Title :
Training Winner-Take-All Simultaneous Recurrent Neural Networks
Author :
Xindi Cai ; Prokhorov, D.V. ; Wunsch, D.C.
Author_Institution :
American Power Conversion Corp., O´Fallon, MO
fDate :
5/1/2007 12:00:00 AM
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
Keywords :
Kalman filters; learning (artificial intelligence); recurrent neural nets; Kalman filter algorithms; car engine data; database management; digital processing; recurrent neural network training; shared-weight SRN architecture; sigmoid transfer function; single-layer fully connected architecture; very large scale integration design; winner-take-all simultaneous neural network; Databases; Engines; Management training; Network synthesis; Process design; Recurrent neural networks; Semiconductor device modeling; Transfer functions; Very large scale integration; Backpropagation through time (BPTT); extended Kalman filter (EKF); simultaneous recurrent network (SRN); winner-take-all (WTA); Algorithms; Artificial Intelligence; Computer Simulation; Decision Support Techniques; Feedback; Game Theory; Information Storage and Retrieval; Models, Theoretical; Neural Networks (Computer);
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.891685
