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The maintenance costs estimation of electrical lines with the use of interpretability-oriented genetic fuzzy rule-based systems

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Warianty tytułu
PL
Szacowanie kosztów utrzymania linii energetycznych z wykorzystaniem systemów regułowo-rozmytych zorientowanych na przejrzystość
Języki publikacji
EN
Abstrakty
EN
The paper demonstrates an evolutionary technique to design an interpretability-oriented fuzzy rule-based system and its application to estimate the maintenance costs of medium voltage electrical lines. The main goal of the proposed technique is to design the system with not only a relatively high accuracy for estimating the costs, but also with a clear and transparent structure which is easy to interpret by humans. The structure includes easily readable and understandable fuzzy logic rules that represent the knowledge about the considered problem.
PL
Praca demonstruje ewolucyjną technikę projektowania przejrzystych systemów regułowo-rozmytych i jej zastosowanie do szacowania kosztów utrzymania linii energetycznej średniego napięcia. Zaprojektowany system posiada względnie wysoką dokładność i przejrzystą strukturę w formie czytelnych, zrozumiałych i łatwych do interpretacji przez człowieka reguł logicznych, będących kwintesencją wiedzy o rozważanym problemie.
Rocznik
Strony
43--47
Opis fizyczny
Bibliogr. 27 poz., tab., wykr.
Twórcy
  • Kielce University of Technology, Department of Electrical and Computer Engineering, Al. 1000-lecia P.P. 7, 25-314 Kielce, Poland
  • Kielce University of Technology, Department of Electrical and Computer Engineering, Al. 1000-lecia P.P. 7, 25-314 Kielce, Poland
Bibliografia
  • [1] Power D. J. , Decision Support Systems: Frequently Asked Questions, iUniverse (2004)
  • [2] Cordón O., Herrera F., Sánchez L., Computing the Spanish Medium Electrical Line Maintenance Costs by means of Evolution-Based Learning Processes, IEA/AIE '98 Proc. of the 11th Int. Conf. on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (1998), 478-486
  • [3] Cordón O., Herrera F., Sánchez L., Solving Electrical Distribution Problems Using Hybrid Evolutionary Data Analysis Techniques, Applied Intelligence, 10 (1999), no. 1, 5-24
  • [4] Cordón O., Herrera F., Zwir I., A hierarchical knowledge-based environment for linguistic modeling: models and iterative methodology, Fuzzy Sets and Systems, 138 (2003), no. 2, 307-341
  • [5] Acosta J., Nebot A., Villar P., Fuertes J. M., Optimization of fuzzy partitions for inductive reasoning using genetic algorithms, Int. J. Systems Science, 38 (2007), no. 12, 991-1011
  • [6] Alcalá R., Alcalá-Fdez J., Herrera F., Otero J., Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuples linguistic representation, Int. J. Approximate Reasoning, 44 (2007), no.1, 45-65
  • [7] Pulkkinen P., Koivisto H., Identification of interpretable and accurate fuzzy classifiers and function estimators with hybrid methods, Applied Soft Computing, 7 (2007), no. 2, 520-533
  • [8] Luis delaOssa., Gámez J. A., Puerta J. M., Learning weighted linguistic fuzzy rules by using specifically-tailored hybrid estimation of distribution algorithms, Int. J. Approximate Reasoning, 50 (2009), no. 3, 541-560
  • [9] Antonelli M., Ducange P., Lazzerini B., Marcelloni F., Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework, Int. J. Approximate Reasoning, 50 (2009), no. 7, 1066-1080
  • [10] Alcalá R., Ducange P., Herrera F., Lazzerini B., Marcelloni F., A multiobjective evolutionary approach to concurrently learn rule and data bases of linguistic fuzzy-rule-based systems, IEEE Trans. Fuzzy Systems, 17 (2009), no. 5, 1106-1122
  • [11] Fazel Zarandi M.H., Rezaee B., Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system, Information Sciences, 180 (2010), no. 2, 241-255
  • [12] Tewari A., Macdonald M., Knowledge-based parameter identification of TSK fuzzy models, Applied Soft Computing, 10 (2010), no. 2, 481-489
  • [13] Nauck D., Klawonn F. and Kruse R, Foundations of Neuro-Fuzzy Systems, J. Wiley & Sons, Chichester, UK (1997)
  • [14] Rutkowski L., Flexible Neuro-Fuzzy Systems: Structures, Learning and Performance Evaluation, Kluwer Academic Publishers, Boston, Dordrecht (2004)
  • [15] Takagi T., Sugeno M., Fuzzy identification of systems and its application to modeling and control, IEEE Trans. Systems, Man and Cybernetics, 15 (1985), no. 1, 116-132
  • [16] Gorzałczany M.B., Computational Intelligence Systems and Applications, Neuro-Fuzzy and Fuzzy Neural Synergisms, Physica-Verlag, Springer-Verlag Co., Heidelberg, New York (2002)
  • [17] Gorzałczany M.B., A Computational-Intelligence-based approach to decision support. In: H. Bunke, A. Kandel (Eds.), Neuro-Fuzzy Pattern Recognition, World Scientific Publishing Co., Singapore, London (2000), 51-73
  • [18] Gorzałczany M.B., Głuszek A., Neuro-fuzzy systems for rulebased modelling of dynamic processes. In: H.-J. Zimmermann, G. Tselentis, M. van Someren, G. Dounias (Eds.), Advances in Computational Intelligence and Learning, Methods and Applications, Kluwer Academic Publishers (2001), 135-146
  • [19] Gorzałczany M.B., On some idea of a neuro-fuzzy controller, Information Sciences, 120 (1999), 69-87
  • [20] Gupta M.M., Gorzałczany M. B., Fuzzy neuro-computational technique and its application to modeling and control, Proc. of IEEE Int. Conf. Fuzzy Systems, San Diego (1992), 1271-1274
  • [21] Smith S. F., A learning system based on genetic adaptive algorithms, Doctoral dissertation, University of Pittsburgh, 1980
  • [22] Gorzałczany M.B., Rudziński F., A modified Pittsburg approach to design a genetic fuzzy-rule based classifier from data, Lecture Notes in Artificial Intelligence, 6113 (2010), Springer-Verlag, Berlin, Heidelberg, 88-96
  • [23] Gorzałczany M.B., Rudziński F., Accuracy vs. Interpretability of Fuzzy Rule-Based Classifiers - an Evolutionary Approach, Lecture Notes in Computer Science, 7269 (2012), Springer-Verlag, Berlin, Heidelberg, 222-230
  • [24] Gacto M. J., Alcalá R., Herrera F., Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures, Information Sciences, 181 (2011), 4340-4360
  • [25] Baczyński M., Jayaram B., Fuzzy Implications, Springer-Verlag, Berlin, Heidelberg (2008)
  • [26] Gorzałczany M.B., Rudziński F., Genetyczno-rozmyte podejście do modelowania systemów dynamicznych na podstawie danych, In: Postępy automatyki i robotyki, Malinowski K., Dindorf R. (Eds.), The Committee on Automatic Control and Robotics of the Polish Academy of Sciences, Publishing house of the Kielce University of Technology, Part 1 (2011), 270-284 (in Polish)
  • [27] Gorzałczany M.B., Rudziński F, Measurement data in genetic fuzzy modelling of dynamic systems, Measurement Automation and Monitoring, 56 (2010), no. 12, 1420-1423
Typ dokumentu
Bibliografia
Identyfikator YADDA
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