Results 191 to 200 of about 9,506 (241)
Boosting reservoir computing with brain-inspired adaptive control of E-I balance. [PDF]
Nat CommunSrinivasan K, Plenz D, Girvan M.
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Targeting and synchronization at tokamak with recurrent artificial neural networks [PDF]
Neural Computing and Applications, 2011 In this letter, we propose an adaptive recurrent artificial neural networks synchronization of H-mode and Edge Localized Modes that is important for obtaining a long pulse tokamak without disruption regime. The deterministic part of the plasma behavior should be synchronized with stochastic part by introducing stochastic artificial neural network.
Danilo Rastovic, Rastovic Danilo
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Artificial Neural Network-Based Nonlinear Black-Box Modeling of Synchronous Generators
IEEE Transactions on Industrial Informatics, 2023Data availability: The complete experimental measurements presented in Figs. 6 and 7, along with some of the used Matlab codes and Simulink models, are located on the following link: https://drive.google.com/file/d/1OlNfo56QIgJUaKioGhenOJ28WNt88y3/view?usp=sharing. It can be downloaded with the permission of the authors.
Mihailo Micev +5 more
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Synchronization in an artificial neural network
Chaos, Solitons & Fractals, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yueh, Wen-Chyuan, Cheng, Sui Sun
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Synchronization in chaotic systems with artificial neural networks
Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 2002Chaos is the apparently irregular motion that is, in reality, nonlinear but deterministic. Chaos exhibits extremely sensitive dependence on initial conditions; this tends to prevent the prediction the behavior of a chaotic system. It has been demonstrated, however, that chaotic systems can be synchronized by linking them with common driving signals ...
K. Otawara, L.T. Fan
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Synchronizing high-dimensional chaos by an artificial neural network
Proceedings of 35th IEEE Conference on Decision and Control, 2002A method for synchronizing high-dimensional chaos in experimental systems is devised by exploiting the learning and predicting capabilities of an artificial neural network (ANN). This method can be regarded as an extension of the methods developed by us for synchronizing and controlling chaos.
K. Otawara, L.T. Fan
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Direct Torque Control of Synchronous Motors Using Artificial Neural Network
2019 IEEE International Conference on Electro Information Technology (EIT), 2019This paper introduces artificial neural network (ANN) controller for constant speed synchronous motors. The objective of the introduced ANN controller is directly control of the motor torque through three different control techniques namely, field control, armature control and field-armature control under load torque variations with minimizing the ...
Mohammed I. Mosaad, Fahd Ahmed Banakhr
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A new protection algorithm for synchronous generators using artificial neural networks
2011 11th International Conference on Hybrid Intelligent Systems (HIS), 2011This paper presents a new protection scheme for synchronous generator using an artificial neural network. The proposed scheme is able to discriminate internal and external faults of synchronous generator, consists of feature extraction using eigenvalues and fault classification using artificial neural networks.
Narri Yadaiah, Nagireddy Ravi
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Statistical Modelling of Artificial Neural Network for Sorting Temporally Synchronous Spikes
2015Artificial neural network (ANN) models are able to predict future events based on current data. The usefulness of an ANN lies in the capacity of the model to learn and adjust the weights following previous errors during training. In this study, we carefully analyse the existing methods in neuronal spike sorting algorithms.
Rakesh Veerabhadrappa +6 more
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