A Novel Fuzzy-Neural-Network Modeling Approach to Crude-Oil Blending

In this brief, we propose a new fuzzy-neural-network (FNN) modeling approach which is applied for the modeling of crude-oil blending. The structure and parameters of FNNs are updated online. The new idea for the structure identification is that the input (precondition) and the output (consequent) spaces partitioning are carried out in the same time index. This idea gives a better explanation for input-output mapping of nonlinear systems. The contributions of the parameters identification are as follows: 1) A time-varying learning rate is applied for the commonly used backpropagation algorithm, and the upper bound of modeling error and stability are proved, and 2) since the data of the precondition and the consequent are in the same temporal interval, we can train each rule by its own group data.