Learning gait patterns for the fuzzy synthesis of piped walk

Magdalena, Luis; Monasterio, F.

doi:10.1109/ijcf.1994.375128

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…They used GAs so as to make the FLC behaves as closely as possible to the expert but not better. Magdalena and Monasterio [13] and Magdalena and Monasterio [14] tuned the normalization limits of the variables and modified the rule bases without reduction. Linkens and Xyongesa [15] derived a GA scheme that produced an acceptable set of control rules without any reduction.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

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Learning and design of fuzzy logic controllers for pursuit-evasion differential games

Desouky¹

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Section: Genetic Algorithmsmentioning

confidence: 99%

“…We also use the same idea of this technique to combine fuzzy inference systems with gradient-based techniques resulting in an iterative adaptive network fuzzy inference system (IANFIS). 13 3. A proof of convergence for the model-building Q-learning algorithm with eligibility traces is presented.…”

Section: Thesis Contributionsmentioning

confidence: 99%

Learning and design of fuzzy logic controllers for pursuit-evasion differential games

Desouky¹

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“…The weights are updated by w: )(m + 1) = w:t)(m) + q87)0!~-'), (12) where m indicates discrete time and q is a learning rate. The outputs in the third layer can be rewritten as…”

Section: Fuzzy Neural Networkmentioning

confidence: 99%

Trajectory synthesis based on different fuzzy modeling network pruning algorithms

Juang

Proceedings of the 2000. IEEE International Conference on Control Applications. Conference Proceedings (Cat. No.00CH37162)

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In this paper, trajectory synthesis based on different fuzzy modeling network pruning algorithms is investigated. The learning scheme uses a fuzzy neural network as the controller. Simulations focus on refinement and a thorough understanding of the pruned fuzzy modeling network. The leaming scheme can control a biped robot that satisfies prespecified constraints such as the step length, crossing clearance, and walking speed. Different issues regarding the scheme have been examined. They include the effects of using weight truncation and the effects of using weight decay. The proposed scheme is tested with simulations of the BLR-GI walking robot.

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Fuzzy neural network approaches for robotic gait synthesis

Juang

2000

IEEE Trans. Syst., Man, Cybern. B

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In this paper, a learning scheme using a fuzzy controller to generate walking gaits is developed. The learning scheme uses a fuzzy controller combined with a linearized inverse biped model. The controller provides the control signals at each control time instant. The algorithm used to train the controller is "backpropagation through time". The linearized inverse biped model provides the error signals for backpropagation through the controller at control time instants. Given prespecified constraints such as the step length, crossing clearance, and walking speed, the control scheme can generate the gait that satisfies these constraints. Simulation results are reported for a five-link biped robot.

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Learning gait patterns for the fuzzy synthesis of piped walk

Cited by 7 publications

References 5 publications

Learning and design of fuzzy logic controllers for pursuit-evasion differential games

Learning and design of fuzzy logic controllers for pursuit-evasion differential games

Trajectory synthesis based on different fuzzy modeling network pruning algorithms

Fuzzy neural network approaches for robotic gait synthesis

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