Dynamics of signal processing in feedback multilayer perceptrons

Neural networks for such perceptual tasks as speech recognition must provide even more invariances than nets dealing with static problems, e.g., invariance under presentation speed fluctuations. The authors presently show that multilayer perceptrons with feedback over several layers (FMLPs) can meet these requirements. FMLPs can be trained simply with the open-loop learning rule. An analytical criterion for the stability of the resulting feedback states is given. By optimizing the output pattern representation, the stability of the feedback states can be improved. In the same way, the basins of attraction of the stable states can be enlarged. The performance with respect to presentation speed fluctuations is demonstrated in an example using three coupled FMLPs. In a continuous input sequence of letters, online detection of words is achieved. even when the presentation speed fluctuates in a wide range