A kernel design approach to improve kernel subspace identification

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dc.contributor.authorSalgado Pilario, Karl Ezra
dc.contributor.authorCao, Yi
dc.contributor.authorShafiee, Mahmood
dc.date.accessioned2020-07-30T16:28:18Z
dc.date.available2020-07-30T16:28:18Z
dc.date.issued2020-05-27
dc.description.abstractSubspace identification methods, such as canonical variate analysis (CVA), are non-iterative tools suitable for the state-space modelling of multi-input, multi-output (MIMO) processes, e.g. industrial processes, using input-output data. To learn nonlinear system behavior, kernel subspace techniques are commonly used. However, the issue of kernel design must be given more attention because the type of kernel can influence the kind of nonlinearities that the model can capture. In this paper, a new kernel design is proposed for CVA based identification, which is a mixture of a global and local kernel to enhance generalization ability and includes a mechanism to vary the influence of each process variable into the model response. During validation, model hyper-parameters were tuned using random search. The overall method is called Feature-Relevant Mixed Kernel Canonical Variate Analysis (FR-MKCVA). Using an evaporator case study, the trained FR-MKCVA models show a better fit to observed data than those of single-kernel CVA, linear CVA, and neural net models under both interpolation and extrapolation scenarios. This work provides a basis for future exploration of deep and diverse kernel designs for system identification.en_UK
dc.identifier.citationSalgado Pilario KE, Cao Y, Shafiee M. (2021) A kernel design approach to improve kernel subspace identification. IEEE Transactions on Industrial Electronics, Volume 68, Issue 7, July 2021, pp. 6171-6180en_UK
dc.identifier.issn0278-0046
dc.identifier.urihttps://doi.org/10.1109/TIE.2020.2996142
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/15620
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectsystem identificationen_UK
dc.subjectkernel PCAen_UK
dc.subjectkernel PCAen_UK
dc.subjectNewell-Lee evaporatoren_UK
dc.subjectrandom searchen_UK
dc.titleA kernel design approach to improve kernel subspace identificationen_UK
dc.typeArticleen_UK

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