Most existing algorithms for identifying multi-model system are based on minimizing the square of bias between global outputs of the actual system and the identified model, but the resultant model lacks of robustness....Most existing algorithms for identifying multi-model system are based on minimizing the square of bias between global outputs of the actual system and the identified model, but the resultant model lacks of robustness. In order to solve this problem, this paper considers some other algorithms in which local models are identified independently and presents a multi-model identification algorithm based on weighted cost function, which uses the idea of local weighted regression and local approximation while keeps the model structure of global identification algorithm. The result of application to a 300MW unit boiler superheater illustrates that the multi-model generated by the proposed algorithm has better trade-off between global fitting and local interpretation.展开更多
文摘Most existing algorithms for identifying multi-model system are based on minimizing the square of bias between global outputs of the actual system and the identified model, but the resultant model lacks of robustness. In order to solve this problem, this paper considers some other algorithms in which local models are identified independently and presents a multi-model identification algorithm based on weighted cost function, which uses the idea of local weighted regression and local approximation while keeps the model structure of global identification algorithm. The result of application to a 300MW unit boiler superheater illustrates that the multi-model generated by the proposed algorithm has better trade-off between global fitting and local interpretation.
