Efficient data-driven predictive control of nonlinear systems: A review and perspectives

Digital Chemical Engineering, Год журнала: 2025, Номер unknown, С. 100219 - 100219

Опубликована: Янв. 1, 2025

Язык: Английский

Enhanced TSMixer Model for the Prediction and Control of Particulate Matter

Sustainability, Год журнала: 2025, Номер 17(7), С. 2933 - 2933

Опубликована: Март 26, 2025

This study presents an improved deep-learning model, termed Enhanced Time Series Mixer (E-TSMixer), for the prediction of particulate matter. By analyzing temporal evolution PM2.5 concentrations from multivariate monitoring data, model demonstrates significant capabilities while maintaining consistency with observed pollutant transport characteristics in urban boundary layer. In E-TSMixer, a fully connected output layer is proposed to enhance predictive capability complex spatiotemporal dependencies. The relevant data on air quality and traffic flow are fused achieve high-precision predictions through time-series forecasting model. An asymmetric penalty mechanism added dynamically optimize loss function. Experimental results indicate that E-TSMixer achieves higher accuracy PM2.5, which significantly outperforms traditional models. Additionally, intelligent dual regulation fixed dynamic threshold introduced combined decision-making real-time adjustments frequency, routes, timing water truck operation practice.

Язык: Английский

Time-mixing and Feature-mixing Modelling for Realized Volatility Forecast: Evidence from TSMixer Model

The Journal of Finance and Data Science, Год журнала: 2024, Номер unknown, С. 100143 - 100143

Опубликована: Окт. 1, 2024

Язык: Английский