document: Nonlinear modeling and adaptive fuzzy control of MCFC stack To improve availability and performance of fuel cells , the operating temperature of the molten carbonate fuel cells -LRB- MCFC -RRB- stack should be controlled within a specified range . However , most existing models of MCFC are not ready to be applied in synthesis . In the paper , a radial basis function neural networks identification model of a MCFC stack is developed based on the input-output sampled data . An adaptive fuzzy control procedure for the temperature of the MCFC stack is also developed . The parameters of the fuzzy control system are regulated by back-propagation algorithm , and the rule database of the fuzzy system is also adaptively adjusted by the nearest-neighbor-clustering algorithm . Finally using the neural networks model of MCFC stack , the simulation results of the control algorithm are presented . The results show the effectiveness of the proposed modeling and design procedures for the MCFC stack based on neural networks identification and the novel adaptive fuzzy control
doc_bio_tags: B I O B I I O B I O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I I I I O O B I O O O O O O O O O O B I I O O O O O O B I O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I I