Recursive subspace identification of Hammerstein models based on least squares support vector machines

Modeling and identification of heat exchanger process using least squares support vector machines

“…Many identification algorithms have been developed for Hammerstein models since Narendra's method [15]. Bako et al [16] proposed a recursive subspace identification algorithm for state space Hammerstein models using least squares support vector machines (LS-SVM) to estimate the non-linear part of the system and ordinary least squares for recovering the linear part.…”

Support Vector Machine Identification of Subspace Hammerstein Models

“…However, they used a least square support vector machine to model the nonlinear part of the Hammerstein system. Recently, Bako et al [14] extended Goethals work to time-varying systems by using LS-SVM to recursively estimate the non-linear part of the system and ordinary least squares for recovering the linear part in state space form. The LS-SVM solution proposed in [13] lacks sparseness.…”

Subspace identification of Hammerstein systems with nonparametric input backlash and switch nonlinearities

“…The use of such methods is mainly motivated by a set of interesting properties: the simplicity, the intrinsic numerical robustness and their straightforward application to MIMO systems ( [26], [29], [30], [10], [18], [23], [24], [9]). Some subspace methods adapted to Hammerstein systems have been introduced ( [15], [14], [3], [33], [28], [17], [21]) but, to the best of our knowledge, these methods aren't adapted to the case of backlash or switch nonlinearity.…”