Proposal of relative-minimization learning for behavior stabilization of complex-valued recurrent neural networks
Relative-minimization learning using additional random teacher signal points is proposed for behavior stabilization of complex-valued recurrent neural networks. Although recurrent neural networks can deal with time-sequential data, they tend to show an unstable behavior after a large number of iterations. The proposed method superimposes a type of basin upon a dynamics-determining hypersurface in an information vector field. It is shown that this process is equivalent to the relative minimization of the error function in the input-signal partial space. Experiments demonstrate that the relative-minimization learning suppresses positive values of Lyapunov exponents down to zero or negative. It is shown that the network behavior becomes stable successfully.
learning stability lyapunov exponent recurrent network complex-valued network