Recurrent and non-recurrent dynamic network paradigms: a case study

Very often, recurrent neural networks are used to model dynamic, nonlinear relationships. However, particularly in many technical applications recurrent networks do not perform noticeably better than static networks (like e.g. Multilayer Perceptrons) processing only the current input pattern. The main reason for this observation is that the networks have to cope with noisy input and sometimes even noisy output data. Large weights in recurrent connections may cause stability problems and with small weights temporal information cannot be considered in an appropriate way. This paper demonstrates by means of an application example (tool wear monitoring in turning) that in these cases dynamic, non-recurrent paradigms like Time Delay neural networks should be preferred. These networks use tapped delay lines (delay elements in feedforward direction) to take past input information into account. Due to their noise suppression capabilities dynamic, non-recurrent networks can be superior to dynamic, recurrent paradigms like NARX-networks. The paper presents the first use of such a non-recurrent network in the mentioned application area and shows that the results of a wear estimation can be improved significantly