Toggle Main Menu Toggle Search

Open Access padlockePrints

Recurrent Neuro-Fuzzy Networks for Nonlinear Process Modeling

Lookup NU author(s): Dr Jie ZhangORCiD, Emeritus Professor Julian Morris


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


A type of recurrent neuro-fuzzy network is proposed in this paper to build long-term prediction models for nonlinear processes. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs. This modeling strategy utilizes both process knowledge and process input output data. Process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights. Process input output data are used to train the network. Network weights are such trained so that the long-term prediction errors are minimized. Through training, membership functions of fuzzy operating regions are refined and local models are learn. Based on the recurrent neuro-fuzzy network model, a novel type of nonlinear model-based long range predictive controller can be developed and it consists of several local linear model-based predictive controllers. Local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions. This control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model-based predictive control. The techniques have been successfully applied to the modeling and control of a neutralization process. © 1999 IEEE.

Publication metadata

Author(s): Zhang J; Morris AJ

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Neural Networks

Year: 1999

Volume: 10

Issue: 2

Pages: 313-326

Print publication date: 01/01/1999

ISSN (print): 1045-9227

ISSN (electronic):

Publisher: IEEE


DOI: 10.1109/72.750562


Altmetrics provided by Altmetric