C. Hametner, S. Jakubek:
"Nonlinear Identification with Local Model Networks Using GTLS Techniques and Equality Constraints";
IEEE Transactions On Neural Networks, 22 (2011), 9; S. 1406 - 1418.

Kurzfassung englisch:
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.

Equality constraints, generalized total least squares, local model network, nonlinear system identification.

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