Fusion of soft computing and hard computing for large-scale plants: A general model

Applied Soft Computing Journal

Volumn 5, Issue 3 SPEC.ISS., 2005, Pages 265-279

  Kamiya, Akimoto ^a   Ovaska, Seppo J ^b   Roy, Rajkumar ^c   Kobayashi, Shigenobu ^d  

^a KUSHIRO NATIONAL COLLEGE OF TECHNOLOGY   (Japan)

^b AALTO UNIVERSITY   (Finland)

^c CRANFIELD UNIVERSITY   (United Kingdom)

^d TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Control; Forecasting; Hard computing; Hybrid intelligent systems; Industrial process; Large scale systems; Plants; Scheduling; Soft computing

Indexed keywords

COMPUTATIONAL METHODS; FORECASTING; FUZZY CONTROL; HIERARCHICAL SYSTEMS; INDUSTRIAL PLANTS; INTELLIGENT CONTROL; SCHEDULING; SYSTEMS ANALYSIS; TECHNOLOGICAL FORECASTING;

HARD COMPUTING; HYBRID INTELLIGENT SYSTEMS; INDUSTRIAL PROCESSES; SOFT COMPUTING;

LARGE SCALE SYSTEMS;

EID: 13844315872     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2004.08.005     Document Type: Article

References (94)

