System Identification in R

Provides functions for constructing mathematical models of dynamical systems from measured input-output data.

sysid: System Identification in R

sysid provides functions for constructing mathematical models of dynamical systems from measured input-output data. It supports discrete-time model estimation using time-domain and frequency-domain data.

Supported model structures include ARX, ARMAX, Output Error (OE), Box-Jenkins (BJ), and instrumental variable methods.

Installation

Install from CRAN:

install.packages("sysid")

Usage

library(sysid)

# Load example data (simulated ARX process)
data(arxsim)

# Split into training and validation sets
train <- dataSlice(arxsim, end = 1500)
test  <- dataSlice(arxsim, start = 1501)

# Estimate an ARX model: na=1, nb=2, nk=2
model <- arx(train, order = c(1, 2, 2))
print(model)

# Compare model predictions against validation data
compare(test, model)

# Plot residual diagnostics
residplot(model)

Documentation

Full documentation is available on CRAN.

Citation

If you use sysid in your research, please cite:

Yerramilli, S., Moudgalya, K. M., & Tangirala, A. K. (2017, January). SYSID: An open-source library for system identification. In 2017 Indian Control Conference (ICC) (pp. 53-58). IEEE.

BibTeX entry:

@inproceedings{yerramilli2017sysid,
  title={SYSID: An open-source library for system identification},
  author={Yerramilli, Suraj and Moudgalya, Kannan M and Tangirala, Arun K},
  booktitle={2017 Indian Control Conference (ICC)},
  pages={53--58},
  year={2017},
  organization={IEEE}
}

License

GPL-3