Steady-state performance constraints for dynamical models based on RBF networks

Authors:
Luis Antonio Aguirre;Gladstone Barbosa Alves;Marcelo Vieira Corrêa
Affiliations:
Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, 31270-901 Belo Horizonte, M.G., Brazil;Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, 31270-901 Belo Horizonte, M.G., Brazil;Programa de Pós-Graduação em Engenharia Elétrica, UNILESTE-MG, Av. Tancredo Neves 3500, 35170-056 Cel. Fabriciano, M.G., Brazil
Venue:
Engineering Applications of Artificial Intelligence
Year:
2007

Citing 16
Cited 1

System identification: theory for the user

System identification: theory for the user
Radial basis functions for multivariable interpolation: a review

Algorithms for approximation
Grey-box modelling and identification using physical knowledge and Bayesian techniques

Automatica (Journal of IFAC)
Dynamical effects of overparametrization in nonlinear models

Physica D
Nonlinear black-box modeling in system identification: a unified overview

Automatica (Journal of IFAC) - Special issue on trends in system identification
Nonlinear dynamics modelling via operating regime decomposition

Selected papers from the 2nd IMACS symposium on Mathematical modelling---2nd MATHMOD
Comparisons of new nonlinear modeling techniques with applications to infant respiration

Physica D
Embedding as a modeling problem

Physica D
Nonlinear prediction of chaotic signals using a normalised radial basis function network

Signal Processing
Evolutionary Radial Basis Functions for Credit Assessment

Applied Intelligence
Neural Networks: A Comprehensive Foundation (3rd Edition)

Neural Networks: A Comprehensive Foundation (3rd Edition)
Fuzzy modeling with multivariate membership functions: gray-boxidentification and control design

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Sparse modeling using orthogonal forward regression with PRESS statistic and regularization

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An alternative solution to the model structure selection problem

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Extending the functional equivalence of radial basis function networks and fuzzy inference systems

IEEE Transactions on Neural Networks
Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks

IEEE Transactions on Neural Networks

Applying fuzzy grey modification model on inflow forecasting

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper is concerned with building RBF dynamical models. The work presents a procedure by which a dynamical model is constrained using information about the system steady-state behavior. Numerical results with simulated and measured data show that the constrained RBF models have a much improved steady-state. For noise-free data such improvement happens with no obvious degradation in dynamical performance which only happens when the steady-state behavior is heavily weighed. For noisy data, however, the constrained models are superior both in steady-state and dynamically. The paper also discusses other situations in which the use of steady-state constraints turn out to be advantageous.