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Stochastic Processes

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Further analysis of global μ-stability of complex-valued neural networks with unbounded time-varying delays.

Neural networks : the official journal of the International Neural Network Society
In this paper, we consider the problem of global μ-stability for complex-valued neural networks (CVNNs) with unbounded time-varying delays and it has been widely investigated. Under mild conditions, some new sufficient conditions for global μ-stabili...

A time-series model of phase amplitude cross frequency coupling and comparison of spectral characteristics with neural data.

BioMed research international
Stochastic processes that exhibit cross-frequency coupling (CFC) are introduced. The ability of these processes to model observed CFC in neural recordings is investigated by comparison with published spectra. One of the proposed models, based on mult...

Stochastic sampled-data control for synchronization of complex dynamical networks with control packet loss and additive time-varying delays.

Neural networks : the official journal of the International Neural Network Society
This study examines the exponential synchronization of complex dynamical networks with control packet loss and additive time-varying delays. Additionally, sampled-data controller with time-varying sampling period is considered and is assumed to switc...

H∞ State Estimation for Discrete-Time Delayed Systems of the Neural Network Type With Multiple Missing Measurements.

IEEE transactions on neural networks and learning systems
This paper investigates the H∞ state estimation problem for a class of discrete-time nonlinear systems of the neural network type with random time-varying delays and multiple missing measurements. These nonlinear systems include recurrent neural netw...

Stochastic mean-field formulation of the dynamics of diluted neural networks.

Physical review. E, Statistical, nonlinear, and soft matter physics
We consider pulse-coupled leaky integrate-and-fire neural networks with randomly distributed synaptic couplings. This random dilution induces fluctuations in the evolution of the macroscopic variables and deterministic chaos at the microscopic level....

A semi-supervised learning approach for RNA secondary structure prediction.

Computational biology and chemistry
RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because o...

Delay-dependent finite-time boundedness of a class of Markovian switching neural networks with time-varying delays.

ISA transactions
In this paper, a novel method is developed for delay-dependent finite-time boundedness of a class of Markovian switching neural networks with time-varying delays. New sufficient condition for stochastic boundness of Markovian jumping neural networks ...

pth moment exponential stochastic synchronization of coupled memristor-based neural networks with mixed delays via delayed impulsive control.

Neural networks : the official journal of the International Neural Network Society
This paper concerns the pth moment synchronization in an array of generally coupled memristor-based neural networks with time-varying discrete delays, unbounded distributed delays, as well as stochastic perturbations. Hybrid controllers are designed ...

Mode-Dependent Stochastic Synchronization for Markovian Coupled Neural Networks With Time-Varying Mode-Delays.

IEEE transactions on neural networks and learning systems
This paper investigates the stochastic synchronization problem for Markovian hybrid coupled neural networks with interval time-varying mode-delays and random coupling strengths. The coupling strengths are mutually independent random variables and the...

Resilient Asynchronous H∞ Filtering for Markov Jump Neural Networks With Unideal Measurements and Multiplicative Noises.

IEEE transactions on cybernetics
This paper is concerned with the resilient H∞ filtering problem for a class of discrete-time Markov jump neural networks (NNs) with time-varying delays, unideal measurements, and multiplicative noises. The transitions of NNs modes and desired mode-de...