Results 201 to 210 of about 47,257 (241)

Multirobot convoying using neuro-fuzzy control

Proceedings of 13th International Conference on Pattern Recognition, 1996
In this paper real-time implementation of multirobot convoying behavior utilizing neuro-fuzzy control is presented. With only nine rules, the follower convoys the leader closely and smoothly by varying its speed and changing its direction. When the leader stops, the follower stops very close to the specified safe distance (within an inch). The follower
null Kim C. Ng, M.M. Trivedi
openaire   +1 more source

Genetic-Neuro-Fuzzy Controllers for Second Order Control Systems

2011 UKSim 5th European Symposium on Computer Modeling and Simulation, 2011
Overshoot, settling and rise time define the timing parameters of a control system. The main challenge is to attempt to reduce these parameters to achieve good control performances. The target is to obtain the optimal timing values. In this paper, three different approaches are presented to improve the control performances of second order control ...
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Neuro-Fuzzy Approaches to Anticipatory Control

1995
Anticipatory systems are systems where change of state is based on information pertaining to present as well as future states. Cellular organisms, industrial processes, global markets, provide many examples of behavior where global output is the result of anticipated not only current state. In the global economy, for example, the anticipation of an oil
L. H. Tsoukalas, A. Ikonomopoulos, R. E. Uhrig   +2 more
openaire   +1 more source

A hybrid neuro-fuzzy PID controller

Fuzzy Sets and Systems, 1998
A hybrid neuro-fuzzy control strategy and its corresponding rule generating approach is proposed. According to this approach, the fuzzy control rules can be generated automatically via fuzzy inputs, and then the appropriate control action can be deduced efficiently by a simplified fuzzy inference engine.
Minyou Chen, D.A. Linkens
openaire   +1 more source

Adaptive Neuro-Fuzzy Sliding Mode Controller

International Journal of System Dynamics Applications, 2018
A novel adaptive sliding mode controller using neuro-fuzzy network based on adaptive cooperative particle sub-swarm optimization (ACPSSO) is presented in this article for nonlinear systems control. The proposed scheme combines the advantages of adaptive control, neuro-fuzzy control, and sliding mode control (SMC) strategies without system model ...
Sana Bouzaida, Anis Sakly
openaire   +1 more source

Neuro-fuzzy controlled Induction Generator system

2011 2nd International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology (Wireless VITAE), 2011
A Recurrent Functional-Link (FL) based Fuzzy Neural Network (FNN) controller with is proposed in this work to control a three phase Induction Generator (IG) system for stand-alone power application. The ac/dc power converter and a dc/ac power inverter are developed to convert the electric power generated by a three-phase IG from variable frequency and ...
S. N. Sabreen, M Malleswaran
openaire   +1 more source

Adaptive State-Space Neuro Fuzzy Control

2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2018
This paper proposes a new general adaptive state- space Neuro-Fuzzy control framework. It combines a eight-layered neuro-fuzzy architecture with a state feedback quadratic stabilising controller. Both the model and controller are updated online within a constrained unscented Kalman filter.
Paulo Gil, Tiago Oliveira, Alberto Cardoso, Luis Palma   +3 more
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Design of adaptive neuro-fuzzy controllers

Proceedings of IEEE International Conference on Systems, Man and Cybernetics, 2002
This paper proposes a design of adaptive fuzzy-logic based controllers with neural networks. A detailed discussion of effects of different reasoning methods on fuzzy controls is given and used to illustrate the need for an adaptive implementation of fuzzy control systems.
null Hung-Man Kim, null Fei-Yue Wang
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Neuro Fuzzy Modeling of Control Systems

16th International Conference on Electronics, Communications and Computers (CONIELECOMP'06), 2006
The 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.
E. Gorrostieta, C. Pedraza
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Incentive games for neuro-fuzzy control

Proceedings of 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society, 2002
Introduces a two-level modular neuro-fuzzy network based on incentive games where the modules are organized as autonomous local optimizers in a leader-follower game hierarchy. Incentive-reaction pairs are used as a measure for the capacity and responsiveness assessment of each follower module.
A.M. Cakmakci, C. Isik
openaire   +1 more source