Convex Optimization In Identification Of Stable Non-Linear State Space Models

TL;DR

This paper introduces a convex optimization framework for identifying stable nonlinear state space models, emphasizing robustness in simulation error and demonstrated through simulation and biological data.

Contribution

It presents a novel convex optimization approach for stable nonlinear system identification with robustness considerations, supported by analytical results.

Findings

Successfully applied to a simple simulation example

Demonstrated on experimental neuron data

Showed robustness of the identified models

Abstract

A new framework for nonlinear system identification is presented in terms of optimal fitting of stable nonlinear state space equations to input/output/state data, with a performance objective defined as a measure of robustness of the simulation error with respect to equation errors. Basic definitions and analytical results are presented. The utility of the method is illustrated on a simple simulation example as well as experimental recordings from a live neuron.