Noise suppress or express exponential growth for hybrid Hopfield neural networks
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Publication:411621
DOI10.1016/j.physleta.2010.03.005zbMath1236.92009OpenAlexW2022284471MaRDI QIDQ411621
Publication date: 30 April 2012
Published in: Physics Letters. A (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.physleta.2010.03.005
Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20)
Related Items (5)
Noise expresses exponential decay for globally exponentially stable nonlinear time delay systems ⋮ Almost sure exponential stabilization of neural networks by aperiodically intermittent control based on delay observations ⋮ Stochastic suppression and almost surely stabilization of non-autonomous hybrid system with a new general one-sided polynomial growth condition ⋮ Stochastic suppression and stabilization of nonlinear differential systems with general decay rate ⋮ Further results on robustness analysis of global exponential stability of recurrent neural networks with time delays and random disturbances
Cites Work
- State estimation for jumping recurrent neural networks with discrete and distributed delays
- Stabilization and destabilization of hybrid systems of stochastic differential equations
- Mean square exponential stability of stochastic delayed Hopfield neural networks
- On global asymptotic stability of neural networks with discrete and distributed delays
- Robust stability analysis of switched Hopfield neural networks with time-varying delay under uncertainty
- Noise expresses exponential growth under regime switching
- Noise suppresses exponential growth under regime switching
- Almost sure exponential stability of neutral stochastic differential difference equations
- Global exponential stability of Hopfield neural networks with continuously distributed delays
- Switching in systems and control
- Environmental Brownian noise suppresses explosions in population dynamics.
- Global asymptotic stability of Hopfield neural network involving distributed delays
- Almost sure exponential stabilisation of stochastic systems by state-feedback control
- Noise suppresses or expresses exponential growth
- An estimation of the domain of attraction and convergence rate for Hopfield continuous feedback neural networks
- Stochastic stabilization of differential systems with general decay rate
- DELAY-DEPENDENT ASYMPTOTIC STABILITY OF NEURAL NETWORKS WITH TIME-VARYING DELAYS
- Stabilization of Linear Systems by Noise
- Stabilization and Destabilization by Noise in the Plane
- A critical analysis on global convergence of Hopfield-type neural networks
- Complete stability of cellular neural networks with time-varying delays
- Stabilization and Destabilization of Nonlinear Differential Equations by Noise
- Neural networks and physical systems with emergent collective computational abilities.
- Stochastic Differential Equations with Markovian Switching
- Stabilization of a class of nonlinear stochastic systems
- Stability of stochastic delay neural networks
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