Particle Swarm Optimization Approach for Multi-step-ahead Prediction using Radial Basis Function Neural Network
| Authors: | dos Santos Coelho Leandro, Pontifical Catholic University of Parana, Brazil
Sierakowski Cezar, Pontifical Catholic University of Parana, Brazil
Guerra Fábio, TECPAR – Parana Institute of Technology, Brazil |
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| Topic: | 3.2 Cognition and Control ( AI, Fuzzy, Neuro, Evolut.Comp.) |
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| Session: | Neural Networks in Modelling and Control |
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| Keywords: | Neural network, clustering, chaotic circuit, Rössler’s circuit, multivariable system identification, particle swarm optimization, nonlinear systems. |
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Abstract
An alternative approach, between much others, for mathematical representation of dynamics systems with complex or chaotic behaviour, is a radial basis function neural network using k-means for clustering and optimized by pseudo-inverse and particle swarm optimisation. This paper presents the implementation and study to identify a dynamic system, with nonlinear and chaotic behaviour, called Rössler’s circuit, with concepts of multi-step-ahead prediction.
