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Computer Simulation

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ADP-based fault-tolerant consensus control for multiagent systems with irregular state constraints.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the consensus control issue for nonlinear multiagent systems (MASs) subject to irregular state constraints and actuator faults using an adaptive dynamic programming (ADP) algorithm. Unlike the regular state constraints conside...

An adaptive testing item selection strategy via a deep reinforcement learning approach.

Behavior research methods
Computerized adaptive testing (CAT) aims to present items that statistically optimize the assessment process by considering the examinee's responses and estimated trait levels. Recent developments in reinforcement learning and deep neural networks pr...

Comparison of feed-forward control strategies for simplified vertical hopping model with intrinsic muscle properties.

Bioinspiration & biomimetics
To analyse walking, running or hopping motions, models with high degrees of freedom are usually used. However simple reductionist models are advantageous within certain limits. In a simple manner, the hopping motion is generally modelled by a spring-...

Bipartite secure synchronization criteria for coupled quaternion-valued neural networks with signed graph.

Neural networks : the official journal of the International Neural Network Society
This study explores the bipartite secure synchronization problem of coupled quaternion-valued neural networks (QVNNs), in which variable sampled communications and random deception attacks are considered. Firstly, by employing the signed graph theory...

Oscillations in an artificial neural network convert competing inputs into a temporal code.

PLoS computational biology
The field of computer vision has long drawn inspiration from neuroscientific studies of the human and non-human primate visual system. The development of convolutional neural networks (CNNs), for example, was informed by the properties of simple and ...

Deep brain stimulation and lag synchronization in a memristive two-neuron network.

Neural networks : the official journal of the International Neural Network Society
In the pursuit of potential treatments for neurological disorders and the alleviation of patient suffering, deep brain stimulation (DBS) has been utilized to intervene or investigate pathological neural activities. To explore the exact mechanism of h...

A multiscale distributed neural computing model database (NCMD) for neuromorphic architecture.

Neural networks : the official journal of the International Neural Network Society
Distributed neuromorphic architecture is a promising technique for on-chip processing of multiple tasks. Deploying the constructed model in a distributed neuromorphic system, however, remains time-consuming and challenging due to considerations such ...

Moving sampling physics-informed neural networks induced by moving mesh PDE.

Neural networks : the official journal of the International Neural Network Society
In this work, we propose an end-to-end adaptive sampling framework based on deep neural networks and the moving mesh method (MMPDE-Net), which can adaptively generate new sampling points by solving the moving mesh PDE. This model focuses on improving...

Multistability and fixed-time multisynchronization of switched neural networks with state-dependent switching rules.

Neural networks : the official journal of the International Neural Network Society
This paper presents theoretical results on the multistability and fixed-time synchronization of switched neural networks with multiple almost-periodic solutions and state-dependent switching rules. It is shown herein that the number, location, and st...

Computer-Simulated Virtual Image Datasets to Train Machine Learning Models for Non-Invasive Fish Detection in Recirculating Aquaculture.

Sensors (Basel, Switzerland)
Artificial Intelligence (AI) and Machine Learning (ML) can assist producers to better manage recirculating aquaculture systems (RASs). ML is a data-intensive process, and model performance primarily depends on the quality of training data. Relatively...