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Neuro-Fuzzy Learning and Genetic Algorithm Approach with Chaos Theory Principles Applying for Diagnostic Problem Solving.
- Source :
-
World Congress on Engineering 2009 (Volume 1) . 2009, p54-62. 9p. 3 Diagrams, 2 Graphs. - Publication Year :
- 2009
-
Abstract
- Performance results for finding the best genetic algorithm for the complex real problem of optimal machinery equipment operation and predictive maintenance are presented. A genetic algorithm is a stochastic computational model that seeks the optimal solution to an objective function. A methodology calculation is based on the idea of measuring the increase of fitness and fitness quality evaluation with chaos theory principles applying within genetic algorithm environment. Fuzzy neural networks principles are effectively applied in solved manufacturing problems mostly where multisensor integration, real - timeness, robustness and learning abilities are needed. A modified Mamdani neuro-fuzzy system improves the interpretability of used domain knowledge. [ABSTRACT FROM AUTHOR]
- Subjects :
- *GENETIC algorithms
*FUZZY systems
*CHAOS theory
*ENTROPY
*MACHINE learning
Subjects
Details
- Language :
- English
- ISBNs :
- 9789881701251
- Database :
- Academic Search Index
- Journal :
- World Congress on Engineering 2009 (Volume 1)
- Publication Type :
- Book
- Accession number :
- 51196515