Continuous-time system identification with neural networks: Model structures and fitting criteria
This work addresses system identification for dynamical systems, offering a tailored neural approach that is incremental in improving model consistency and accuracy.
The paper tackled the problem of continuous-time system identification by proposing neural model structures and custom fitting criteria to learn dynamical systems, demonstrating effectiveness through three case studies including two public benchmarks.
This paper presents tailor-made neural model structures and two custom fitting criteria for learning dynamical systems. The proposed framework is based on a representation of the system behavior in terms of continuous-time state-space models. The sequence of hidden states is optimized along with the neural network parameters in order to minimize the difference between measured and estimated outputs, and at the same time to guarantee that the optimized state sequence is consistent with the estimated system dynamics. The effectiveness of the approach is demonstrated through three case studies, including two public system identification benchmarks based on experimental data.