A novel Deep Neural Network architecture for non-linear system identification
This work addresses system identification for practitioners by reducing user-chosen hyper-parameters, but it appears incremental as it builds on existing DNN frameworks with added constraints.
The authors tackled the problem of non-linear system identification by proposing a novel Deep Neural Network architecture that constrains representational power to improve generalization, resulting in reduced hyper-parameter tuning and successful application to large-scale datasets.
We present a novel Deep Neural Network (DNN) architecture for non-linear system identification. We foster generalization by constraining DNN representational power. To do so, inspired by fading memory systems, we introduce inductive bias (on the architecture) and regularization (on the loss function). This architecture allows for automatic complexity selection based solely on available data, in this way the number of hyper-parameters that must be chosen by the user is reduced. Exploiting the highly parallelizable DNN framework (based on Stochastic optimization methods) we successfully apply our method to large scale datasets.