English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This article introduces the Tensor Network B-spline model for the regularizedidentification of nonlinear systems using a nonlinear autoregressive exogenous(NARX) approach. Tensor network theory is used to alleviate the curse ofdimensionality of multivariate B-splines by representing the high-dimensionalweight tensor as a low-rank approximation. An iterative algorithm based on thealternating linear scheme is developed to directly estimate the low-rank tensornetwork approximation, removing the need to ever explicitly construct theexponentially large weight tensor. This reduces the computational and storagecomplexity significantly, allowing the identification of NARX systems with alarge number of inputs and lags. The proposed algorithm is numerically stable,robust to noise, guaranteed to monotonically converge, and allows thestraightforward incorporation of regularization. The TNBS-NARX model isvalidated through the identification of the cascaded watertank benchmarknonlinear system, on which it achieves state-of-the-art performance whileidentifying a 16-dimensional B-spline surface in 4 seconds on a standarddesktop computer. An open-source MATLAB implementation is available on GitHub.

Nonlinear system identification with regularized Tensor Network B-splines