Non-linear systems identification using radial basis functions

By Sheng Chen, Stephen A. Billings, C. Cowan, Peter M. Grant. 1990

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1. G. Em Karniadakis
2. Maizar Raissi
3. Paris Perdikaris
4. Yibo Yang
5. Liu Yang
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7. Xuhui Meng
8. Guofei Pang

Citations in the corpus

• Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems (2018, background): “…in the context of identifying nonlinear dynamics, there exist several deterministic and probabilistic tools including radial basis functions [6], neural networks [7], Gaussian processes [8, 9, 10, 11, 12], and nonlinear auto-regressive models such as NARMAX [13] and recurrent neural…”
• Non-linear system identification using Hammerstein and non-linear feedback models with piecewise linear static maps (2001, methodology): “Another class of methods for non-linear system identi®cation is based on neural network functional approximation techniques (Chen et al. 1990, Narendra and Parthasarathy 1990, Lu and Basar 1998).”