Abstract
In this paper a novel method for identification of dynamical neurofuzzy system is proposed. The proposed method benefits from both LOLIMOT as the premise part optimizer of the system and the subspace identification method of N4SID to optimize the state space parameters of the conclusion part. The resulting neurofuzzy system is a nonlinear dynamical system which is modeled by some locally linear state space models. using this model it is then possible to use different parallel distributed control techniques such as linear matrix inequality to control the identified system. The proposed approach is tested on a flexible robot arm and satisfactory results are generated.
Original language | English |
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Title of host publication | 2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010 |
Number of pages | 7 |
Publication date | 2010 |
Pages | 366-372 |
Article number | 5641736 |
ISBN (Print) | 9781424465880 |
DOIs | |
Publication status | Published - 2010 |
Externally published | Yes |
Event | 2010 IEEE International Conference on Systems, Man and Cybernetics - Istanbul, Turkey Duration: 10 Oct 2010 → 13 Oct 2010 https://ieeexplore.ieee.org/xpl/conhome/5629466/proceeding |
Conference
Conference | 2010 IEEE International Conference on Systems, Man and Cybernetics |
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Country/Territory | Turkey |
City | Istanbul |
Period | 10/10/2010 → 13/10/2010 |
Internet address |
Keywords
- LOLIMOT
- N4SID
- Neurofuzzy
- Nonlinear identification
- Subspace identification