Nonlinear Identification With Local Model Networks Using GTLS Techniques and Equality Constraints

Local model networks approximate a nonlinear system through multiple local models fitted within a partition space. The main advantage of this approach is that the identification of complex nonlinear processes is alleviated by the integration of structured knowledge about the process. This paper exte...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2011-09, Vol.22 (9), p.1406-1418
Main Authors: Hametner, C., Jakubek, S.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Approximation
Artificial Intelligence
Computer science
control theory
systems
Constraints
Eigenvalues and eigenfunctions
Equality constraints
Exact sciences and technology
generalized total least squares
Humans
Image reconstruction
Information systems. Data bases
Least squares method
Least-Squares Analysis
local model network
Mathematical models
Memory organisation. Data processing
Models, Theoretical
Networks
Neural networks
Noise
Noise measurement
Nonlinear Dynamics
nonlinear system identification
Nonlinearity
Optimization
Parameter estimation
Partitioning algorithms
Software
Studies
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Summary:Local model networks approximate a nonlinear system through multiple local models fitted within a partition space. The main advantage of this approach is that the identification of complex nonlinear processes is alleviated by the integration of structured knowledge about the process. This paper extends these concepts by the integration of quantitative process knowledge into the identification procedure. Quantitative knowledge describes explicit dependences between inputs and outputs and is integrated in the parameter estimation process by means of equality constraints. For this purpose, a constrained generalized total least squares algorithm for local parameter estimation is presented. Furthermore, the problem of proper integration of constraints in the partitioning process is treated where an expectation-maximization procedure is combined with constrained parameter estimation. The benefits and the applicability of the proposed concepts are demonstrated by means of two illustrative examples and a practical application using real measurement data.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/TNN.2011.2159309