Abstract
In this paper, a neuro-fuzzy system based on improved CART algorithm (ICART) is presented, in which the ICART algorithm is used to design neuro-fuzzy system. It is worth noting that ICART algorithm partitions the input space into tree structure adaptively, which avoids the curse of dimensionality (number of rules goes up exponentially with number of input variables). Moreover, it adopts density function to construct the local model for every node in order to overcome the discontinuous boundaries existed in CART algorithm. In addition, a supervised scheme is used to adjust parameters to minimize the network output error and construct more accurate fuzzy model on the basis of the ICART algorithm. Finally, to illustrate the validity of the proposed method, a simulation research and a practical application are done. The results show that the proposed method can provide optimal model structure and parameters for fuzzy modeling, possesses high learning efficiency, and is smoother than CART algorithm. It can be successfully applied to modeling jet fuel endpoint of hydrocracking processing.
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