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Fuzzy logic system (FLS) promises an efficient way for obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base tuned by a human expert. In this paper, a novel approach termed probabilistic fuzzy controller with operant learning (PFCOL) for robot navigation is presented. Operant learning (OL) is a form animal learning way. The key...
This paper constructs a stochastic fuzzy controller to realize self-balancing control of two-wheeled robot. The rule of fuzzy controller is stochastic, which is automatically generated by an OCPFA learning system and optimized online. The OCPFA learning system is in fact a Probabilistic Finite Automata (PFA) which based on Skinner Operant Conditioning (Skinner OC), and it is composed by a bionic reorientation...
This paper constructs an operant conditioning learning system based on fuzzy and probabilistic automata, which used for on-line self-learning of fuzzy rules. The learning system can learn its rules on line by interaction with environment, and achieve the best rule consequent. The probability can guarantee the global superiority of learning mechanism. The fuzzy inference can improve the robustness...
This paper constructs a learning probabilistic automata (PA) model with response of operant conditioning (OC) behavior, which used for simulating skinner-pigeon experiment. The PA model with OC is a form of animal learning in that it allows an agent to adapt its actions to gain maximally from the environment while only being rewarded for correct performance. The learning mechanism achieved by design...
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