Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Soft Computing
Genetic Algorithms and Soft Computing
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
Fuzzy logic-based expert system to predict the results of finite element analysis
Knowledge-Based Systems
Feedforward neural network and adaptive network-based fuzzy inference system in study of power lines
Expert Systems with Applications: An International Journal
Prediction of magnetic field near power lines by normalized radial basis function network
Advances in Engineering Software
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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.