Modeling and identification of parallel and feedback nonlinear systems
Structural classification and parameter estimation (SCPE) methods have been used for studying single-input single. output (SISO) parallel and feedback nonlinear system models from input-output (I-O) measurements. The uniqueness of the I-O mappings of different models and parameter uniqueness of the I-O mapping of a given structural model are evaluated. The former aids in defining the conditions under which different model structures may be differentiated from one another. The latter defines the conditions under which a given model parameter can be uniquely estimated from I-O measurements. SCPE methods presented in this paper can be further developed to study more complicated multi-input multi-output (MIMO) block-structured models which will provide useful techniques for modeling and identifying highly complex nonlinear systems.
- Research Organization:
- Los Alamos National Lab., NM (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 10187628
- Report Number(s):
- LA-UR-94-3075; CONF-941216-1; ON: DE95001006
- Resource Relation:
- Conference: 33. Institute of Electrical and Electronic Engineers (IEEE) conference on decision and control,Orlando, FL (United States),14-16 Dec 1994; Other Information: PBD: [1994]
- Country of Publication:
- United States
- Language:
- English
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