Belbic Composition

Expert Devices

with Applications

Expert Systems with Applications 32 (2007) 911–918

www.elsevier.com/locate/eswa

Brain emotional learning centered intelligent control applied to neurofuzzy model of micro-heat exchanger

Hossein Rouhani a, *, one particular, Mahdi Jalili b, 2, Babak In. Araabi n,

Wolfgang Eppler c, Querido Lucas b

b

a

Mechanical Executive Department, School of Tehran, Tehran, Iran Control and Intelligent Control Center of Excellence, Electric powered and Computer system Engineering Section, University of Tehran, Tehran, Iran

c

Institute of Data Processing and Electronics, Forschungszentrum Karlsruhe, Germany

Abstract

In this paper, a brilliant controller can be applied to control the mechanics of electrically heated micro-heat exchanger plant. First, the dynamics of the micro-heat exchanger, which provides a non-linear grow, is identified using a neurofuzzy network. To build the neurofuzzy model, a locally linear learning formula, namely, in your area linear mode tree (LoLiMoT) is used. Then, an intelligent control mechanism based on mind emotional learning algorithm is usually applied to the identified version. The brilliant controller is founded on a computational model of limbic system inside the mammalian mind. The brain emotional learning based intelligent control (BELBIC) based on PID control is followed for the micro-heat exchanger plant. The contribution of BELBIC in improving the control system performance is definitely shown by comparison with results obtained from vintage PID controller without BELBIC. The effects demonstrate excellent improvements of control action, without any extensive increase in control effort for PID + BELBIC. Ó 2006 Elsevier Ltd. All rights appropriated.

Keywords: Clever control; Feeling based learning; Neurofuzzy models; Locally linear models; Nonlinear system identification; Heat exchanger

1 . Advantages

Although commercial processes generally contain complicated

nonlinearities, most of the conventional control algorithms

derive from a linearized model of the procedure. Linear

5.

Corresponding creator.

E-mail address: [email protected] ac. ir, hossein. [email protected]fl. ch (H. Rouhani), [email protected] lace. ac. ir (M. Jalili), [email protected] ac. ir (B. N. Araabi), wolfgang. [email protected] fzk. de (W. Eppler), [email protected] ir (C. Lucas).

1

Present address: Laboratory for Computer-Aided Design and Production, Commence of Development and Robotics, Swiss Government Institute of Technology Lausanne (EPFL), CH 1015 Lausanne, Switzerland.

a couple of

Present talk about: Laboratory to get Nonlinear Devices, School of Computer and Communication Sciences, Swiss National Institute of Technology Lausanne (EPFL), CH 1015 Lausanne, Switzerland.

0957-4174/$ - discover front subject Ó 2006 Elsevier Ltd. All legal rights reserved. doi: 10. 1016/j. eswa. 06\. 01. 047

models could be identified within a straightforward method from

method test data; e. g. via stage or impulse response. Yet , if the method is highly non-linear and be subject to large regular disturbances, a non-linear style will be required

to describe the behaviour of the procedure. For such systems

nonlinear identification methods should be accustomed to describe the dynamic tendencies of the system, which can be attained

by means of nerve organs networks. An alternative solution approach should be to

design a nonlinear model consisting of many linear functions. The major outcome function is derived from a combination of thready models. Various training algorithms and buildings are suggested for the mentioned networks such

while locally linear model shrub (LoLiMoT), adaptive network

structured fuzzy inference system (ANFIS), Takagi–Sugeno

(TS) and piecewise linear sites (PLN) (Eppler & Beck,

1999; Jang, 1993; Nelles, 1997; Sugeno & Kang, 1988). In

this work we can make use of LoLiMoT for schooling

912

H. Rouhani et al. as well as Expert Systems with Applications 32 (2007) 911–918

formula of the neurofuzzy network for its rapid

and accurate procedure in control applications.

We uses brain emotional based learning intelligent...

References: Balkenius, C., & Moren, J. (2000). Emotional learning: a computational

model of the amygdale

Brandner, J. T., & Schubert, K. (2005). Fabrication and testing of

microstructure high temperature exchangers pertaining to thermal applications

Eppler, W., & Beck, H. D. (1999). Piecewise linear sites (PLN) intended for

function approximation

Fatourechi, M., Lucas, C., & Khaki Sedigh, A. (2001a). Reducing control

effort by means of mental learning

conference on electric engineering (ICEE2001), May, Tehran, Iran,

pp

Fatourechi, M., Lucas, C., & Khaki Sedigh, A. (2001b). Lowering of

optimum overshoot through emotional learning

Fatourechi, Meters., Lucas, C., & Khaki Sedigh, A. (2003). Psychological learning

like a new device for development of agent based system

Fink, A., Fischer, M., Nelles, O., & Isermann, 3rd there�s r. (2000). Oversight of

nonlinear adaptive controllers based on fluffy models

Fink, A., Topfer, S., & Isermann, 3rd there�s r. (2003). non-linear model-based

control with regional linear neuro-fuzzy models

Hafner, M., Schukler, M., Nelles, O., & Isermann, R. (2001). Fast neural

networks for diesel-powered engine control design

Practice, 8, 1211–1221.

Henning, To., Brandner, L. J., & Schubert, E. (2004). Portrayal of

electrically powered micro-heat exchangers

Inoue, K., Kawabata, K., & Kobayashi, L. (1996). On a decision making

system with feelings

Jang, M. S. 3rd there�s r. (1993). Adaptive-network-based fuzzy inference system.

Lucas, C., Shahmirzadi, D., & Sheikholeslami, D. (2004). Launching

BELBIC: human brain emotional learning based intelligent controller

Moren, J. (2002). Emotion and learning: a computational model of the

amygdala, PhD Thesis, Lund school, Lund, Sweden.

Neese, Ur. (1998). Psychological disorders in evolutionary point of view. British

Diary of Medical Psycology, 71, 397–415.

Nelles, O. (1997). Orthonormal basis functions for nonlinear system

identification with local thready model forest (LoLiMoT)

Nelles, O. (2001). Nonlinear program identification: Via classical

approaches to neural sites and fuzzy models

Savinov, A. A. (1999). An algorithm for induction of options setvalued rules by finding prime disjunctions. In Process of the last

on-line world conference on soft computing in industrial applications

Sugeno, M., & Kang, G. T. (1988). Structure identification of fluffy model.

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