Gradient-Optimized Fuzzy Classifier: A Benchmark Study Against State-of-the-Art Models

This paper presents a performance benchmarking study of a Gradient-Optimized Fuzzy Inference System (GF) classifier against several state-of-the-art machine learning models, including Random Forest, XGBoost, Logistic Regression, Support Vector Machines, and Neural Networks. The evaluation was conducted across five datasets from the UCI Machine Learning Repository, each chosen for their diversity in input types, class distributions, and classification complexity. Unlike traditional Fuzzy Inference Systems that rely on derivative-free optimization methods, the GF leverages gradient descent to significantly improving training efficiency and predictive performance. Results demonstrate that the GF model achieved competitive, and in several cases superior, classification accuracy while maintaining high precision and exceptionally low training times. In particular, the GF exhibited strong consistency across folds and datasets, underscoring its robustness in handling noisy data and variable feature sets. These findings support the potential of gradient optimized fuzzy systems as interpretable, efficient, and adaptable alternatives to more complex deep learning models in supervised learning tasks.

A Genetic Fuzzy-Enabled Framework on Robotic Manipulation for In-Space Servicing

Automation of robotic systems for servicing in cislunar space is becoming extremely important as the number of satellites in orbit increases. Safety is critical in performing satellite maintenance, so the control techniques utilized must be trusted in addition to being highly efficient. In this work, Genetic Fuzzy Trees are combined with the widely used LQR control scheme via Thales' TrUE AI Toolkit to create a trusted and efficient controller for a two-degree-of-freedom planar robotic manipulator that would theoretically be used to perform satellite maintenance. It was found that Genetic Fuzzy-LQR is 18.5% more performant than optimal LQR on average, and that it is incredibly robust to uncertainty.