By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Wen Yu ; Dept. de Control Automatico, Inst. Politec. Nac. (Cinvestav-IPN), Mexico City, Mexico

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.

Published in:

Control Systems Technology, IEEE Transactions on  (Volume:17 ,  Issue: 6 )