Why Your 'Accurate' AI Model Might Still Be Dangerously Wrong: The Hidden Importance Of Model Calibration

Trustworthy AI isn’t just about predicting the right outcome; it’s about knowing how confident we should actually be.
TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and data preprocessing. If you''ve ever built a predictive model, worked on a ...
IEEE

Practical Nonlinear Model Predictive Control With Kolmogorov–Arnold Network Models

Abstract: This paper presents a novel approach to practical nonlinear model predictive control (PNMPC) using Kolmogorov–Arnold networks (KANs) as prediction models. KANs are based on the ...
IEEE

Modified Model-Free Predictive Control for Reliable Operation of Multiple Parallel Grid-Forming Inverters

Abstract: The increasing integration of parallel grid-forming (GFM) inverters within islanded AC microgrids introduces significant challenges in maintaining robust voltage regulation and accurate ...