Development of a new type of recurrent co-active neuro-fuzzy system for system identification

Author

Mirea, Letitia

Author_Institution

Dept. of Autom. Control & Appl. Inf., Gh. Asachi Tech. Univ. of Iasi, Iasi, Romania

fYear

2012

fDate

12-14 Oct. 2012

Firstpage

1

Lastpage

6

Abstract

In this paper, the development of a new type of recurrent co-active neuro-fuzzy system is presented, together with its application to system identification. Hybrid learning based on fuzzy clustering algorithm and the steepest-descent method is used to train the proposed neuro-fuzzy system. The experimental case studies refer to the identification of a simulated plant and the evaporator system of the evaporation station from a sugar factory. The identification is performed using the proposed recurrent co-active neuro-fuzzy system. The obtained results demonstrate the efficiency of the approach.

Keywords

evaporation; fuzzy set theory; gradient methods; learning (artificial intelligence); pattern clustering; process control; production engineering computing; recurrent neural nets; sugar industry; evaporation station; evaporator system; fuzzy clustering algorithm; hybrid learning; neurofuzzy system training; process control; recurrent coactive neurofuzzy system; simulated plant identification; steepest descent method; sugar factory; system identification; Biological neural networks; Clustering algorithms; Production facilities; Sugar; System identification; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

System Theory, Control and Computing (ICSTCC), 2012 16th International Conference on

Conference_Location

Sinaia

Print_ISBN

978-1-4673-4534-7

Type

conf

Filename

6379240