Advertisement

Topics

PubMed Journals Articles About "Neural Networks Everywhere" RSS

23:51 EDT 20th July 2018 | BioPortfolio

Neural Networks Everywhere PubMed articles on BioPortfolio. Our PubMed references draw on over 21 million records from the medical literature. Here you can see the latest Neural Networks Everywhere articles that have been published worldwide.

More Information about "Neural Networks Everywhere" on BioPortfolio

We have published hundreds of Neural Networks Everywhere news stories on BioPortfolio along with dozens of Neural Networks Everywhere Clinical Trials and PubMed Articles about Neural Networks Everywhere for you to read. In addition to the medical data, news and clinical trials, BioPortfolio also has a large collection of Neural Networks Everywhere Companies in our database. You can also find out about relevant Neural Networks Everywhere Drugs and Medications on this site too.

Showing "Neural networks everywhere" PubMed Articles 1–25 of 4,800+

Impact of leakage delay on bifurcation in high-order fractional BAM neural networks.

The effects of leakage delay on the dynamics of neural networks with integer-order have lately been received considerable attention. It has been confirmed that fractional neural networks more appropriately uncover the dynamical properties of neural networks, but the results of fractional neural networks with leakage delay are relatively few. This paper primarily concentrates on the issue of bifurcation for high-order fractional bidirectional associative memory(BAM) neural networks involving leakage delay. T...


Corrigendum to "Hopfield networks as a model of prototype-based category learning: A method to distinguish trained, spurious, and prototypical attractors" Neural Netw. 91 (2017) 76-84.

Nonlinear recurrent neural networks for finite-time solution of general time-varying linear matrix equations.

In order to solve general time-varying linear matrix equations (LMEs) more efficiently, this paper proposes two nonlinear recurrent neural networks based on two nonlinear activation functions. According to Lyapunov theory, such two nonlinear recurrent neural networks are proved to be convergent within finite-time. Besides, by solving differential equation, the upper bounds of the finite convergence time are determined analytically. Compared with existing recurrent neural networks, the proposed two nonlinear...


Global exponential synchronization of inertial memristive neural networks with time-varying delay via nonlinear controller.

The paper is concerned with the synchronization problem of inertial memristive neural networks with time-varying delay. First, by choosing a proper variable substitution, inertial memristive neural networks described by second-order differential equations can be transformed into first-order differential equations. Then, a novel controller with a linear diffusive term and discontinuous sign term is designed. By using the controller, the sufficient conditions for assuring the global exponential synchronizatio...

Boundedness and global robust stability analysis of delayed complex-valued neural networks with interval parameter uncertainties.

In this paper, the boundedness and robust stability for a class of delayed complex-valued neural networks with interval parameter uncertainties are investigated. By using Homomorphic mapping theorem, Lyapunov method and inequality techniques, sufficient condition to guarantee the boundedness of networks and the existence, uniqueness and global robust stability of equilibrium point is derived for the considered uncertain neural networks. The obtained robust stability criterion is expressed in complex-valued ...

New conditions for global stability of neutral-type delayed Cohen-Grossberg neural networks.

This paper carries out a theoretical investigation of the class of neutral-type delayed Cohen-Grossberg neural networks by using the Lyapunov stability theory. By employing a suitable Lyapunov functional candidate, we derive some new delay independent sufficient conditions for the global asymptotic stability of the equilibrium point for the neutral-type Cohen-Grossberg neural networks with time delays. The obtained stability conditions can be completely characterized by the networks parameters of the neutra...

pth moment exponential stability of stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays.

Stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays play an increasingly important role in the design and implementation of neural network systems. Under the framework of Filippov solutions, the issues of the pth moment exponential stability of stochastic memristor-based BAM neural networks are investigated. By using the stochastic stability theory, Itô's differential formula and Young inequality, the criteria are derived. Meanwhile, with Lyapunov approach and...

Anti-synchronization of complex-valued memristor-based delayed neural networks.

This paper investigates the anti-synchronization of complex-valued memristor-based neural networks with time delays via designed external controllers. By constructing appropriate Lyapunov functions and using inequality technique, two different types of controllers are derived to guarantee the exponential anti-synchronization of complex-valued memristor-based delayed neural networks. Compared with existing relevant results, the proposed results of this paper are more general and less conservative. In additio...

Impulsive synchronization of stochastic reaction-diffusion neural networks with mixed time delays.

This paper discusses impulsive synchronization of stochastic reaction-diffusion neural networks with Dirichlet boundary conditions and hybrid time delays. By virtue of inequality techniques, theories of stochastic analysis, linear matrix inequalities, and the contradiction method, sufficient criteria are proposed to ensure exponential synchronization of the addressed stochastic reaction-diffusion neural networks with mixed time delays via a designed impulsive controller. Compared with some recent studies, t...

Fixed-time stabilization of impulsive Cohen-Grossberg BAM neural networks.

This article is concerned with the fixed-time stabilization for impulsive Cohen-Grossberg BAM neural networks via two different controllers. By using a novel constructive approach based on some comparison techniques for differential inequalities, an improvement theorem of fixed-time stability for impulsive dynamical systems is established. In addition, based on the fixed-time stability theorem of impulsive dynamical systems, two different control protocols are designed to ensure the fixed-time stabilization...

Unified synchronization criteria in an array of coupled neural networks with hybrid impulses.

This paper investigates the problem of globally exponential synchronization of coupled neural networks with hybrid impulses. Two new concepts on average impulsive interval and average impulsive gain are proposed to deal with the difficulties coming from hybrid impulses. By employing the Lyapunov method combined with some mathematical analysis, some efficient unified criteria are obtained to guarantee the globally exponential synchronization of impulsive networks. Our method and criteria are proved to be eff...

