Inherent structure detection by neural sequential associator

A sequential associator based on a feedback multilayer neural network is proposed to analyze inherent structures in a sequence generated by a nonlinear dynamical system and to predict a future sequence based on these structures. The network represents time correlations in the connection weights during learning. It is capable of detecting the inherent structure and explaining the behavior of systems. The structure of the neural sequential associator, inherent structure detection, and the optimal network size based on the use of an information criterion are discussed