On a multiobjective training algorithm for RBF networks using particle swarm optimization

G. R L Silva, D. A G Vieira, A. C. Lisboa, Vasile Palade

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review


This paper presents a novel algorithm for multiobjective training of Radial Basis Function (RBF) networks based on least-squares and Particle Swarm Optimization methods. The formulation is based on the fundamental concept that supervised learning is a bi-objective optimization problem, in which two conflicting objectives should be minimized. The objectives are related to the empirical training error and the machine complexity. The training is done in three steps: i) a conventional minimization of the training error, ii) multiobjective least-squares optimization for the linear parameters and, iii) particle swarm optimization for the nonlinear parameters. Some results are presented and they show the effectiveness of the proposed approach.

Original languageEnglish
Title of host publicationProceedings - 22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010
Number of pages4
ISBN (Electronic)978-0-7695-4263-8
Publication statusPublished - 17 Dec 2010
Externally publishedYes
Event22nd International Conference on Tools with Artificial Intelligence - Arras, France
Duration: 27 Oct 201029 Oct 2010


Conference22nd International Conference on Tools with Artificial Intelligence
Abbreviated titleICTAI 2010


  • Training
  • Radial basis function networks
  • Complexity theory
  • Particle swarm optimization
  • Artificial neural networks
  • Optimization
  • Machine learning
  • radial basis function networks
  • learning (artificial intelligence)
  • least squares approximations
  • particle swarm optimisation
  • linear parameters
  • multiobjective training
  • RBF networks
  • particle swarm optimization
  • least-squares approximation
  • supervised learning
  • biobjective optimization problem
  • machine complexity
  • multiobjective least-squares optimization
  • radial basis network
  • multiobjective least squares

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Science Applications


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