1. L.A. Zadeh The roles of soft computing and fuzzy logic in the conception, design and deployment of intelligent systems, IIZUKA'96 Proceedings of the Fourth International Conference on Soft Computing 1996 3 4
2. D.B. Fogel An introduction to simulated evolutionary optimization IEEE Trans. Neural Networks 5 1994 3 14
3. Y. Dote, and S.J. Ovaska Industrial applications of soft computing: a review Proceedings of the IEEE 2001 1243 1265
4. H. Takatsu, and T. Itoh Future needs for control theory in industry - report of the control technology survey in Japanese industry IEEE Trans. Control Syst. Technol. 7 1999 298 305
5. S.J. Ovaska, Y. Dote, T. Furuhashi, A. Kamiya, and H.F. VanLandingham Fusion of soft computing and hard computing techniques: a review of applications Proceedings of 1999 IEEE International Conference on Systems, Man, and Cybernetics 1999 I-370 I-375
6. M. Jamshidi Large-Scale Systems: Modeling, Control and Fuzzy Logic 1997 Prentice Hall PTR
7. A.P. Sage Methodology for Large-Scale Systems 1977 McGraw-Hill
8. D.D. Siljak Decentralized Control of Complex Systems 1991 Academic Press
9. D.A. Wismer Optimization Methods for Large-Scale Systems 1971 McGraw-Hill
10. S.J. Ovaska, H.F. VanLandingham, and A. Kamiya Fusion of soft computing and hard computing in industrial applications: an overview IEEE Trans. Syst. Man Cybernet. Part C: Appl. Rev. 32 2002 72 79
11. K.T. Erickson, and J.L. Hedrick Plantwide Process Control 1999 John Wiley & Sons
12. Lee Suk, Lee Sang Ho, Lee Kyung Chang, Lee Man Hyung, and F. Harashima Intelligent performance management of networks for advanced manufacturing systems IEEE Trans. Indus. Electron. 48 2001 731 741
13. B. Fardanesh Future trends in power system control IEEE Comput. Appl. Power 15 2002 24 31
14. Wang Dingwei, K.L. Yung, and W.H. Ip A heuristic genetic algorithm for subcontractor selection in a global manufacturing environment IEEE Trans. Syst. Man Cybernet. Part C: Appl. Rev. 31 2001 189 198
15. B.F. Hobbs, S. Jitprapaikulsarn, and D.J. Maratukulam Analysis of the value for unit commitment of improved load forecasts IEEE Trans. Power Syst. 14 1999 1342 1348
16. A. Pankratz Forecasting with Univariate Box-Jenkins Models: Concepts and Cases 1983 Wiley
17. J.G. De Gooijer, and K. Kumar Some recent developments in non-linear time series modelling, testing, and forecasting Int. J. Forecasting 8 1992 135 156
18. A.D. Papalexopoulos, and T.C. Hesterberg A regression-based approach to short-term system load forecasting IEEE Trans. Power Syst. 5 1990 1535 1547
19. A. Pinkus Approximation theory of the MLP model in neural networks Acta Num. 8 1999 143 196
20. G. Zhang, B.E. Patuwo, and M.Y. Hu Forecasting with artificial neural networks: the state of the art Int. J. Forecast. 14 1998 35 62
21. H. Ying, Y. Ding, S. Li, and S. Shao Comparison of necessary conditions for typical Takagi-Sugeno and Mamdani fuzzy systems as universal approximators IEEE Trans. Syst. Man Cybernet. Part A 29 1999 508 514
22. K. Liu, S. Subbarayan, R.R. Shoults, M.T. Manry, C. Kwan, F.I. Lewis, and J. Naccarino Comparison of very short-term load forecasting techniques IEEE Trans. Power Syst. 11 1996 877 882
23. M.R. Khan, A. Abraham, C. Ondrusek, Short term load forecasting models in Czech Republic using soft computing techniques, Int. J. Knowledge-Based Intell. Eng. Syst., 2002, in press.
24. P.A. Mastorocostas, J.B. Theocharis, and A.G. Bakirtzis Fuzzy modeling for short term load forecasting using the orthogonal least squares method IEEE Trans. Power Syst. 14 1999 29 36
25. H.S. Hippert, C.E. Pedreira, and R.C. Souza Neural networks for short-term load forecasting: a review and evaluation IEEE Trans. Power Syst. 16 2001 41 55
26. A. Khotanzad, R. Afkhami-Rohani, and D. Maratukulam ANNSTLF - artificial neural network short-term load forecaster-generation three IEEE Trans. Power Syst. 13 1998 1413 1422
27. K.S. Swarup, and B. Satish Integrated ANN approach to forecast load IEEE Comput. Appl. Power 15 2002 46 51
28. R.T. Clemen Combining forecasts: a review and annotated bibliography Int. J. Forecast. 5 1989 559 583
29. N. Amjady Short-term hourly load forecasting using time-series modeling with peak load estimation capability IEEE Trans. Power Syst. 16 2001 798 805
30. K.-H. Kim, H.-S. Youn, and Y.-C. Kang Short-term load forecasting for special days in anomalous load conditions using neural networks and fuzzy inference method IEEE Trans. Power Syst. 15 2000 559 565
31. H.W. Lewis III, Intelligent hybrid load forecasting system for an electric power company, in: Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications, SMCia/01, 2001, pp. 23-27.
32. X. Wang, N. Hatziargyriou, and L.H. Tsoukalas A new methodology for nodal load forecasting in deregulated power systems IEEE Power Eng. Rev. 22 2002 48 51
33. H.R. Kassaei, A. Keyhani, T. Woung, and M. Rahman A hybrid fuzzy, neural network bus load modeling and predication IEEE Trans. Power Syst. 14 1999 718 724
