Nonlinear system identification based on adaptive competitive clustering and OLS
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Biology as inspiration towards a novel service life-cycle
ATC'07 Proceedings of the 4th international conference on Autonomic and Trusted Computing
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Describes a type of fuzzy system with interpolating capability to extract MISO fuzzy rules from input-output sample data through learning. The proposed model inherits many merits from Sugeno-type models and their variations. A heuristic error-feedback learning algorithm associated with the model is suggested. Based on which, the estimator is shown to have a self-adjusting step when approaching a minimum