A Radial Basis Function network training algorithm using a non-symmetric partition of the input space - Application to a Model Predictive Control configuration

Authors:
Alex Alexandridis;Haralambos Sarimveis;Konstantinos Ninos
Affiliations:
Department of Electronics, Technological Educational Institute of Athens, Agiou Spiridonos, Aigaleo 12210, Greece;School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechniou Street, Zografou Campus, Athens 15780, Greece;School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechniou Street, Zografou Campus, Athens 15780, Greece
Venue:
Advances in Engineering Software
Year:
2011

Citing 10
Cited 3

Logical radial basis function networks a hybrid intelligent model for function approximation

Advances in Engineering Software
Design of structural modular neural networks with genetic algorithm

Advances in Engineering Software
A new EM-based training algorithm for RBF networks

Neural Networks
A new algorithm for online structure and parameter adaptation of RBF networks

Neural Networks
Adaptive neural network model based predictive control for air-fuel ratio of SI engines

Engineering Applications of Artificial Intelligence
Fuzzy control of multivariable nonlinear servomechanisms with explicit decoupling scheme

IEEE Transactions on Fuzzy Systems
Effects of moving the center's in an RBF network

IEEE Transactions on Neural Networks
A parameter optimization method for radial basis function type models

IEEE Transactions on Neural Networks
On global-local artificial neural networks for function approximation

IEEE Transactions on Neural Networks
A Hybrid Forward Algorithm for RBF Neural Network Construction

IEEE Transactions on Neural Networks

Density based grid clustering partition of the input space for RBF neural network

AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Parameterization of point-cloud freeform surfaces using adaptive sequential learning RBFnetworks

Pattern Recognition
Stabilization of gas-lift oil wells by a nonlinear model predictive control scheme based on adaptive neural network models

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work presents the non-symmetric fuzzy means algorithm which is a new methodology for training Radial Basis Function neural network models. The method is based on a non-symmetric fuzzy partition of the space of input variables which results to networks with smaller structures and better approximation capabilities compared to other state-of-the-art training procedures. The lower modeling error and the smaller size of the produced models become particularly important when they are used in online applications. This is demonstrated by integrating the model produced by the proposed algorithm in a Model Predictive Control configuration, resulting in better control performance and shorter computational times.