Non-Linear System Identification Of An Autocatalytic Reactor Using Least Squares Support Vector Machines
×
Citation (Style = APA)
Jemwa, G. T. (2003). Non-Linear System Identification Of An Autocatalytic Reactor Using Least Squares Support Vector Machines.
Citation Downloads
Reference Manager (RIS)
SpreadSheed (CSV)
Jemwa, G. T. (2003). Non-Linear System Identification Of An Autocatalytic Reactor Using Least Squares Support Vector Machines.
Citation Downloads
Reference Manager (RIS)
SpreadSheed (CSV)
Organization: The Southern African Institute of Mining and Metallurgy
Pages: 8
Publication Date:
Jan 1, 2003
Email to a Friend
Abstract
Abstract
| Paper written on project work carried out in partial fulfilment of B.Sc. Eng. (Extractive Metallurgy)degree The concepts behind support vector machines are very interesting both in theory and in practice, as they are based on a universal constructive learning procedure derived from the statistical learning theory developed by Vapnik. In this paper, their application to time series modelling is considered by means of simulated data from an autocatalytic reactor. In particular, reconstruction methods from non-linear dynamics are used to define a state space model for the process. Multivariate embedding techniques are compared to scalar embedding with respect to modelling. Keywords: System identification, Support vector machines, Multilayer perceptrons, Non-linear dynamics |
Full Article Download:
