Indirect adaptive neuro fuzzy controller for a class of non-linear systems

2011 ◽  
Author(s):  
Amineh Baberi ◽  
Maryam Zekri ◽  
Saeid Hosseiniea
Keyword(s):  
Linear Systems ◽  
Fuzzy Controller ◽  
Neuro Fuzzy ◽  
Non Linear ◽  
Non Linear Systems

scholarly journals A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems

2008 ◽  
Vol 223 (3) ◽  
pp. 309-321 ◽  
Author(s):  
O F Lutfy ◽  
Mohd S B Noor ◽  
M H Marhaban ◽  
K A Abbas
Keyword(s):  
Linear Systems ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Proportional Integral Derivative ◽  
Scaling Factors ◽  
Inference System ◽  
Input And Output ◽  
Neuro Fuzzy ◽  
Non Linear ◽  
Non Linear Systems

This paper presents a genetically trained PID (proportional-integral-derivative)-like ANFIS (adaptive neuro-fuzzy inference system) acting as a feedback controller to control non-linear systems. Three important issues are addressed in this paper, which are, first, the evaluation of the ANFIS as a PID-like controller; second, the utilization of the GA (genetic algorithm) alone to train the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature; and, third, the determination of the input and output scaling factors for this controller by the GA. The GA, with real-coding operators, is used to adjust all of the ANFIS parameters, which include the input and output scaling factors, the centres and widths of the input membership functions (MFs), and the consequent parameters. To show the effectiveness of this controller and its learning method, several non-linear plants, including the CSTR (continuous stirred tank reactor), have been selected to be controlled by this controller through simulation. Moreover, this controller's robustness to output disturbances has also been tested and the results clearly indicated the remarkable performance of this controller and its learning algorithm. In addition, the result of comparing the performance of this controller with a genetically tuned classical PID controller has shown the superiority of the PID-like ANFIS controller.

scholarly journals A Fuzzy MLP Approach for Identification of Nonlinear Systems

2019 ◽  
Vol 65 (1) ◽  
pp. 44-53 ◽  
Author(s):  
A R Marakhimov ◽  
K K Khudaybergenov
Keyword(s):  
Linear Systems ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Performance Comparison ◽  
Neuro Fuzzy ◽  
Non Linear ◽  
Non Linear Systems ◽  
And Performance ◽  
Non Linear System

In case of decision making problems, identification of non-linear systems is an important issue. Identification of non-linear systems using a multilayer perceptron (MLP) trained with back propagation becomes much complex with an increase in number of input data, number of layers, number of nodes, and number of iterations in computation. In this paper, an attempt has been made to use fuzzy MLP and its learning algorithm for identification of non-linear system. The fuzzy MLP and its training algorithm which allows to accelerate a process of training, which exceeds in comparing with classical MLP is proposed. Results show a sharp reduction in search for optimal parameters of a neuro fuzzy model as compared to the classical MLP. A training performance comparison has been carried out between MLP and the proposed fuzzy-MLP model. The time and space complexities of the algorithms have been analyzed. It is observed, that number of epochs has sharply reduced and performance increased compared with classical MLP.

Holomorphically projective mappings between generalized m-parabolic Kähler manifolds

Filomat ◽  
2017 ◽  
Vol 31 (15) ◽  
pp. 4865-4873 ◽  
Author(s):  
Milos Petrovic
Keyword(s):  
Linear Systems ◽  
Kähler Manifolds ◽  
Kahler Manifolds ◽  
Covariant Derivatives ◽  
Linearly Independent ◽  
Non Linear ◽  
Non Linear Systems ◽  
Curvature Tensors ◽  
Derivatives Of

Generalized m-parabolic K?hler manifolds are defined and holomorphically projective mappings between such manifolds have been considered. Two non-linear systems of PDE?s in covariant derivatives of the first and second kind for the existence of such mappings are given. Also, relations between five linearly independent curvature tensors of generalized m-parabolic K?hler manifolds with respect to these mappings are examined.

Sign in / Sign up

Export Citation Format

Share Document