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dc.contributor.advisorWang, Wilson
dc.contributor.advisorLiu, Xiaoping
dc.contributor.authorLyu, Hongli
dc.date.accessioned2023-06-29T15:56:27Z
dc.date.available2023-06-29T15:56:27Z
dc.date.created2021
dc.date.issued2021
dc.identifier.urihttps://knowledgecommons.lakeheadu.ca/handle/2453/5193
dc.description.abstractGenerally, in real-world engineering disciplines a dynamical system is nonlinear, having multi-input and multi-output (MIMO) variables, and high level parameter uncertainties. Although there are many approaches proposed in the literature for system modeling and optimization, it remains a challenging topic to derive the precise mathematical models to characterize complex, dynamic and globally described systems. If training data in a real-world system are available, artificial neural network theories can be applied for system parameter recognition and optimization. The objective of this work is to develop a new fuzzy formulation based on the semi-tensor product (STP) method to construct fuzzy logic models for MIMO systems in a matrix representation. It involves the following processing operations: fuzzy modeling, structure and parameters identification, system optimization, and adaptive control of closed-loop fuzzy systems based on the fuzzy relation matrix (FRM) models and STP algorithms. The related contributions are summarized below. [...]en_US
dc.language.isoen_USen_US
dc.subjectSystem identificationen_US
dc.subjectSemi-tensor product (STP)en_US
dc.subjectFuzzy relation matrix (FRM)en_US
dc.subjectMIMO systemsen_US
dc.subjectMatrix representationen_US
dc.subjectAdaptive controlen_US
dc.titleSystem identification and optimization of fuzzy relation matrix models based on semi-tensor producten_US
dc.typeDissertationen_US
etd.degree.nameDoctor of Philosophyen_US
etd.degree.levelDoctoralen_US
etd.degree.disciplineEngineering: Electrical & Computeren_US
etd.degree.grantorLakehead Universityen_US
dc.contributor.committeememberLiu, Kefu
dc.contributor.committeememberTayebi, Abdelhamid


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