Please use this identifier to cite or link to this item:
https://knowledgecommons.lakeheadu.ca/handle/2453/5193
Title: | System identification and optimization of fuzzy relation matrix models based on semi-tensor product |
Authors: | Lyu, Hongli |
Keywords: | System identification;Semi-tensor product (STP);Fuzzy relation matrix (FRM);MIMO systems;Matrix representation;Adaptive control |
Issue Date: | 2021 |
Abstract: | Generally, 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. [...] |
URI: | https://knowledgecommons.lakeheadu.ca/handle/2453/5193 |
metadata.etd.degree.discipline: | Engineering: Electrical & Computer |
metadata.etd.degree.name: | Doctor of Philosophy |
metadata.etd.degree.level: | Doctoral |
metadata.dc.contributor.advisor: | Wang, Wilson Liu, Xiaoping |
metadata.dc.contributor.committeemember: | Liu, Kefu Tayebi, Abdelhamid |
Appears in Collections: | Electronic Theses and Dissertations from 2009 |
Files in This Item:
File | Description | Size | Format | |
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LyuH2021d.pdf | 5.5 MB | Adobe PDF | View/Open |
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