Weight Initialization for Simultaneous Recurrent Neural Network Trained with a Fixed-point Learning Algorithm |
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Authors: | Serpen Gursel Xu Yifeng |
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Affiliation: | (1) Electrical Engineering and Computer Science Department, The University of Toledo, Toledo, OH 43606, USA |
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Abstract: | This letter presents a study of the Simultaneous Recurrent Neural network, an adaptive algorithm, as a nonlinear dynamic system
for static optimization. Empirical findings, which were recently reported in the literature, suggest that the Simultaneous
Recurrent Neural network offers superior performance for large-scale instances of combinatorial optimization problems in terms
of desirable convergence characteristics improved solution quality and computational complexity measures. A theoretical study
that encompasses exploration of initialization properties of the Simultaneous Recurrent Neural network dynamics to facilitate
application of a fixed-point training algorithm is carried out. Specifically, initialization of the weight matrix entries
to induce one or more stable equilibrium points in the state space of the nonlinear network dynamics is investigated and applicable
theoretical bounds are derived. A simulation study to confirm the theoretical bounds on initial values of weights is realized.
Theoretical findings and correlating simulation study performed suggest that the Simultaneous Recurrent Neural network dynamics
possesses desirable stability characteristics as an adaptive recurrent neural network for addressing static optimization problems.
This revised version was published online in June 2006 with corrections to the Cover Date. |
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Keywords: | fixed-point training nonlinear dynamics recurrent backpropagation recurrent neural network static optimization weight initialization |
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