Fusion of soft computing and hard computing for large-scale plants: an overview

The design of control systems for large-scale and complex industrial plants involves numerous trade-off problems, such as costs, quality, environmental impact, safety, reliability, accuracy, and robustness. Some of these parameters are even conflicting. Thus, the use of a multidiscipline approach is suggested to satisfy these requirements in an acceptable and well-balanced manner, and a fusion of soft computing and hard computing appears to be a natural and practical choice. Although the state-of-the-art soft computing technology has distinguished features, the use of soft computing technology would be ineffective, if it is improperly fused with conventional hard computing technology and control processes. Proper fusion is key to success, and a general model of fusion is worth examining. In this paper, through a survey of published literature, a general fusion model and fusion topologies are shown at the system level as well as at the algorithm level.

[1]  R. Clemen.  Combining forecasts: A review and annotated bibliography , 1989 .

[2]  Seppo J. Ovaska,et al.  Classification of fusion topologies in hybrid soft computing and hard computing systems , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[3]  H. W. Lewis.  Intelligent hybrid load forecasting system for an electric power company , 2001, SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504).

[4]  N. Abe,et al.  Comparison between human supervisory control and hierarchical control system based on human's knowledge in petroleum plant , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[5]  Tore Hägglund,et al.  New Tuning Methods for PID Controllers , 1995 .

[6]  Allan Pinkus,et al.  Approximation theory of the MLP model in neural networks , 1999, Acta Numerica.

[7]  L. Darrell Whitley,et al.  Lamarckian Evolution, The Baldwin Effect and Function Optimization , 1994, PPSN.

[8]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[9]  G. T. Tsao,et al.  Fuzzy-Decision-Making Problems of Fuel Ethanol Production Using a Genetically Engineered Yeast , 1998 .

[10]  H. Zimmermann.  Fuzzy programming and linear programming with several objective functions , 1978 .

[11]  David E. Goldberg,et al.  Decision making in a hybrid genetic algorithm , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[12]  C. von Altrock,et al.  Fuzzy logic supervisory control for coal power plant , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[13]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[14]  Alan Pankratz,et al.  Forecasting with univariate Box-Jenkins models : concepts and cases , 1983 .

[15]  Y. Dote,et al.  Fusion of soft computing and hard computing techniques: a review of applications , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[16]  Jens Rasmussen,et al.  Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  Anastasios G. Bakirtzis,et al.  A genetic algorithm solution to the unit commitment problem , 1996 .

[18]  Richard Bellman,et al.  Decision-making in fuzzy environment , 2012 .

[19]  János Abonyi,et al.  Identification and Control of Nonlinear Systems Using Fuzzy Hammerstein Models , 2000 .

[20]  Isao Ono,et al.  Theoretical proof of edge search strategy applied to power plant start-up scheduling , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[21]  Alireza Khotanzad,et al.  ANNSTLF-Artificial Neural Network Short-Term Load Forecaster- generation three , 1998 .

[22]  B. Satish,et al.  Integrated ANN approach to forecast load , 2002, IEEE Computer Applications in Power.

[23]  James R. Wilson,et al.  Empirical Investigation of the Benefits of Partial Lamarckianism , 1997, Evolutionary Computation.

[24]  MASATOSHI SAKAWA,et al.  An interactive fuzzy satisficing method using augmented minimax problems and its application to environmental systems , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[25]  B. Hobbs,et al.  Analysis of the value for unit commitment of improved load forecasts , 1999 .

[26]  Dingwei Wang,et al.  A heuristic genetic algorithm for subcontractor selection in a global manufacturing environment , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[27]  Shigenobu Kobayashi,et al.  Soft Computing-Based Optimal Operation in Power Energy System , 2002 .

[28]  Junzo Suzuki,et al.  A Plant Control Expert System to Cope with Unforeseen Situations , 1993 .

[29]  Yong Cheol Kang,et al.  Short-term load forecasting for special days in anomalous load conditions using neural networks and fuzzy inference method , 2000 .

[30]  Bruce Fardanesh.  Future trends in power system control , 2002 .

[31]  Suk Lee,et al.  Intelligent performance management of networks for advanced manufacturing systems , 2001, IEEE Trans. Ind. Electron..

[32]  Cairo L. Nascimento,et al.  An NN-based approach for tuning servocontrollers , 1999, Neural Networks.

[33]  Wail Gueaieb,et al.  The hierarchical expert tuning of PID controllers using tools of soft computing , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[34]  Stéphane Dauzère-Pérès,et al.  An Integrated Approach in Production Planning and Scheduling , 1994 .

[35]  Y.Y. Haimes,et al.  Methodology for large-scale systems , 1978, Proceedings of the IEEE.

[36]  Nima Amjady,et al.  Short-term hourly load forecasting using time-series modeling with peak load estimation capability , 2001 .

[37]  Gerald B. Sheblé,et al.  Unit commitment literature synopsis , 1994 .

[38]  Yongsheng Ding,et al.  Comparison of necessary conditions for typical Takagi-Sugeno and Mamdani fuzzy systems as universal approximators , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[39]  Ying-Yu Tzou,et al.  Fuzzy-tuning current-vector control of a three-phase PWM inverter for high-performance AC drives , 1998, IEEE Trans. Ind. Electron..

[40]  J. Gooijer,et al.  Some recent developments in non-linear time series modelling, testing, and forecasting☆ , 1992 .

[41]  Kelvin T. Erickson,et al.  Plantwide process control , 1999 .

[42]  Constantino Tsallis,et al.  Optimization by Simulated Annealing: Recent Progress , 1995 .

[43]  M. Jamshidi.  Large-Scale Systems: Modeling, Control and Fuzzy Logic , 1996 .

[44]  Stephen E. Barndt,et al.  Essentials of operations management , 1982 .

[45]  Bryant A. Julstrom.  Comparing Darwinian, Baldwinian, and Lamarckian Search in a Genetic Algorithm for the 4-Cycle Proble , 1999 .

[46]  Thomas E. Morton,et al.  Heuristic scheduling systems : with applications to production systems and project management , 1993 .

[47]  Lawrence Davis,et al.  Using a genetic algorithm to optimize problems with feasibility constraints , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[48]  Zbigniew Michalewicz,et al.  Boundary Operators for Constrained Parameter Optimization Problems , 1997, ICGA.

[49]  Michael T. Manry,et al.  Comparison of very short-term load forecasting techniques , 1996 .

[50]  Chuan-Ping Cheng,et al.  Unit commitment by Lagrangian relaxation and genetic algorithms , 2000 .

[51]  Hisao Ishibuchi,et al.  A multi-objective genetic local search algorithm and its application to flowshop scheduling , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[52]  Toshiaki Itoh,et al.  Future needs for control theory in industry-report of the control technology survey in Japanese industry , 1999, IEEE Trans. Control. Syst. Technol..

[53]  P. Mastorocostas,et al.  Fuzzy modeling for short term load forecasting using the orthogonal least squares method , 1999 .

[54]  Kensuke Kawai.  Knowledge engineering in power-plant control and operation , 1995 .

[55]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[56]  Fred W. Glover,et al.  A user's guide to tabu search , 1993, Ann. Oper. Res..

[57]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.

[58]  Mohamed Azlan Hussain,et al.  Review of the applications of neural networks in chemical process control - simulation and online implementation , 1999, Artif. Intell. Eng..