Seiya Satoh

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In the parameter space of MLP(J), multilayer perceptron with J hidden units, there exist flat areas called singular regions created by applying reducibility mappings to the optimal solution of MLP( $$J-1$$ ). Since such singular regions cause serious stagnation of learning, a learning method to avoid singular regions has been desired. However, such avoiding(More)
In the search space of a complex-valued multilayer perceptron (C-MLP) there exist flat areas called singular regions. Although singular regions cause serious stagnation of learning, there exist descending paths from the regions. Based on this observation, a completely new learning method for C-MLP, called C-SSF1.0, was proposed, making good use of singular(More)
In the search space of a complex-valued multilayer perceptron having J hidden units, C-MLP(J), there are singular regions, where the gradient is zero. Although singular regions cause serious stagnation of learning, there exist narrow descending paths from the regions. Based on this observation, a completely new learning method called C-SSF (complex(More)
A complex-valued multilayer perceptron has the capability to represent complicated periodicity. We employ a very powerful learning method called C-SSF for learning a complex-valued multilayer perceptron. C-SSF finds a series of excellent solutions through successive learning. In deterministic chaos, long-term prediction is considered impossible. We apply(More)
—In a search space of a multilayer perceptron having J hidden units, MLP(J), there exist flat areas called singular regions. Since singular regions cause serious stagnation of learning, a learning method to avoid them was once proposed, but was not guaranteed to find excellent solutions. Recently, SSF1.2 was proposed which utilizes singular regions to(More)
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