Abstract There  are  some  difficulties  encountered  in  the  appl ication  of  fuzzy  Radial  Basis  Function  (RBF)  neural   network.  One of them is how to determine the number of hidde n rule neurons and another difficulty is about interpretability. In order to  overcome these difficulties, we have proposed a fuz zy neural network based on RBF network and takagi-s ugeno fuzzy system.  We  have  used  a  new  structure  of  fuzzy  RBF  neural  ne twork,  which  has  been  proved  that  it  is  better  than  other  structures  in  term  of  interpretability.  Our  model  also  use  a  Riva l  Penalized  Competitive  Learning  (RPCL)  and  a  swarm   based  algorithm  called  Quantum-behaved  Particle  Swarm  Optimization  (QPSO)  to  determine  design  parameters  of  hidden  layer  and  design  parameters  of  output  layer,  respectively.  RPCL  is  t he  best  clustering  algorithm  that  is  introduced  so far.  The  Particle  Swarm  Optimization  (PSO)  is  a  well-known  population-based   swarm  intelligence  algorithm.  The  QPSO  is  also  pro posed  by  combining  the  classical  CPSO  philosophy  and  quantum   mechanics  to  improve  performance  of  PSO.  We  have  c ompared  the  performance  of  the  proposed  method  with  gradient  ba sed  method.  Simulation  results  of  nonlinear  function  approximation  demonstrate the superiority of the proposed method  over gradient based method. 

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[17] Zadeh A., Outline of A New Approach to the Analysis of Complex Systems and Decision Processes, IEEE Transactions on Systems , vol. 3, no. 1, pp. 28#44, 1973. Saeed Farzi received his BS in computer engineering from Razi University, Iran in 2004, and his MS in artificial intelligence from Isfehan University, Iran in 2006. He is a faculty member at the Department of Computer Engineering, Islamic Azad University#beranch of Kermanshah, Iran. His current research interests include artificial intel ligence, neural network, soft computing and grid computing.