Diagnostic Problem Solving by Means of Neuro-Fuzzy Learning, Genetic Algorithm and Chaos Theory Principles Applying

摘要

The complexity and reliability demands of contemporary industrial systems and technological processes require the development of new fault diagnosis approaches. 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.