Application of neuro-fuzzy identifier for a fossil fuel boiler system
Hamid Ghezelayagh, Kyu Young Lee · 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077) · 2002
A multi-layer neuro-fuzzy system presents identification of a drum type boiler. This identification provides a rule-based approach to approximate the boiler dynamics from the experimental boiler data. The interconnections of neuro-fuzzy layers furnish these fuzzy rules. A genetic algorithm (GA) trains the neuro-fuzzy identifier and extracts the linguistic rules from measured boiler data. GA training takes the advantages of nonbinary alphabet and compound chromosomes to train the multi-input multi-output (MIMO) neuro-fuzzy identifier. The fuzzy membership functions need to be adjusted during the training to minimize the identifier response error. Error back-propagation training methodology is chosen to tune the membership function parameters. The identifier response is investigated in several operating points. This neuro-fuzzy identifier is implemented within an object oriented programming tool that provides portability of the identification process. Therefore, it is a strong candidate to substitute model-based identifiers in applications such as model reference control system or predictive control problem to reduce the required design time.