Evolutionary optimization of echo state networks: multiple motor pattern learning [PDF]
, 2010 Krause AF, Dürr V, Bläsing B, Schack T. Evolutionary optimization of echo state networks: multiple motor pattern learning. In: Artificial neural networks and intelligent information processing : proceedings of the 6th International Workshop on ...
Krause, André Frank +3 more
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Cross refinement network with edge detection for salient object detection
IET Signal Processing, 2021 Salient object detection aims to identify the most attractive objects from images. However, their boundaries are typically of poor quality when predicted using available methods.
Junjiang Xiang +4 more
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A VLSI neuromorphic device for implementing spike-based neural networks [PDF]
, 2011 Indiveri G, Chicca E. A VLSI neuromorphic device for implementing spike-based neural networks. Presented at the Proceedings of the 21st Italian Workshop on Neural Nets (WIRN).We present a neuromorphic VLSI device which comprises hybrid analog/digital ...
Morabito, C. F. +5 more
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Exponential stability of delayed recurrent neural networks with Markovian jumping parameters [PDF]
, 2006 This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.In this Letter, the global exponential stability analysis problem is considered for a class of recurrent ...
Liu, Y, Yu, L, Wang, Z, Iu, X
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Prediction of Convergence Dynamics of Design Performance using Differential Recurrent Neural Networks [PDF]
, 2008 Computational Fluid Dynamics (CFD) simulations have been extensively used in many aerodynamic design optimization problems, such as wing and turbine blade shape design optimization.
Sendhoff, Bernhard +12 more
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Parallelizing Linear Recurrent Neural Nets Over Sequence Length
CoRR, 2017 9 pages.
Eric Martin, Chris Cundy
openaire +4 more sources
Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation. [PDF]
, 2008 In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used in nonlinear model predictive control (NMPC) context. The neural network represented in a general nonlinear state-space form is used to predict the future ...
Cao, Yi, Al Seyab, Rihab Khalid Shakir
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A BiLSTM cardinality estimator in complex database systems based on attention mechanism
CAAI Transactions on Intelligence Technology, 2022 An excellent cardinality estimation can make the query optimiser produce a good execution plan. Although there are some studies on cardinality estimation, the prediction results of existing cardinality estimators are inaccurate and the query efficiency ...
Qiang Zhou +8 more
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Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks [PDF]
Biological Cybernetics, 2012 Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in ...
Sebastian Bitzer, Stefan J. Kiebel
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A Model for Programmability and Virtuality in Dynamical Neural Networks [PDF]
, 2009 In this dissertation a fixed-weight architecture for Continuous Time Recurrent Neural Networks (CTRNNs) is proposed in order to give an account for biological phenomena, controlled by neuronal activity, in which changes of behavior occur so fast that ...
Donnarumma, Francesco
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