Applying genetics to fuzzy logic
AI Expert
Genetic algorithms for learning the rule base of fuzzy logic controller
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Fuzzy and Neuro-Fuzzy Systems in Medicine
Fuzzy and Neuro-Fuzzy Systems in Medicine
Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
A survey on use of soft computing methods in medicine
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
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Modelling of joint properties of lower limbs in people with spinal cord injury is significantly challenging for researchers due to the complexity of the system. The objective of this study is to develop a knee joint model capable of relating electrical parameters to dynamic joint torque as well as knee angle for functional electrical stimulation application. The joint model consists of a segmental dynamic, time-invariant passive properties and uncertain time-variant active properties. The knee joint model structure comprising optimised equations of motion and fuzzy models to represent the passive viscoelasticity and active muscle properties is formulated. The model thus formulated is optimised using genetic optimization, and validated against experimental data. The developed model can be used for simulation of joint movements as well as for control development. The results show that the model developed gives an accurate dynamic characterisation of the knee joint.