Solving Nonlinear Equations Using Recurrent Neural Networks
نویسنده
چکیده
Abstract A class of recurrent neural networks is developed to solve nonlinear equations, which are approximated by a multilayer perceptron (MLP). The recurrent network includes a linear Hopfield network (LHN) and the MLP as building blocks. This network inverts the original MLP using constrained linear optimization and Newton’s method for nonlinear systems. The solution of a nonlinear equation with computer simulation illustrates the algorithm.
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