no code implementations • 30 Nov 2020 • Deepak Maurya, Arun K. Tangirala, Shankar Narasimhan
We propose a novel identification algorithm based on a modified Dynamic Iterative Principal Components Analysis (DIPCA) approach for identifying the EIV-ARX model for single-input, single-output (SISO) systems where the output measurements are corrupted with coloured noise consistent with the ARX model.
1 code implementation • 12 Aug 2020 • Chaithanya K. Donda, Deepak Maurya, Arun K. Tangirala, Shankar Narasimhan
In this work, we deal with the challenging problem of identifying order, delay in each input of minimal realization form separately while estimating the transfer functions.
Systems and Control Systems and Control
3 code implementations • 11 Aug 2020 • Deepak Maurya, Arun K. Tangirala, Shankar Narasimhan
This article is concerned with the identification of autoregressive with exogenous inputs (ARX) models.
Systems and Control Systems and Control
no code implementations • 21 May 2019 • Satya Jayadev P., Shankar Narasimhan, Nirav Bhatt
A challenging problem in complex networks is the network reconstruction problem from data.
no code implementations • 1 Jun 2015 • Aravind Rajeswaran, Shankar Narasimhan
We show that identification is equivalent to learning a model $\mathbf{A_n}$ which captures the approximate linear relationships between the different variables comprising $\mathbf{X}$ (i. e. of the form $\mathbf{A_n X \approx 0}$) such that $\mathbf{A_n}$ is full rank (highest possible) and consistent with a network node-edge incidence structure.
no code implementations • 2 May 2015 • Shankar Narasimhan, Nirav Bhatt
Data reconciliation (DR) and Principal Component Analysis (PCA) are two popular data analysis techniques in process industries.