RAOGA-based fuzzy neural network model of design evaluation

  • Authors:
  • Li-Hua Xue;Hong-Zhong Huang;Jun Hu;Qiang Miao;Dan Ling

  • Affiliations:
  • School of Mechatronics Eng., University of Electronic Science and Technology of China, Chengdu, Sichuan, China and School of Mechanical Eng., Dalian University of Technology, Dalian, Liaoning, Chi ...;School of Mechatronics Eng., University of Electronic Science and Technology of China, Chengdu, Sichuan, China;School of Mechatronics Eng., University of Electronic Science and Technology of China, Chengdu, Sichuan, China;School of Mechatronics Eng., University of Electronic Science and Technology of China, Chengdu, Sichuan, China;School of Mechatronics Eng., University of Electronic Science and Technology of China, Chengdu, Sichuan, China

  • Venue:
  • ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
  • Year:
  • 2006

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Abstract

This paper presents a new Fuzzy Neural Network (FNN) model to evaluate design alternatives in conceptual design. In the proposed method, a fuzzy reasoning based on feedforward neural network is used to evaluate concepts, and a learning algorithm based on ranking-based adaptive evolutionary operator genetic algorithm (RAOGA) is utilized to adjust fuzzy weights and thresholds with fuzzy inputs and outputs in FNN.