Constraint optimization of input-driven recurrent neural networks
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چکیده
We introduce a novel constraint optimization approach for a class of input-driven recurrent neural networks. A unified network model allows algebraic derivation of optimal network parameters using the Lagrange multiplier method. Regularization of weights serves as optimality criterion, while the solution is constraint to implement given network state dynamics. We derive the analytical, closed form solution, which is based on solving a linear system of equations, and show results in experiments.
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