Results 201 to 210 of about 158,186 (268)

Self-Organizing Dual-Buffer Adaptive Clustering Experience Replay (SODACER) for safe reinforcement learning in optimal control. [PDF]

Sci Rep
Khalili-Amirabadi R, Jalaeian-Farimani M, Solaymani-Fard O.   +2 more
europepmc   +1 more source

Leef: environmental neuroscience modeling for cognitive resilience. [PDF]

Front Hum Neurosci
Morina E.
europepmc   +1 more source

Mind-body exercise as epigenetic modulators: Rewiring neural circuits for stress resilience and cognitive aging. [PDF]

IBRO Neurosci Rep
Gu M, Kang J, Zhou Y, Lin L, Li C, Wu M, Fan Q, Tang M.   +7 more
europepmc   +1 more source

AI-driven adaptive vibration control in smart plate systems: a sustainable approach for next-generation sports engineering. [PDF]

Sci Rep
Lin B, Wang J, Safarpour M, Yaylacı M.
europepmc   +1 more source

Enhancement of modern defense capabilities by convergence of cognitive neuroscience and artificial intelligence. [PDF]

Front Psychol
Cosic K, Kuljanac A, Palermo S.
europepmc   +1 more source

New Trends in Adaptive Control. Adaptive Control and Neural Network.

Journal of The Society of Instrument and Control Engineers, 1996
openaire   +1 more source

From cognitive abstraction to adaptive behavior: neural bases of concept learning in autistic adolescents. [PDF]

Mol Autism
Chen Y, Hawkins B, Puckett H, Sharp K, Lopez A, Zeithamova D, Xie H, Verbalis A, VanMeter AS, Gaillard WD, Kenworthy L, Vaidya CJ.   +11 more
europepmc   +1 more source

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SSSC's Adaptive Neural Control

The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006
The aim of this paper is to present the control of a solid-state series compensator (SSSC) by a B-spline neural network (NN) in order to regulate the active power flow on a transmission line. Such control drives the phase of the series source to the desired value so as to achieve the reference power flow.
P. Z. Haro, Juan M. Ramirez
openaire   +1 more source

Neural network adaptive controllers

1990 IJCNN International Joint Conference on Neural Networks, 1990
The application of parallel distributed processing (neural networks) to the problem of adaptive control is examined. Focusing on the backpropagation paradigm, a general form of a controller consistent with neural networks is developed and combined with linear least-squares parameter-estimation techniques to suggest a structure for neural network ...
R. W. Scott, D. J. Collins
openaire   +1 more source