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Recurrent neuro-fuzzy networks for nonlinear process modeling.

J Zhang¹, A J Morris

¹Centre for Process Analysis, Chemometrics and Control, Department of Chemical and Process Engineering, University of Newcastle, Newcastle upon Tyne, NE1 7RU, UK.

IEEE Transactions on Neural Networks

|February 7, 2008

Summary

This study introduces a recurrent neuro-fuzzy network for long-term nonlinear process prediction and control. This approach simplifies complex nonlinear systems into manageable fuzzy regions, enabling efficient and accurate predictive modeling and control strategies.

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Area of Science:

Artificial Intelligence
Control Engineering
Process Systems Engineering

Background:

Modeling and controlling nonlinear processes is challenging due to their complex dynamics.
Traditional nonlinear model-based predictive control often involves computationally intensive nonlinear programming.
Existing methods may struggle with long-term predictions in dynamic industrial operations.

Purpose of the Study:

To propose a recurrent neuro-fuzzy network for developing long-term prediction models of nonlinear processes.
To develop a novel nonlinear model-based long-range predictive controller using the proposed network.
To demonstrate an analytical approach for control action calculation, avoiding complex optimization.

Main Methods:

Partitioning process operation into fuzzy operating regions, each modeled by a local linear model.
Utilizing center of gravity defuzzification for interpolating local model outputs into a global model.
Training the recurrent neuro-fuzzy network using process input-output data to minimize long-term prediction errors.

Main Results:

The recurrent neuro-fuzzy network effectively models nonlinear processes by refining membership functions and learning local models.
A novel predictive controller was developed, combining outputs of local linear model-based predictive controllers.
The proposed control strategy allows for analytical calculation of control actions, significantly reducing computation time.

Conclusions:

The recurrent neuro-fuzzy network provides an effective framework for long-term prediction and control of nonlinear systems.
The developed model-based predictive controller offers an efficient alternative to conventional methods for nonlinear processes.
The techniques were successfully validated through application to the modeling and control of a neutralization process.