Network Structure Identification Based on Measured Output Data Using Koopman Operators

Model-Based Control Using Koopman Operators

This paper explores the application of Koopman operator theory to the control of robotic systems. The operator is introduced as a method to generate data-driven models that have utility for model-based control methods. We then motivate the use of the Koopman operator towards augmenting modelbased control. Specifically, we illustrate how the operator can be used to obtain a linearizable data-dri...

Improving Data-based Wind Turbine Using Measured Data Foggy Method

The purpose of this paper is to improve the modeling of the data-driven wind turbine system that receives data from noise signals. Most of the data on industrial systems is noisely and data noise is inevitable and natural. The method and idea proposed in this paper, Data Fogging, significantly reduce the impact of noise on data-driven wind turbine system modeling, which is the basis of this met...

Spatiotemporal Feature Extraction with Data-Driven Koopman Operators

We present a framework for feature extraction and mode decomposition of spatiotemporal data generated by ergodic dynamical systems. Unlike feature extraction techniques based on kernel operators, our approach is to construct feature maps using eigenfunctions of the Koopman group of unitary operators governing the dynamical evolution of observables and probability measures. We compute the eigenv...

Weighted Frobenius-Perron and Koopman operators

Identification of Multiple Input-multiple Output Non-linear System Cement Rotary Kiln using Stochastic Gradient-based Rough-neural Network

Because of the existing interactions among the variables of a multiple input-multiple output (MIMO) nonlinear system, its identification is a difficult task, particularly in the presence of uncertainties. Cement rotary kiln (CRK) is a MIMO nonlinear system in the cement factory with a complicated mechanism and uncertain disturbances. The identification of CRK is very important for different pur...