Adaptive fuzzy-neuro control with application to a water bath process

Khalid,; Omatu,; Yusof,

doi:10.1109/cca.1994.381231

Cited by 7 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MATHEMATICAL MODELLING OF TEMPERATURE PROCESS method like PID. However, since the beginning of this decade, some unconventional methods namely Neural Network and fuzzy logic approaches have been fast gaining popularity [3][4][5][6]. The salient feature of these latter methods is the dependence on some advanced control algorithms to implement the necessary control using a computer.…”

mentioning

confidence: 99%

Development of multilayer neural network for real time temperature control system

Ghatule

Uplane

2007

2007 International Conference on Intelligent and Advanced Systems

View full text Add to dashboard Cite

mentioning

confidence: 99%

Development of multilayer neural network for real time temperature control system

Ghatule

Uplane

2007

2007 International Conference on Intelligent and Advanced Systems

View full text Add to dashboard Cite

“…Experiments are then conducted on the slip energy recovery process. The performance of the adaptive fuzzy-neural controller is compared to a conventional analog-PID controller and the basic fuzzy controller under identical conditions of varying complexities in the process [2,3].…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy-neuro controller for speed of wound rotor induction motor with slip energy recovery

Tunyasrirut¹,

Ngamwiwit²,

Furuya³

et al.

2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119)

View full text Add to dashboard Cite

In this paper, we proposed an adaptive fuzzyneuro controller to control the speed of wound rotor induction motor with slip energy recovery. An adaptive fuzzy-neuro controller has been designed by integrating two neural network models with a basic fuzzy logic controller. Using the backpropagation algorithm, the first neural network is trained as a plant emulator and the second neural network is used as a compensator for the basic fuzzy controller to improve its performance on-line. The function of the neural network plant emulator is to provide the correct error signal at the output of the neural fuzzy compensator without the need for any mathematical modeling of the plant. The scheme is applied to the control speed of wound rotor induction motor process. The simulation and experimental results show that the adaptive fuzzy-neuro controller gives the controlled system by keeping the speed constant and good transient response without overshoot can be obtained.

show abstract

Combining neural and conventional paradigms for modelling,prediction and control

Agarwal

1997

International Journal of Systems Science

View full text Add to dashboard Cite

Adaptive fuzzy-neuro control with application to a water bath process

Cited by 7 publications

References 20 publications

Development of multilayer neural network for real time temperature control system

Development of multilayer neural network for real time temperature control system

Adaptive fuzzy-neuro controller for speed of wound rotor induction motor with slip energy recovery

Combining neural and conventional paradigms for modelling,prediction and control

Contact Info

Product

Resources

About