Initial Estimation of Wiener-Hammerstein System with Random Forest

Adaptive System Identification via Low-Rank Tensor Decomposition

“…I N recent years, machine learning (ML) gained in popularity among the scientific and industrial as well as the signal processing communities. Although ML is usually associated with deep learning (DL) and neural networks (NN) [1] and is among the more broadly used techniques in signal processing and other fields [2]- [6], this term covers many other techniques such as random forests [7], [8], support vector machines [9]- [11], kernel adaptive filters [12]- [14] and tensorbased learning (see e.g. [15]).…”

“…For this problem machine learning-based methods gained tremendous popularity. Machine learning is mainly associated with deep learning and neural networks [1]- [5], but this field covers many other techniques such as random forests [6], [7], support vector machines [8]- [10], kernel adaptive filters [11]- [13] and tensor-based learning (cf. e.g.…”

“…D EEP LEARNING (DL) and neural networks (NNs) [1] are among the most popular techniques of machine learning (ML), and are broadly used in signal processing, among other disciplines [2]- [5]. However, the umbrella of ML covers many other techniques, such as support vector machines [6], [7], kernel adaptive filters [8], [9], random forests [10], [11] and tensor-based estimators [12]. Although it has been shown that tensor-based methods can deliver on par, or even better performance than other methods [13], [14], and can be used in a variety of applications [12], [15]- [20], they are usually disregarded due to their high memory-and computational footprint needed to approximate a given system.…”