Real-coded chaotic quantum-inspired genetic algorithm for training of fuzzy neural networks

  • Authors:

  • Shuanfeng Zhao;Guanghua Xu;Tangfei Tao;Lin Liang

  • Affiliations:

  • School of Mechanical Engineering, Xi'an Jiaotong University, 710049, Xi'an, China and School of Mechanical Engineering, Xi'an University of Science and Technology, 710054, Xi'an, China;School of Mechanical Engineering, Xi'an Jiaotong University, 710049, Xi'an, China;School of Mechanical Engineering, Xi'an Jiaotong University, 710049, Xi'an, China and The State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, 710049, Xi'an, Chin ...;School of Mechanical Engineering, Xi'an Jiaotong University, 710049, Xi'an, China

  • Venue:
  • Computers & Mathematics with Applications
  • Year:
  • 2009

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Abstract

In this paper, a novel approach to adjusting the weightings of fuzzy neural networks using a Real-coded Chaotic Quantum-inspired genetic Algorithm (RCQGA) is proposed. Fuzzy neural networks are traditionally trained by using gradient-based methods, which may fall into local minimum during the learning process. To overcome the problems encountered by the conventional learning methods, RCQGA algorithms are adopted because of their capabilities of directed random search for global optimization. It is well known, however, that the searching speed of the conventional quantum genetic algorithms (QGA) is not satisfactory. In this paper, a real-coded chaotic quantum-inspired genetic algorithm (RCQGA) is proposed based on the chaotic and coherent characters of Q-bits. In this algorithm, real chromosomes are inversely mapped to Q-bits in the solution space. Q-bits probability-guided real cross and chaos mutation are applied to the evolution and searching of real chromosomes. Chromosomes consisting of the weightings of the fuzzy neural network are coded as an adjustable vector with real number components that are searched by the RCQGA. Simulation results have shown that faster convergence of the evolution process in searching for an optimal fuzzy neural network can be achieved. Examples of nonlinear functions approximated by using the fuzzy neural network via the RCQGA are demonstrated to illustrate the effectiveness of the proposed method.