Identification and adaptive control scheme using fuzzy parameterized linear filters
conference paper
A nonlinear fuzzy control structure enhanced with supervised learning and/or adaption is presented. Availability of at least a partial process model is assumed. Nonlinear process identification procedure is used to complete the partial model. Based on the identification model the system sensitivity model is derived which guides the training process to keep the training time at minimum. The identification model introduced consists of a linear dynamical section and a nonlinear zero-memory section. Only the filter section is on the primary signal path. The nonlinear mapping delivers the filter parameters. An adaption procedure is introduced, which tunes the nonlinear mapping to minimize identification error.
Topics
TNO Identifier
234319
Publisher
IEEE
Source title
Proceedings of the 1998 IEEE International Conference on Fuzzy Systems, 4-9 May 1998, Anchorage, AK, USA
Place of publication
Piscataway, NJ, USA
Pages
468-473
Files
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