Study of Random Forest to Identify Wiener–Hammerstein System
- Resource Type
- Periodical
- Authors
- Shaikh, M.A.H.; Barbe, K.
- Source
- IEEE Transactions on Instrumentation and Measurement IEEE Trans. Instrum. Meas. Instrumentation and Measurement, IEEE Transactions on. 70:1-12 2021
- Subject
- Power, Energy and Industry Applications
Components, Circuits, Devices and Systems
Forestry
Poles and zeros
Vegetation
Optimization
Estimation
Benchmark testing
Learning systems
Dynamical system
nonlinear identification
nonlinear optimization
random forest (RF)
Wiener–Hammerstein (W-H) system
- Language
- ISSN
- 0018-9456
1557-9662
The Wiener–Hammerstein (W–H) system is the most popular type of the Volterra nonlinear dynamical system. It is a combination of two dynamical subsystems, separated by a static nonlinearity. The best linear approximation (BLA) technique assembles two linear filters and the nonlinearity into a single filter for input and output. The main identification challenge resides in separating two filters. This work proposes an iterative random forest as an alternative to select the dynamics combinatorially. It is like the iterative selection of holiday destinations based on the recommendations of random travelers. The proposed technique supports reasonably high noise level and requires the optimization of a single model. Thus, a speedup in processing time is achieved without any prior knowledge about the model configuration both on simulated examples and benchmark data.