An adaptive RBF neural network–based multi-objective optimization method for lightweight and crashworthiness design of cab floor rails using fuzzy subtractive clustering algorithm

To improve the computational cost and accuracy of approximate-based multi-objective optimization problems in engineering, an adaptive RBF neural network (ARBFNN) method integrating RBF neural network model, fuzzy subtractive clustering (FSC) sequence sampling method and non-dominated sorting genetic...

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Veröffentlicht in:	Structural and multidisciplinary optimization Jg. 63; H. 2; S. 915 - 928
Hauptverfasser:	Wang, Dengfeng, Xie, Chong, Wang, Shuang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2021 Springer Nature B.V
Schlagworte:	Accuracy Approximation Artificial neural networks Clustering Computational Mathematics and Numerical Analysis Crashworthiness Design optimization Efficiency Engineering Engineering Design Finite element method Floors Fuzzy logic Genetic algorithms Impact strength Industrial Application Paper Lightweight Multiple objective analysis Neural networks Nonlinear analysis Optimization Rails Sampling methods Sorting algorithms Theoretical and Applied Mechanics Coupled cab Multi-objective optimization Fuzzy subtraction clustering sequence sampling Lightweight and crashworthiness design Adaptive RBF neural network
ISSN:	1615-147X, 1615-1488
Online-Zugang:	Volltext
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