# Matlab Identify Linear System

Identify a linear dynamic model from input-output or time-series data using MATLAB...

- Repository: https://github.com/matlab/matlab-agentic-toolkit (1237 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-10-27
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-identify-linear-system -g -a claude-code -y`
Codex: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-identify-linear-system -g -a codex -y`
Cursor: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-identify-linear-system -g -a cursor -y`
Gemini CLI: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-identify-linear-system -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add matlab/matlab-agentic-toolkit`

## Description

Identify a linear dynamic model from input-output or time-series data using MATLAB System Identification Toolbox. Use when estimating transfer function, state-space, ARX, ARMAX, BJ, OE polynomial or process models from measurement data.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Linear Model Identification
  Estimate a linear dynamic model from measurement data using MATLAB System Identification Toolbox. This skill selects the right model type, determines model order, estimates parameters, and validates results, following the methodology a System Identification Toolbox expert would use.

## When to Use
- Identify a transfer function, state-space, or process model from I/O data
- Determine model order from measurement data
- Fit a parametric model for simulation, prediction, or control design
- Convert frequency response data (FRD) to a parametric model
- Compare model structures (ARX vs state-space vs transfer function)  
- Determine frequency response from time-domain data
- Determine a plant model for PID tuning or control design
- Obtain a data-driven linear model when linearization of a Simulink model is not possible or practical
- Tune parameters of a physics-based model (grey-box) using data
- Compare multiple models to determine which best fits the data
- Simulate or predict system response using the identified model
- Perform subspace identification for high-order systems or MIMO systems, or use Eigenvalue Realization Algorithm (ERA) 
- Extract modal parameters (natural frequencies, damping ratios, mode shapes) from frequency response
- Compare model structures (ARX vs state-space vs transfer function)
- Study the possibility of feedback in data by analyzing the correlation between input and output signals
- Study persistence of excitation in the input signals to ensure that the data is informative enough for model identification

The rest: https://github.com/matlab/matlab-agentic-toolkit/blob/HEAD/skills-catalog/control-systems/matlab-identify-linear-system/SKILL.md