Modeling spike-wave discharges by a complex network of neuronal oscillators.

The organization of neural networks and the mechanisms, which generate the highly stereotypical for absence epilepsy spike-wave discharges (SWDs) is heavily debated. Here we describe such a model which can both reproduce the characteristics of SWDs and dynamics of coupling between brain regions, relying mainly on properties of hierarchically organized networks of a large number of neuronal oscillators.

Quasi-projective synchronization of fractional-order complex-valued recurrent neural networks.

In this paper, without separating the complex-valued neural networks into two real-valued systems, the quasi-projective synchronization of fractional-order complex-valued neural networks is investigated. First, two new fractional-order inequalities are established by using the theory of complex functions, Laplace transform and Mittag-Leffler functions, which generalize traditional inequalities with the first-order derivative in the real domain. Additionally, different from hybrid control schemes given in th...

Multistability and multiperiodicity in impulsive hybrid quaternion-valued neural networks with mixed delays.

The existence of multiple exponentially stable equilibrium states and periodic solutions is investigated for Hopfield-type quaternion-valued neural networks (QVNNs) with impulsive effects and both time-dependent and distributed delays. Employing Brouwer's and Leray-Schauder's fixed point theorems, suitable Lyapunov functionals and impulsive control theory, sufficient conditions are given for the existence of 16n attractors, showing a substantial improvement in storage capacity, compared to real-valued or co...

H∞ state estimation of stochastic memristor-based neural networks with time-varying delays.

This paper addresses the problem of H∞ state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the H∞ state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficie...

Global stabilization analysis of inertial memristive recurrent neural networks with discrete and distributed delays.

This paper deals with the stabilization problem of memristive recurrent neural networks with inertial items, discrete delays, bounded and unbounded distributed delays. First, for inertial memristive recurrent neural networks (IMRNNs) with second-order derivatives of states, an appropriate variable substitution method is invoked to transfer IMRNNs into a first-order differential form. Then, based on nonsmooth analysis theory, several algebraic criteria are established for the global stabilizability of IMRNNs...

Bio-inspired spiking neural network for nonlinear systems control.

Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or dif...

Modified Primal-Dual Neural Networks for Motion Control of Redundant Manipulators With Dynamic Rejection of Harmonic Noises.

In recent decades, primal-dual neural networks, as a special type of recurrent neural networks, have received great success in real-time manipulator control. However, noises are usually ignored when neural controllers are designed based on them, and thus, they may fail to perform well in the presence of intensive noises. Harmonic noises widely exist in real applications and can severely affect the control accuracy. This work proposes a novel primal-dual neural network design that directly takes noise contro...

Improving efficiency in convolutional neural networks with multilinear filters.

The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices. However, current deep net architectures are heavy with millions of parameters and require billions of floating point operations. Several works have been developed to compress a pre-trained deep network to reduce memory footprint and, possibly, computation. Instead of compressing a pre-trained network, in this work, we propose a generic neural network layer structur...

Improved Stability Analysis for Delayed Neural Networks.

In this brief, by constructing an augmented Lyapunov-Krasovskii functional in a triple integral form, the stability analysis of delayed neural networks is investigated. In order to exploit more accurate bounds for the derivatives of triple integrals, new double integral inequalities are developed, which include some recently introduced estimation techniques as special cases. The information on the activation function is taken into full consideration. Taking advantages of the proposed inequalities, the stabi...

O(t-α)-synchronization and Mittag-Leffler synchronization for the fractional-order memristive neural networks with delays and discontinuous neuron activations.

This paper investigates O(t-α)-synchronization and adaptive Mittag-Leffler synchronization for the fractional-order memristive neural networks with delays and discontinuous neuron activations. Firstly, based on the framework of Filippov solution and differential inclusion theory, using a Razumikhin-type method, some sufficient conditions ensuring the global O(t-α)-synchronization of considered networks are established via a linear-type discontinuous control. Next, a new fractional differential inequality ...

Generalized pinning synchronization of delayed Cohen-Grossberg neural networks with discontinuous activations.

In this article, generalized pinning synchronization problem is investigated for a class of Cohen-Grossberg neural networks with discontinuous neuron activations and mixed delays. By designing generalized pinning state-feedback and adaptive controllers, several criteria for global exponential synchronization and global asymptotical synchronization of the drive-response based system are obtained in view of non-smooth analysis theory with generalized Lyapunov functional method, in which first pinning the neur...

Leaderless synchronization of coupled neural networks with the event-triggered mechanism.

This paper is concerned with leaderless synchronization of coupled delayed neural networks. A distributed event-triggered control strategy under the periodic sampling scheme is introduced to reduce control updates. By introducing a weighted average state as a virtual leader, the leaderless synchronization problem can be transformed to the stability problem of the error system, which is defined as the distance between each node and the virtual leader. A leaderless synchronization criterion under the periodic...

The convergence analysis of SpikeProp algorithm with smoothing Lregularization.

Unlike the first and the second generation artificial neural networks, spiking neural networks (SNNs) model the human brain by incorporating not only synaptic state but also a temporal component into their operating model. However, their intrinsic properties require expensive computation during training. This paper presents a novel algorithm to SpikeProp for SNN by introducing smoothing Lregularization term into the error function. This algorithm makes the network structure sparse, with some smaller weights...

Neural electrical activity and neural network growth.

The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interne...


Advertisement
Quick Search
Advertisement
Advertisement