RAOGA-based fuzzy neural network model of design evaluation
LH Xue, HZ Huang, J Hu, Q Miao, D Ling - … 16-19, 2006 Proceedings, Part II …, 2006 - Springer
LH Xue, HZ Huang, J Hu, Q Miao, D Ling
Computational Intelligence: International Conference on Intelligent Computing …, 2006•SpringerThis 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.
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.
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.
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