Abstract: DC-DC converters are extensively deployed across a range of new energy applications. However, traditional control methods that rely on accurate state space models encounter limitations in dc ...

This paper evaluates three approaches to address parameter proliferation issue in nowcasting: (i) variable selection using adjusted stepwise autoregressive integrated moving average with exogenous ...

Abstract: System identification is essential for modeling and control of nonlinear dynamic systems. In practice, traditional linear or unidirectional recurrent models often fail to capture the ...

Some cars invite you in with chrome and comfort. The Model T invites you into a time machine, hands you three pedals that mean the wrong things, and politely asks you to learn 1910s. Then it coughs, ...

This document provides a detailed explanation of the MATLAB code that demonstrates the application of the Koopman operator theory for controlling a nonlinear system using Model Predictive Control (MPC ...

ABSTRACT: The study applies a Kalman filter (KF) to Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to create a hybrid model, to estimate the parameters of the GARCH model in ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. In this research work authors have experimentally validated a blend of Machine ...

Model Context Protocol, or MCP, is arguably the most powerful innovation in AI integration to date, but sadly, its purpose and potential are largely misunderstood. So what's the best way to really ...

Chaotic systems, such as fluid dynamics or brain activity, are highly sensitive to initial conditions, making long-term predictions difficult. Even minor errors in modeling these systems can rapidly ...

Traditional language models rely on autoregressive approaches, which generate text sequentially, ensuring high-quality outputs at the expense of slow inference speeds. In contrast, diffusion models, ...