Results 191 to 200 of about 17,567 (257)
Some of the next articles are maybe not open access.
Intelligent Automation & Soft Computing, 1999
ABSTRACTIn this paper, a new analog neuro-fuzzy controller is presented. Standard CMOS technology was used for implementation of the building blocks. Internal architecture provides the trade-off between speed and the number of fuzzy rules and/or number of antecedents. Although the input signals, output signals and the processor circuits are all analog,
Nasser Sadati, Hooman Mohseni
openaire +1 more source
ABSTRACTIn this paper, a new analog neuro-fuzzy controller is presented. Standard CMOS technology was used for implementation of the building blocks. Internal architecture provides the trade-off between speed and the number of fuzzy rules and/or number of antecedents. Although the input signals, output signals and the processor circuits are all analog,
Nasser Sadati, Hooman Mohseni
openaire +1 more source
Neuro-Fuzzy Identifiers and Controllers
Journal of Intelligent & Fuzzy Systems, 1994A neuro-fuzzy identifier for fuzzy modeling of a system is explained, and a control structure using this neurofuzzy identifier is proposed. The neuro-fuzzy identifier contains not only an adaptive clustering process for determining center points of the input and virtual output membership functions but also an adaptive process for deciding the shapes of
M.Lee +2 more
openaire +1 more source
Neuro-fuzzy Model-based Control
Journal of Intelligent and Robotic Systems, 1998The paper deals with the Neuro-fuzzy model-based control and its application. Various types of the fuzzy logic and neural-net-based nonlinear autoregressive models with exogenous variables are reviewed with respect to the model error. Two types of model-based neuro-fuzzy control – a cancellation controller and a predictive controller are reviewed – and
Drago Matko +2 more
openaire +2 more sources
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. Kanaka Durgamba, M. W. Cook
openaire +1 more source
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.
Ning Wang, Jun Liang, Guang Yang 0002
openaire +1 more source
Neuro Fuzzy Modeling of Control Systems
16th International Conference on Electronics, Communications and Computers (CONIELECOMP'06), 2006The analysis of the models is carried out starting from experimental data of a multivariable system MISO (Many Input Single Output). The models implementation was made using fuzzy logic. In fuzzy logic, the cluster technique was used to decrease the number of rules to use in the identification.
Efrén Gorrostieta Hurtado +1 more
openaire +1 more source
Neuro-fuzzy controller for helicopter motion control
10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297), 2005This paper presents a neuro-fuzzy controller to control a non-linear system such as the flight of a helicopter in the hover and forward flight mode positions. Hovering is a formidable stability problem, where helicopter pilots typically train for weeks before managing to do it manually.
Tito G. B. Amaral, Manuel M. Crisóstomo
openaire +1 more source
A neuro-fuzzy systems for control applications
1st International Symposium on Neuro-Fuzzy Systems, AT '96. Conference Report, 2002This paper describes DANIELA a neuro-fuzzy system for control applications. The system is based on a custom neural device that can implement either multilayer perceptrons, radial basis functions or fuzzy paradigms. The system implements intelligent control algorithms mixing neuro-fuzzy paradigms with finite state automata and is used to control a ...
F. BERARDI +3 more
openaire +2 more sources
Neuro-fuzzy techniques for traffic control
Control Engineering Practice, 1997Abstract Intersection stage control using Forward Dynamic Programming (FDP) with a sample time of five seconds is already effective on the field. Neuro-fuzzy techniques are proposed here for controlling each light each second. Rules, fuzzyfication and inference are modeled by a neural network.
J.J. Henry, J.L. Farges, J.L. Gallego
openaire +1 more source