The International Arab Journal of Information Technology (IAJIT)


Fuzzy and Neuro-Fuzzy Modeling of a Fermentation Process

  Neuro-fuzzy  modeling  may  be  qualified  as  a  grey-box   technique,  since  it  combines  the  transparency  of  rule-based  fuzzy systems with the learning capability of neura l networks. The main problem in the identification  of non-linear processes is  the  lack  of  complete  information.  Certain  variables   are,  either  immeasurable  or  difficult  to  measure, the  soft  sensors  are  the  necessary tools to solve the problem. Those latter  can be used via online estimation, and then they wi ll be implemented in fed- batch  fermentation  processes  for  optimal  production   and online  monitoring.  The  process  parameters  are  estimated  through  a  fuzzy logic system.  The fuzzy  models of takagi-suge no type suffer of the problem of poor initialization, which can be solved by  the trial-and error method Trial-and-error method i s used to solve the poor initialization problem of TS models, this deals with  identifying  the  structure  of  the  model,  such  struct ure  consists  on  finding  the  optimum  number  of  rules ,  which  enters  in  the  model  cost  reduction.  The  fuzzy  model  might  not  cap ture  the  process  non-linearity,  especially  if  the  number  of  rules  is  over- optimized.  Bioreactors  exhibit  a  wide  range  of  dyna mic  behaviours  and  offer  many  challenges  to  modelin g,  as  a  result  of  the  presence  of  living  micro-organisms  whose  growth  rat e  is  described  by  complex  equations.  We  will  illustrate  the  fuzzy  and  the  neuro-fuzzy modeling on the identification of such  a system. In order to compare the NF model outputs,  we     use another fuzzy  model  that  does  not  incorporate  the  neural  network  learning  capability,  to  identify  the  parameters  of the  same  process.  Even  though,  the  two  models  were  trained  using  levenberg-marquardt  algorithm,  the  corresponding  simulation results  show  that  a  better  modeling  is  achieved  using  NF  technique,  esp ecially  that  we  did  not  employ  any  involved  optimization  procedure  to  identify the NF structure.    

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[9] Xiong Z., On Line Estimation of Concentration Parameters in Fermentation Processes, Journal of Zhejiang University SCIENCE, vol. 6, no. 3, pp. 530*534, 2005. Chabbi Charef received his BSc in electrical engineering from USTO University, Algeria in 1981, his MSc from Ohio University, USA in 1985, and currently, preparing his PhD in electrical engineering. Currently, he is an assistant professor at BadjiMokhtar University, Algeria since 1988. Mahmoud Taibi received his BSc in electrical engineering from USTO University, Algeria in 1980, and his MSc from Badji*Mokhtar University, Algeria in 1996. Currently, he is an assistant professor at Badji*Mokhtar University, Algeria since 1983. His interests are i n intelligent systems. Nicole Vincent is full professor since 1996. She presently heads the Center of Research in Computer Science (CRIP5) and the team Syst mes Intelligents de Perception (SIP) in the University Paris Descartes, France. After studying in Ecole Normale Sup rieure and graduation in mathematics, and graduation in mathematics, Nicole Vincent received a PhD in computer science in 1988 from Lyon Insa. 385 The International Arab Journal of Information Technology, Vol. 6, No. 4, October 2009 386 The International Arab Journal of Information Technology, Vol. 6, No. 4, October 2009 Fuzzy and neuro-fuzzy modeling of a fermentation Process