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Neuro-fuzzy control for turning processes
Proceedings of the 2003 American Control Conference, 2003., 2004The neuro-fuzzy control method for a constant cutting force metal turning process is proposed in this paper. The model of a turning process is described. After discussing the neuron controller and a basic fuzzy controller, the neuro-fuzzy control system is designed.
null Ning Wang +2 more
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A self-learning neuro fuzzy controller
Proceedings of ICNN'95 - International Conference on Neural Networks, 2002This paper describes a neuro-fuzzy controller that can mimic the way a human controller might function. The controller comprises an artificial neural network (ANN), a knowledge base and a fuzzy inference engine (FIE). Initially the controller learns the system dynamics, which it stores in its knowledge base.
J.K. Durgamba, M.W. Cook
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Design of a neuro-fuzzy controller
1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, 2002The essence of fuzzy control is to build a model of human expert who is capable of controlling the plant without thinking in terms of mathematical model. The transformation of expert's knowledge in terms of control rules to fuzzy framework has not been formalized and arbitrary choices concerning, for example, the shape of membership functions have to ...
G.S. Sandhu, K.S. Rattan
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2013
Performance improvement of fuzzy logic controllers (FLC) can be achieved by adjusting the membership functions (MF). Neuro-fuzzy approaches are mostly used in such adjustment procedure, which involves several parameters of the MFs to be adjusted. In many cases, tuning the scaling factors gives the same performance as with MFs adjustment.
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Performance improvement of fuzzy logic controllers (FLC) can be achieved by adjusting the membership functions (MF). Neuro-fuzzy approaches are mostly used in such adjustment procedure, which involves several parameters of the MFs to be adjusted. In many cases, tuning the scaling factors gives the same performance as with MFs adjustment.
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1995
Neuronale Netze sind technische Abbilder von Nervensystemen, wie sie wesentlich fur die Gehirnfunktionen des Menschen (und naturlich auch anderer, hoher oder weniger hoch entwickelter Spezies) von Bedeutung sind. Ein Nervensystem besteht aus einer Vielzahl von miteinander “kommunizierenden” Nervenzellen, die als Neuronen bezeichnet werden.
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Neuronale Netze sind technische Abbilder von Nervensystemen, wie sie wesentlich fur die Gehirnfunktionen des Menschen (und naturlich auch anderer, hoher oder weniger hoch entwickelter Spezies) von Bedeutung sind. Ein Nervensystem besteht aus einer Vielzahl von miteinander “kommunizierenden” Nervenzellen, die als Neuronen bezeichnet werden.
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Simple Adaptive Neuro-Fuzzy Controller
IFAC Proceedings Volumes, 1998Abstract A simple adaptive neuro-fuzzy controller based on hybrid model structure is presented. It combines the sliding mode control strategy for the rule base and a modified reinforcement learning algorithm based on fuzzy error estimation for membership functions tuning. Benchmark tests show the good system performance in comparison to the classical
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Implementation of Neuro-fuzzy Control Systems
2021 IV International Conference on Control in Technical Systems (CTS), 2021Neuro-fuzzy approach for implementing control systems is considered. Neuro-fuzzy systems are a tool for a development of trainable control systems with high interpretability. These systems can be trained to work in new conditions. There is a possibility to analyze the actions, which implement the control.
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Evolutionary neuro-fuzzy control
2017This chapter focuses on the use of genetic algorithms (GAs) in the design of FLC. An approach of adopting genetic algorithm search is adopted to determine optimal FLC scaling factors. The approach is then extended by adoption of neural network learning of the scaling factors leading to a neuro-fuzzy control method. This is further combined with genetic
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TMS320 DSP based neuro-fuzzy controller
1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century, 2002Fuzzy logic can be used to map complex nonlinear relations by a set of IF-THEN rules. The membership functions are designed by intuitive human reasoning. This poses two problems; first, for different control application a new set of membership functions have to developed and second, once these membership functions are developed and implemented there is
K.K. Kumbla +2 more
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Evolutionary-Neuro-Fuzzy Control
2013It has been demonstrated that learning the shape of sigmoidal function can improve performance of neuro-fuzzy controller. Backpropagation learning algorithm does not include the parameter of the sigmoidal function shape. This chapter proposes the use of genetic algorithm to learn the weights, biases and shape of the sigmoidal function of the neural ...
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