Results 121 to 130 of about 3,974,691 (210)
Organizational regularities in recurrent neural networks
Frontiers in Complex SystemsPrevious work has shown that the dynamical regime of Recurrent Neural Networks (RNNs)—ranging from oscillatory to chaotic and fixed point behavior—can be controlled by the global distribution of weights in connection matrices with statistically ...
Claus Metzner +3 more
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Interactive Evolving Recurrent Neural Networks Are Super-Turing Universal
, 2014 Understanding the dynamical and computational capabilities of neural models represents an issue of central importance. In this context, recent results show that interactive evolving recurrent neural networks are super-Turing, irrespective of whether ...
Jérémie Cabessa +3 more
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Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization [PDF]
, 2013 The motivation for this paper is to introduce a hybrid Neural Network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF-PSO), a time varying leverage trading strategy based on Glosten, Jagannathan and Runkle (GJR ...
Sermpinis, G. +3 more
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Classification of cyber attacks in IoMT networks using deep learning: a comparative study
Journal of Computer Sciences InstituteThe Internet of Medical Things (IoMT) is transforming healthcare through enhanced remote monitoring and real-time data exchange, but it also introduces significant cybersecurity challenges.
Asif Rahman Rumee
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Spike-based computation using classical recurrent neural networks
Neuromorphic Computing and EngineeringSpiking neural networks (SNNs) are a type of artificial neural networks in which communication between neurons is only made of events, also called spikes.
Florent De Geeter +2 more
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Bifurcation in a Discrete-Time Piecewise Constant Dynamical System
Discrete Dynamics in Nature and Society, 2013 The study of recurrent neural networks with piecewise constant transition or control functions has attracted much attention recently because they can be used to simulate many physical phenomena.
Chenmin Hou, Sui Sun Cheng
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Dynamic Object Identification with SOM-based neural networks [PDF]
Computer Science Journal of Moldova, 2014 In this article a number of neural networks based on self-organizing maps, that can be successfully used for dynamic object identification, is described. Unique SOM-based modular neural networks with vector quantized associative memory and recurrent self-
Aleksey Averkin +3 more
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, 2013 This paper addresses the global exponential dissipativity of memristor-based recurrent neural networks with time-varying delays. By constructing proper Lyapunov functionals and using M-matrix theory and LaSalle invariant principle, the sets of global ...
Wang, J, Yan, Z, Guo, Z
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