Genetic fuzzy control for time-varying delayed uncertain systems with a robust stability safeguard
Applied Mathematics and Computation
Automatic generation of fuzzy rule-based models from data by genetic algorithms
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
An evolutionary approach to fuzzy rule-based model synthesis using indices for rules
Fuzzy Sets and Systems - Theme: Modeling and control
A fuzzy controller with evolving structure
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Bio-inspired systems (BIS)
Genetically generated double-level fuzzy controller with a fuzzy adjustment strategy
Proceedings of the 9th annual conference on Genetic and evolutionary computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Motion control of a nonlinear spring by reinforcement learning
Control and Intelligent Systems
Fuzzy numerical schemes for hyperbolic differential equations
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
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This paper presents a new method for learning a fuzzy logic controller automatically. A reinforcement learning technique is applied to a multilayer neural network model of a fuzzy logic controller. The proposed self-learning fuzzy logic control that uses the genetic algorithm through reinforcement learning architecture, called a genetic reinforcement fuzzy logic controller, can also learn fuzzy logic control rules even when only weak information such as a binary target of “success” or “failure” signal is available. In this paper, the adaptive heuristic critic algorithm of Barto et al. (1987) is extended to include a priori control knowledge of human operators. It is shown that the system can solve more concretely a fairly difficult control learning problem. Also demonstrated is the feasibility of the method when applied to a cart-pole balancing problem via digital simulations