Genetic-algorithm-based learning
Machine learning
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
An introduction to genetic algorithms
An introduction to genetic algorithms
Fuzzy sets, fuzzy logic, applications
Fuzzy sets, fuzzy logic, applications
Practical genetic algorithms
Practical Handbook of Genetic Algorithms: New Frontiers
Practical Handbook of Genetic Algorithms: New Frontiers
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
| Hi-index | 0.00 |
The present work addresses the problem of on-line signal trend identification within a fuzzy logic-based methodology previously proposed in the literature. A modification in the application of the methodology is investigated which entails the use of singletons instead of triangular fuzzy numbers for the characterization of the truth values of the six parameters describing the dynamic trend of the evolving process. Further, calibration of the model is performed by a genetic algorithm procedure. In an example of application of the method, this procedure is also exploited for feature selection, i.e. for choosing which of the measured plant signals are relevant for the transient identification.