application on car engine data.
Recommended Citation
X. Cai et al., "Training Winner-Take-All Simultaneous Recurrent Neural Networks," IEEE Transactions on Neural Networks, Institute of Electrical and Electronics Engineers (IEEE), Jan 2007.
The definitive version is available at https://doi.org/10.1109/TNN.2007.891685
Department(s)
Electrical and Computer Engineering
Sponsor(s)
Mary K. Finley Missouri Endowment
National Science Foundation (U.S.)
Keywords and Phrases
Backpropagation Through Time (BPTT); Extended Kalman Filter (EKF); Simultaneous Recurrent Network (SRN); Winner-Take-All (WTA)
International Standard Serial Number (ISSN)
1045-9227
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2007 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2007
