Sequence Processing Neural Network with a Non-Monotonic Transfer Function : General Physics
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概要
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We investigate storage capacity and retrieval property for a synchronous fully connected neural network with a non-monotonic transfer function which retrieves sequences of patterns, by an analytic method and also by numerical simulations. Because of asymmetry of interactions and non-monotonicity of the transfer function, it is difficult to use conventional methods of the equilibrium statistical mechanics in order to investigate the network. We then use a generating-function method of path-integral representation, and obtain equations for dynamical order parameters in the stationary state. We clarify that the network with the non-monotonic transfer function retrieves more sequences of patterns than that with a monotonic transfer function at zero temperature when non-monotonicity of the transfer function is selected optimally. It is also clarified that some chaotic behavior appears in solutions for the equations of the dynamical order parameters when non-monotonicity of the transfer function increases. The analytic results are in excellent agreement with the results obtained by numerical simulations.
- 社団法人日本物理学会の論文
- 2001-05-15
著者
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Katayama Katsuki
Department Of Computer And Mathematical Sciences Graduate School Of Information Sciences Tohoku Univ
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Horiguchi Tsuyoshi
Department Of Computer And Mathematical Sciences Graduate School Of Information Sciences Tohoku Univ
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Horiguchi Tsuyoshi
Department Of Applied Science Faculty Of Engineering Tohoku University
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