34. D. Srinivasan, C.S. Chang, and A.C. Liew Demand forecasting using fuzzy neural computation, with special emphasis on weekend and public holiday forecasting IEEE Trans. Power Syst. 10 1995 1897 1903
35. J.-S.R. Jang ANFIS: adaptive-network-based fuzzy inference system IEEE Trans. Syst. Man Cybernet. 23 1993 665 685
36. N. Kasabov Evolving fuzzy neural networks - algorithms, applications and biological motivation, IIZUKA'98 Proceedings of the Fifth International Conference on Soft Computing 1998 271 274
37. A. Abraham, and B. Nath A neuro-fuzzy approach for modelling electricity demand in victoria Appl. Soft Comput. J. 1 2001 127 138
38. A.G. Bakirtzis, J.B. Theocharis, S.J. Kiartzis, and K.J. Satsios Short term load forecasting using fuzzy neural networks IEEE Trans. Power Syst. 10 1995 1518 1524
39. I. Escoda, A. Ortega, A. Sanz, and A. Herms Demand forecast by neuro-fuzzy techniques Proceedings of The Sixth IEEE International Conference on Fuzzy Systems 1997 1381 1386
40. G.B. Sheble, and G.N. Fahd Unit commitment literature synopsis IEEE Trans. Power Syst. 9 1994 128 135
41. A. Kamiya, M. Kato, K. Shimada, and S. Kobayashi Soft computing-based optimal operation in power energy system S. Ovaska L. Sztandera Soft Computing in Industrial Electronics 2002 Physica-Verlag
42. A. Rudolf, and R. Bayrleithner A genetic algorithm solution to the unit commitment problem of a hydro-thermal power system Proceedings of the 13th Power Systems Computations Conference 1999 745 752
43. C.-P. Cheng, C.-W. Liu, and C.-C. Liu Unit commitment by lagrangian relaxation and genetic algorithms IEEE Trans. Power Syst. 15 2000 707 714
44. S.E. Barndt, and D.W. Carvey Essentials of Operations Management 1982 Prentice-Hall
45. S. Dauzere-Peres, and J.-B. Lasserre An Integrated Approach in Production Planning and Scheduling 1994 Springer-Verlag
46. T. Nishi, T. Inoue, and Y. Hattori Development of a decentralized supply chain optimization system Proceedings of PSE Asia 2000, International Symposium on Design, Operation and Control of Next Generation Chemical Plants 2000 141 151
47. D.H. Wolpert, and W.G. Macready No free lunch theorems for optimization IEEE Trans. Evolut. Comput. 1 1997 67 82
48. T.E. Morton, and D.W. Pentico Heuristic Scheduling Systems: With Applications to Production Systems and Project Management 1993 John Wiley & Sons Inc.
49. S. Kirtpatrick, C.D. Gelatt Jr., and M.P. Vecchi Optimization by simulated annealing Science 220 1983 671 680
50. F. Glover, E. Taillard, and D. De Werra A user's guide to Tabu search Ann. Operat. Res. 41 1993 3 28
51. J.H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan, 1975.
52. B.P.-C. Yen Agent-based distributed planning and scheduling in global manufacturing Proceedings of the Third Annual International Conference on Industrial Engineering Theories, Application and Practice 1998 259.1 259.9
53. A.D. Baker A survey of factory control algorithms which can be implemented in a multi-agent heterarchy: dispatching, scheduling, and pull J. Manufact. Syst. 17 1998 297 320
54. E.A. Kendall, M.T. Malkoun, and C. Jiang A methodology for developing agent based systems for enterprise integration P. Bernus L. Nemes Modelling and Methodologies for Enterprise Integration 1996 Chapman & Hall
55. J. Rachlin, R. Goodwin, S. Murthy, R. Akkiraju, F. Wu, S. Kumaran, and R. Das A-teams: an agent architecture for optimization and decision-support J. Mueller M. Singh A. Rao Lecture Notes in Artificial Intelligence: Intelligent Agents V vol. 1555 1999 Springer-Verlag New York
56. A. Nareyek Constraint-Based Agents: An Architecture for Constraint-Based Modeling and Local-Search-Based Reasoning for Planning and Scheduling in Open and Dynamic Worlds 2001 Springer-Verlag
57. F. Lobo, and D.E. Goldberg Decision making in a hybrid genetic algorithm Proceedings of the 1997 IEEE International Conference on Evolutionary Computation 1997 121 125
58. C.R. Houck, J.A. Joines, M.G. Kay, and J.R. Wilson Empirical investigation of the benefits of partial Lamarckianism Evolut. Comput. 5 1 1997 31 60
59. D. Orvosh, and L. Davis Using a genetic algorithm to optimize problems with feasibility constraints Proceedings of the First IEEE Conference on Evolutionary Computation 1994 548 553
60. B.A. Julstrom Comparing Darwinian, Baldwinian, and Lamarckian search in a genetic algorithm for the 4-cycle problem Proceedings of the Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference 1999 134 138
61. D. Whitley, S. Gordon, and K. Mathias Lamarckian evolution, the baldwin effect and function optimization parallel problem solving from nature PPSN III 1994 6 15
62. M. Schoenauer, and Z. Michalewicz Boundary operators for constrained parameter optimization problems Proceedings of the Seventh International Conference on Genetic Algorithms 1997 322 329
63. A. Kamiya, K. Kawai, I. Ono, and S. Kobayashi Theoretical proof of edge search strategy applied to power plant start-up scheduling IEEE Trans. Syst. Man Cybernet. Part B 32 2002 316 331
