Recognition of sequential patterns by nonmonotonic neural networks
Masahiko Morita, Satoshi Murakami · Systems and Computers in Japan · 1999
A neural network model that recognizes sequential patterns without expanding them into spatial patterns is presented. This model forms trajectory attractors in the state space of a fully recurrent network by a simple learning algorithm using nonmonotonic dynamics. When a sequential pattern is input after learning, the network state is attracted to the corresponding learned trajectory and the incomplete part of the input pattern is restored in the input part of the model; at the same time, the output part indicates which sequential pattern is being input. In addition, this model can recognize learned patterns correctly even if they are temporally extended or contracted. © 1999 Scripta Technica, Syst Comp Jpn, 30(4): 11–19, 1999