Combined fuzzy logic and genetic algorithm techniques-application to an electromagnetic field problem

The influence of a faulted electrical power transmission line on a buried pipeline is investigated. The induced electromagnetic field depends on several parameters, such as the position of the phase conductors, the currents flowing through conducting materials, and the earth resistivity. A fuzzy logic system was used to simulate the problem. It was trained using data derived from finite element method calculations for different configuration cases (training set) of the above electromagnetic field problem. After the training, the system was tested for several configuration cases, differing significantly from the training cases, with satisfactory results. It is shown that the proposed method is very time efficient and accurate in calculating electromagnetic fields compared to the time straining finite element method. In order to create the rule base for the fuzzy logic system a special incremental learning scheme is used during the training. The system is trained using genetic algorithms. Binary and real genetic encoding were implemented and compared.

[1]  Francisco Herrera,et al.  Tuning fuzzy logic controllers by genetic algorithms , 1995, Int. J. Approx. Reason..

[2]  Francisco Herrera,et al.  Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems , 2001, Fuzzy Sets Syst..

[3]  K. J. Satsios,et al.  Finite element computation of field and eddy currents of a system consisting of a power transmission line above conductors buried in nonhomogeneous earth , 1998 .

[4]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[5]  Hisao Ishibuchi,et al.  Genetic-algorithm-based approaches to the design of fuzzy systems for multi-dimensional pattern classification problems , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[6]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[7]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[8]  Francisco Herrera,et al.  A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples , 1997, Int. J. Approx. Reason..

[9]  T. Fukuda,et al.  Self-tuning fuzzy modeling with adaptive membership function, rules, and hierarchical structure based on genetic algorithm , 1995 .

[10]  John H. Holland,et al.  Outline for a Logical Theory of Adaptive Systems , 1962, JACM.

[11]  Chuen-Chien Lee.  FUZZY LOGIC CONTROL SYSTEMS: FUZZY LOGIC CONTROLLER - PART I , 1990 .

[12]  Fuzzy Logic in Control Systems : Fuzzy Logic , 2022 .

[13]  K. J. Satsios,et al.  Inductive interference caused to telecommunication cables by nearby AC electric traction lines. Measurements and FEM calculations , 1999 .

[14]  Charles L. Karr,et al.  Design of a cart-pole balancing fuzzy logic controller using a genetic algorithm , 1991, Defense, Security, and Sensing.

[15]  Peter P. Silvester,et al.  Finite elements for electrical engineers: Finite Elements for Electrical Engineers , 1996 .

[16]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[17]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[18]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[19]  Francisco Herrera,et al.  Genetic Algorithms and Soft Computing , 1996 .

[20]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[21]  H. Zimmermann,et al.  Fuzzy Set Theory and Its Applications , 1993 .

[22]  Hisao Ishibuchi,et al.  Selecting fuzzy if-then rules for classification problems using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[23]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[24]  Lotfi A. Zadeh,et al.  Please Scroll down for Article International Journal of General Systems Fuzzy Sets and Systems* Fuzzy Sets and Systems* , 2022 .

[25]  Frank Hoffmann.  Incremental Tuning of Fuzzy Controllers by Means of an Evolution Strategy , 1998 .

[26]  James C. Bezdek,et al.  Optimization of fuzzy clustering criteria using genetic algorithms , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.