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Unlike the other fuzzy neural networks, in this paper the fuzzy system and the neural network are separated, and they are corresponding to structure identification and parameter identification. The fuzzy model is generated automatically by on-line clustering method and fuzzy support vector machines. This fuzzy model is not updated, but its modeling error is compensated by a neural network. The neural network acts as the parameter identification model. The benefits of this new method are the structure is simple, and the physical meanings of each part of the model are clear. For the neural compensator, a new stable and fast training algorithm is proposed. Finally, this modeling method is successfully applied to model a magnetic tube recovery ratio in a metal company in China. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a two-dimensional numerical calculation algorithm for the water quality modeling in the Hengyang City section of the Xiangjiang River is researched considering the effect of the Dayuandu navigational key project. The research river is winding and has two branches resulted from an isle. The numerical calculation algorithm for the water quality modeling is set up on the basis of applying topographic map of the river course and the finite element method. The calculation result for the water quality modeling includes the concentration fields for various pollutants. The numerical calculation algorithm for the water quality modeling set up in this paper can he applied to shallow river with similar topographically complicated river course. Key words: two-dimensional river; water quality; numerical calculation algorithm. [ABSTRACT FROM AUTHOR]