We study the nonlinear dynamics of multilayer perceptrons with feedback and propose their application to the analysis of signals with complex time dependence. We show that their dynamics provides a built-in time-warping invariance, as, e.g., required for presentation speed fluctuations in speech recognition. We suggest an appropriate learning rule (open-loop learning), give an analytical stability condition for the resulting multistable states, and determine their basins of attraction. To demonstrate their utility for possible applications, we consider the example of a three-stage feedback multilayer perceptron that is trained to detect words in a sequence of letters and does it with perfect invariance with respect to presentation speed fluctuations.

• Received 9 March 1990

DOI:https://doi.org/10.1103/PhysRevA.42.2401