A Hopfield neural network approach to the dual response problem
Küçük Resim Yok
Tarih
2005
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
JOHN WILEY & SONS LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The application of neural networks to optimization problems has been an active research area since the early 1980s. Unconstrained optimization, constrained optimization and combinatorial optimization problems have been solved using neural networks. This study presents a new approach using Hopfield neural networks (HNNs) for solving the dual response system (DRS) problems. The major aim of the proposed method is to produce a string of solutions, rather than a 'one-shot' optimum solution, to make the trade-offs available between the mean and standard deviation responses. This gives more flexibility to the decision-maker in exploring alternative solutions. The proposed method has been tested on two examples. The HNN results are very close to those obtained by using the NIMBUS (Nondifferentiable Interactive Multiobjective Bundle-based Optimization System) algorithm. Choosing an appropriate solution method for a certain multi-objective optimization problem is not easy, as has been made abundantly clear. Unlike the NIMBUS method, the HNN approach does not set any specific assumptions on the behaviour or the preference structure of the decision maker. As a result, the proposed method will still work and generate alternative solutions whether or not the decision maker has enough time and capabilities for co-operation. Copyright (c) 2005 John Wiley & Sons, Ltd.
Açıklama
Anahtar Kelimeler
Hopfield neural networks, dual response optimization, quality improvement, response surface methodology
Kaynak
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
21
Sayı
6
