A novel Deep Neural Network architecture for non-linear system identification

• URL: http://arxiv.org/abs/2106.03078v1
• Date: Sun, 6 Jun 2021 10:06:07 GMT
• Title: A novel Deep Neural Network architecture for non-linear system identification
• Authors: Luca Zancato, Alessandro Chiuso
• Abstract summary: We present a novel Deep Neural Network (DNN) architecture for non-linear system identification. 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.
• Score: 78.69776924618505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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