Probability of failure for uncertain control systems using neural networks and multi-objective uniform-diversity genetic algorithms (MUGA).
Ali JamaliM. GhamatiB. AhmadiNader Nariman-ZadehPublished in: Eng. Appl. Artif. Intell. (2013)
Keyphrases
- multi objective
- genetic algorithm
- neural network
- control system
- evolutionary algorithm
- multi objective optimization
- multi objective evolutionary algorithms
- pareto optimal solutions
- multiobjective optimization
- optimization algorithm
- failure rate
- artificial neural networks
- particle swarm optimization
- fuzzy logic
- multi objective optimization problems
- simulated annealing
- multiple objectives
- evolutionary optimization
- back propagation
- fitness function
- probability distribution
- objective function
- nsga ii
- fuzzy systems
- conflicting objectives
- pattern recognition
- evolutionary computation
- multi objective genetic algorithms
- genetic search
- bi objective
- learning classifier systems
- pareto optimal
- genetic operators
- mutation probability
- multi objective evolutionary
- mutation operator
- differential evolution
- subjective probabilities
- modular neural networks
- evolutionary strategy
- genetic programming
- decision making
- recurrent neural networks
- control algorithm
- robust stability
- real time
- computational intelligence
- hopfield neural network
- knapsack problem
- evolutionary process
- optimization method
- premature convergence
- particle swarm