64. V. Chankong, Y.Y. Haimes, Optimization-Based Methods for Multiobjective Decision-Making: An Overview, Large Scale Systems, vol. 5, 1983, pp. 1-33.
65. C.A. Coello, and A. Coello Comprehensive survey of evolutionary-based multiobjective optimization techniques Knowledge Informat. Syst. 1 1999 269 308
66. H. Ishibuchi, and T. Murata A multi-objective genetic local search algorithm and its application to flowshop scheduling, systems IEEE Trans. Man Cybernet. Part C: Appl. Rev. 28 1998 392 403
67. D. Srinivasan, and A. Tettamanzi An evolutionary algorithm for evaluation of emission compliance options in view of the clean air act amendments IEEE Trans. Power Syst. 12 1997 336 341
68. H.-J. Zimmermann Fuzzy programming and linear programming with several objective functions Fuzzy Sets Syst. 1 1978 45 55
69. M. Sakawa, and H. Yano An interactive fuzzy satisficing method using augmented minimax problems and its application to environmental systems IEEE Trans. Syst. Man Cybernet. SMC-15 1985 720 729
70. R.E. Bellman, and L.A. Zadeh Decision-making in a fuzzy environment Manage. Sci. 17 1970 141 166
71. F.S. Wang, C.H. Jing, and G.T. Tsao Fuzzy decision making problems of fuel ethanol production using a genetically engineered yeast Indus. Eng. Chem. Res. 37 1998 3434 3443
72. L.F.B. Baptistella, and A. Ollero Fuzzy methodologies for interactive multicriteria optimization IEEE Trans. Syst. Man Cybernet. SMC-10 1980 335 365
73. K. Kawai Knowledge engineering in power-plant control and operation Control Eng. Pract. 4 1996 1199 1208
74. E. van der Wal, Standardization in industrial control programming: what's in it for me? in: Proceedings of The International Conference and Exposition for Advancing Instrumentation, System, and Automation, ISA EXPO/2000, 2000.
75. P.J. Antsaklis, M. Lemmon, and J.A. Stiver Learning to be autonomous intelligent supervisory control M.M.M. Gupta N.K. Sinha Intellectual Control System 1996 IEEE Press 28 62
76. D. Pruessmann, B. Krause, and C. von Altrock Fuzzy logic supervisory control for coal power plant Proceedings of the Sixth IEEE International Conference on Fuzzy Systems 1997 921 925
77. T. Kobayashi, T. Tani, N. Abe, S. Miyamoto, Comparison between human supervisory control and hierarchical control system based on human's knowledge in petroleum plant, in: Proceedings of the 1998 IEEE International Conference on Fuzzy Systems of the IEEE World Congress on Computational Intelligence, vol. 1, 1998, pp. 200-204.
78. A. Ben-Abdennour, and K.Y. Lee An autonomous control system for boiler-turbine units IEEE Trans. Energy Conv. 11 1996 401 406
79. H. Voos Intelligent agents for supervision and control: a perspective Proceedings of the 2000 IEEE International Symposium on Intelligent Control 2000 339 344
80. T. Kohno, J. Suzuki, M. Iwamasa, N. Sueda, and A. Kamiya A plant control expert system to cope with unforeseen situations Trans. Soc. Instrum. Control Eng. 29 1993 833 842 (in Japanese)
81. J. Rasmussen Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models IEEE Trans. Syst. Man Cybernet. SMC-13 1983 257 266
82. B.G. Buchanan, and E.H. Shortliffe Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project 1984 Addison-Wesley
83. M. Ulieru, and R. Isermann Design of a fuzzy-logic based diagnostic model for technical processes Fussy Sets Syst. 58 1993 249 271
84. S. Edwards, A.W. Lees, and M.I. Friswell Fault diagnosis of rotating machinery Shock Vibrat. Digest. 30 1998 4 13
85. P. Antsaklis Defining intelligent control IEEE Control Syst. 4-5 1994 58 66
86. K.J. Aström, T.H. Hägglund, New tuning methods for PID controllers, in: Proceedings of the Third European Control Conference, 1995, pp. 2456-2462.
87. F. Karray, W. Gueaieb, and S. Al-Sharhan The hierarchical expert tuning of pid controllers using tools of soft computing, systems IEEE Trans. Man Cybernet. Part B 32 2002 77 90
88. E.M. Hemerly, and C.L. Nascimento Jr. An NN-based approach for tuning servocontrollers Neural Networks 12 1999 113 118
89. C.-H. Lee, and C.-C. Teng Tuning of PID controller for stable and unstable processes with specifications on gain and phase margins Int. J. Fuzzy Syst. 3 2001 346 355
90. Y.-Y. Tzou, and S.-Y. Lin Fuzzy-tuning current-vector control of a three-phase PWM inverter for high-performance ac drives IEEE Trans. Ind. Electron. 45 1998 782 791
91. C.E. Garcia, D.M. Prett, and M. Morari Model predictive control: theory and practice - a survey Automatica 25 1989 335 348
92. M.A. Hussain Review of the applications of neural networks in chemical process control-simulation and online implementation Artif. Intell. Eng. 13 1999 55 68
93. J. Abonyi, R. Babuska, M. Ayala Botto, F. Szeifert, and L. Nagy Identification and control of nonlinear systems using Fuzzy Hammerstein Models Ind. Eng. Chem. Res. 39 2000 4302 4314
94. Intelligent Manufacturing Systems, International Research and Development Program, http://www.ims.org/, 2001.