Work

Utilizing a Bounding Procedure Based on Simulated Annealing to Effectively Locate the Bounds for the Parameters of Radial Basis Function Networks

Year: 2025

Type: article

Abstract: Radial basis function (RBF) networks are an established parametric machine learning tool which has been extensively utilized in data classification and data fitting problems. These specific machine learning tools have been applied in various scientific areas, such as problems in physics, chemistry, and medicine, with excellent results. A two-step technique is usually used to adjust the parameters of these models, which is in most cases extremely effective. However, it does not effectively explore the value space of the network parameters and often results in parameter stability problems. In this paper, the use of a bounding technique that explores the value space of the parameters of these networks using intervals generated by a procedure based on the Simulated Annealing method is recommended. After finding a promising range of values for the network parameters, a genetic algorithm is applied within this range of values to more effectively adjust its parameters. The new method was applied on a wide range of classification and regression datasets from the relevant literature and the results are reported in the current manuscript.

Source: Algorithms

DOI: 10.3390/a18040234

Authors: Ioannis G. Tsoulos, Vasileios Charilogis, Dimitrios Tsalikakis

Institutions: University of Ioannina, University of Western Macedonia

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funder: European Commission