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PubMed Journals Articles About "Neural Networks Everywhere" RSS

20:05 EDT 21st September 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.

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Showing "Neural networks everywhere" PubMed Articles 1–25 of 5,100+

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.


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...

The Vapnik-Chervonenkis dimension of graph and recursive neural networks.

The Vapnik-Chervonenkis dimension (VC-dim) characterizes the sample learning complexity of a classification model and it is often used as an indicator for the generalization capability of a learning method. The VC-dim has been studied on common feed-forward neural networks, but it has yet to be studied on Graph Neural Networks (GNNs) and Recursive Neural Networks (RecNNs). This paper provides upper bounds on the order of growth of the VC-dim of GNNs and RecNNs. GNNs and RecNNs are from a new class of neural...


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...

Synchronization criteria for inertial memristor-based neural networks with linear coupling.

This paper is concerned with the synchronization problem for an array of memristive neural networks with inertial term, linear coupling and time-varying delay. Since parameters in the connection weight matrices are state-dependent, that is to say, the connection weight matrices jump in certain intervals, the mathematical model of the coupled inertial memristive neural networks can be considered as an interval parametric uncertain system. Based on the interval parametric uncertainty theory, two different syn...

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...

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...

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...

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 ...

Passivity and stability analysis of neural networks with time-varying delays via extended free-weighting matrices integral inequality.

This paper is concerned with the problem of passivity for uncertain neural networks with time-varying delays. First, the recently developed integral inequality called generalized free-matrix-based integral inequality is extended to estimate further tight lower bound of integral terms. By constructing a suitable augmented LKF, an enhanced passivity condition for the concerned network is derived in terms of linear matrix inequalities (LMIs). Here, the integral terms having three states in its quadratic form i...

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks.

Biological neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifelong learning. The interplay of these elements leads to the emergence of biological intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) employ simulated evolution in-silico to breed plastic neural networks with the aim to autonomously design and create learning systems. EPANN experiments evolve networks that include both inna...

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...

Evolving Spiking Neural Networks for online learning over drifting data streams.

Nowadays huge volumes of data are produced in the form of fast streams, which are further affected by non-stationary phenomena. The resulting lack of stationarity in the distribution of the produced data calls for efficient and scalable algorithms for online analysis capable of adapting to such changes (concept drift). The online learning field has lately turned its focus on this challenging scenario, by designing incremental learning algorithms that avoid becoming obsolete after a concept drift occurs. Des...

Monostable multivibrators as novel artificial neurons.

Retriggerable and non-retriggerable monostable multivibrators are simple timers with a single characteristic, their period. Motivated by the fact that monostable multivibrators are implementable in large quantities as counters in digital programmable hardware, we set out to investigate their applicability as building blocks of artificial neural networks. We derive the nonlinear input-output firing rate relations for single multivibrator neurons as well as the equilibrium firing rate of large recurrent netwo...

Multiplierless Implementation of Noisy Izhikevich Neuron with Low Cost Digital Design.

Fast speed and high accuracy implementation of biological plausible neural networks are vital key objectives to achieve new solutions to model, simulate and cure the brain diseases. Efficient hardware implementation of Spiking Neural Networks (SNN) is a significant approach in biological neural networks. This paper presents a Multiplierless Noisy Izhikevich Neuron (MNIN) model, which is used for digital implementation of biological neural networks in large scale. Simulation results show that the MNIN model ...

Stability Analysis for Neural Networks With Time-Varying Delay via Improved Techniques.

This paper is concerned with the stability problem for neural networks with a time-varying delay. First, an improved generalized free-weighting-matrix integral inequality is proposed, which encompasses the conventional one as a special case. Second, an improved Lyapunov-Krasovskii functional is constructed that contains two complement triple-integral functionals. Third, based on the improved techniques, a new stability condition is derived for neural networks with a time-varying delay. Finally, two widely u...


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