Radial Basis Function Networks

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Overview

Radial basis function networks represent a function using kernels centered at selected points, with learned or fitted weights combining their responses. Common approaches vary the kernel and its shape, choose centers using clustering or optimization, or adapt the network online for tasks such as control and solving differential equations. Researchers also use radial bases in newer architectures, including Kolmogorov-Arnold networks, and as components for clustering, representation learning, and surrogate modeling. A central challenge is selecting centers and kernel scales that balance accuracy, robustness, and computational cost across different data and function structures.

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16 papers0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

  1. CFKS: Continuum-Feature Kernel PDE Solvers

    Oct 28, 2026Vikas Dwivedi, Balaji Srinivasan, Monica Sigovan +1PDE SolvingKernel Methods

    CREATIS Biomedical Imaging Laboratory INSA, CNRS UMR 5220, Inserm, Université Lyon 1 Lyon 69621, France · Wadhwani School of Data Science and AI IIT Madras, India

  2. ER-KANs: Efficient and Robust Kolmogorov-Arnold Networks for Data-Scarce Scientific Machine Learning

    Aug 14, 2026Harshil LodhiyaKolmogorov-Arnold NetworksNeural Network Robustness

    Sliced Health

  3. An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models

    Aug 3, 2026Kei Nishihara, Yaochu Jin, Masaya NakataSurrogate ModelingSurrogate-Assisted Optimization

    Organization for the Promotion of Education, Yokohama National University, Yokohama 2408501, Japan · School of Engineering, Westlake University, Hangzhou 310030, China · Faculty of Engineering, Yokohama National University, Yokohama 2408501, Japan

  4. Risk-Aware Motion Planning with Learned Trajectory Primitives and Probabilistic Safety Assessment

    Jul 29, 2026Marc Kaufeld, Dian Zhuang, Johannes BetzCollision AvoidanceConstrained Motion Planning

    Professorship of Autonomous Vehicle Systems, Technical University of Munich, 85748 Garching, Germany; Munich Institute of Robotics and Machine Intelligence (MIRMI) · School of Engineering and Design, Technical University of Munich, 85748 Garching, Germany

  5. Influence of Radial Basis Activation Functions on Intelligent Controller for Robotic Manipulators

    Jul 2, 2026Kimmo Paldanius, Gabriel Da Silva Lima, Wallace Moreira BessaRobotic ControlRobot Trajectory Tracking

    Smart Systems Lab, Department of Mechanical Engineering, University of Turku, 20520, Finland.

  6. Machine Learning-based Feedback Linearization Control of Quadrotor Subject to Unmodeled Dynamics

    Jun 30, 2026Amos Alwala, Gabriel da Silva Lima, Wallace Moreira BessaRobot Trajectory TrackingFeedback Control

    Smart Systems, Department of Mechanical and Materials Engineering, Faculty of Technology, University of Turku, 20520 Turku, Finland.

  7. Radial Basis Function Networks as Projection Heads in Self-Supervised Learning

    Jun 19, 2026Andreas Schliebitz, Heiko Tapken, Martin AtzmuellerRepresentation LearningSelf-Supervised Visual Representation Learning

    Osnabrück University of Applied Sciences, Albrechtstr. 30, 49076 Osnabrück, Germany · Osnabrück University, Semantic Information Systems Group,2026 Wachsbleiche 27, 49090 Osnabrück Germany · German Research Center for Artificial Intelligence (DFKI), Hamburger Str. 24, 49084 Osnabrück, Germany

  8. Physics-Informed Neural Networks and Radial Basis Functions for PDEs with Dirac Delta Sources

    Jun 10, 2026Manuel Reyna, Alexandre TartakovskyPorous Media FlowPDE Solving

    Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, 205 North Mathews Avenue, Urbana, 61801, IL, USA

  9. Multi-Column RBF Neural Network Using Adaptive and Non-Adaptive Particle Swarm Optimization

    Jun 3, 2026Ammar Hoori, Yuichi MotaiMetaheuristic OptimizationNeural Network Optimization

    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA · Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA

  10. Hierarchical RBF-KAN and RBF-SKAN Architectures for Multidimensional Function Approximation and Random Field Learning

    Jun 1, 2026Mingtao Xia, Qijing ShenKolmogorov-Arnold NetworksNeural Network Approximation Theory

    University of Houston, Philip Guthrie Hoffman Hall, 3551 Cullen Blvd, Houston, 77204, Texas, United States of America · University of Birmingham, Watson Building, Birmingham, B15 2TT,, United Kingdom · University of Oxford, Henry Wellcome Building for Molecular Physiology, Old Road, Oxford, OX3 7BN, Oxfordshire, United Kingdom

  11. LLMs Without Deep Neural Networks: New Architecture, Benefits and Case Study

    May 28, 2026Vincent GranvilleNeural Network OptimizationRadial Basis Function Networks

    CAIO

  12. Adaptive RBF-KAN: A Comparative Evaluation of Dynamic Shape Parameters in Kolmogorov-Arnold Networks

    May 20, 2026Roberto Cavoretto, Alessandra De Rossi, Adeeba Haider +1Kolmogorov-Arnold NetworksKernel Methods

    Department of Mathematics “Giuseppe Peano”, University of Torino, via Carlo Alberto 10, 10123 Torino, Italy · Member of the INdAM Research Group GNCS · Department of Mathematics, Hong Kong Baptist University, Hong Kong SAR, China

  13. Partition-of-Unity Gaussian Kolmogorov-Arnold Networks

    Apr 26, 2026Amir NoorizadeganKolmogorov-Arnold NetworksKernel Methods

    Department of Mathematics, Hong Kong Baptist University, Hong Kong SAR, China

  14. K-Means as the Zero-Temperature Limit of Normalized RBF Assignments: Rates, Trajectories, and Exact Recovery with Entmax

    Mar 4, 2026Felipe de Jesus Felix Arredondo, Manuel Alejandro Ucan Puc, Carlos Astengo NouguezClusteringRadial Basis Function Networks

    School of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, Mexico · School of Engineering and Sciences, Tecnológico de Monterrey, Guadalajara, Mexico

  15. Revisiting Chebyshev Polynomial and Anisotropic RBF Models for Tabular Regression

    Feb 25, 2026Luciano Gerber, Huw LloydTabular RegressionRadial Basis Function Networks

    Department of Computing and Mathematics, Manchester Metropolitan University, Dalton Building, Chester Street, Manchester M1 5GD, United Kingdom

  16. Nonlinearity as Rank: Generative Low-Rank Adapter with Radial Basis Functions

    Feb 5, 2026Yihao Ouyang, Shiwei Li, Haozhao Wang +5Nonlinear LoRALow-Rank Adaptation

    Huazhong University of Science and Technology, Wuhan, China