A hopfield neural network approach to the dual response problem

Quality and Reliability Engineering International

Volumn 21, Issue 6, 2005, Pages 595-603

  Köksoy, Onur ^a,b   Yalcinoz, Tankut ^a,b,c  

^a NIGDE UNIVERSITY   (Turkey)

^b ASA ^*

^c IEEE

Author keywords

Dual response optimization; Hopfield neural networks; Quality improvement; Response surface methodology

Indexed keywords

DUAL RESPONSE OPTIMIZATION; HOPFIELD NEURAL NETWORKS; QUALITY IMPROVEMENT; RESPONSE SURFACE METHODOLOGY;

ALGORITHMS; COMBINATORIAL MATHEMATICS; CONSTRAINT THEORY; DECISION MAKING; OPTIMIZATION; PROBLEM SOLVING;

NEURAL NETWORKS;

EID: 26644439560     PISSN: 07488017     EISSN: None     Source Type: Journal    
DOI: 10.1002/qre.675     Document Type: Article

References (28)

1. Vining GG, Myers RH. Combining Taguchi and response surface philosophies: A dual response approach. Journal of Quality Technology 1990; 22:38-45.
2. Myers RH, Carter WH. Response surface techniques for dual response systems. Technometrics 1973; 15:301-317.
3. Köksoy O, Doganaksoy N. Joint optimization of mean and standard deviation in response surface experimentation. Journal of Quality Technology 2003; 35(3):239-252.
4. Tang LC, Xu K. Unified approach for dual response surface optimization. Journal of Quality Technology 2002; 34:437-447.
5. Carlyle WM, Montgomery DC, Runger GC. Optimization problems and methods in quality control and improvement. Journal of Quality Technology 2000; 32:1-31.
6. Del Castillo E, Montgomery DC. A nonlinear programming solution to the dual response problem. Journal of Quality Technology 1993; 25:199-204.
7. Lin DKJ, Tu W. Dual response surface optimization. Journal of Quality Technology 1995; 27:34-39.
8. Copeland KA, Nelson PR. Dual response optimization via direct function minimization. Journal of Quality Technology 1996; 28:331-336.
9. Del Castillo E. Multiresponse process optimization via constrained confidence regions. Journal of Quality Technology 1996; 28:61-70.
10. Del Castillo E, Fan SK, Semple J. Computation of global optima in dual response systems. Journal of Quality Technology 1997; 29:347-353.
11. Fan SKS. A generalized global optimization algorithm for dual response systems. Journal of Quality Technology 2000; 32:444-456.
12. Pignatielio JJ. Strategies for robust multiresponse quality engineering. IIE Transactions 1993; 25:5-15.
13. Ames AE, Mattucci N, Macdonald S, Szonyi G, Hawkins DM. Quality loss functions for optimization across multiple response surfaces. Journal of Quality Technology 1997; 29:339-346.
14. Vining GG. A compromise approach to multiresponse optimization. Journal of Quality Technology 1998; 30:309-313.
15. Antony J. Multi-response optimization in industrial experiments using Taguchi's quality loss function and principal component analysis. Quality and Reliability Engineering International 2000; 16(1):3-8.
16. Kim K, Lin DKJ. Dual response surface optimization: A fuzzy modeling approach. Journal of Quality Technology 1998; 30:1-10.
17. Tong L, Su C. Optimizing multi-response problems in the Taguchi method by fuzzy multiple attribute decision making. Quality and Reliability Engineering International 1997; 13(1):25-34.
18. Kumar P, Barua PB, Gaindhar JL. Quality optimization (multi- characteristics) through Taguchi's technique and utility concept. Quality and Reliability Engineering International 2000; 16(6):475-485.
19. Kim YJ, Cho BR. Development of priority-based robust design. Quality Engineering 2002; 14:355-363.
20. Tagliarini GA, Christ JF, Page EW. Optimization using neural networks. IEEE Transactions on Computers 1991; 40(12): 1347-1358.
21. Cichocki A, Unbehauen R. Neural Networks for Optimization and Signal Processing. Wiley: New York, 1993.
22. Gee AH, Prager RW. Polyhedral combinatorics and neural networks. Neural Computation 1994; 6:161-180.
23. Yalcinoz T, Short MJ, Cory BJ. Hopfield neural network approaches to economic dispatch problems. International Journal of Electrical Power and Energy Systems 2001; 23(6):435-442.
24. Hopfield JJ, Tank DW. Neural computations of decisions in optimization problems. Biological Cybernetics 1985; 52(3):141-152.
25. Abe S. Global convergence and suppression of spurious states of the Hopfield neural networks. IEEE Transactions on Circuit and Systems - 1: Fundamental Theory and Applications 1993; 40(4):246-257.
26. Gee AH, Aiyer SVB, Prager RW. An analytical framework for optimizing neural networks. Neural Networks 1993; 6(1):79-97.
27. Yalcinoz T, Short MJ. Large-scale economic dispatch using an improved Hopfield neural network. IEE Proceedings on Generation, Transmission, and Distribution 1997; 144(2): 181-185.
28. Myers RH, Kim Y, Griffiths KL. Response surface methods and the use of noise variables. Journal of Quality Technology 1997; 29:429-440.