AIMC Topic: Time Factors

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Event-triggered control for robust exponential synchronization of inertial memristive neural networks under parameter disturbance.

Neural networks : the official journal of the International Neural Network Society
Synchronization of memristive neural networks (MNNs) by using network control scheme has been widely and deeply studied. However, these researches are usually restricted to traditional continuous-time control methods for synchronization of the first-...

A novel time series prediction method based on pooling compressed sensing echo state network and its application in stock market.

Neural networks : the official journal of the International Neural Network Society
In the prediction of time series, the echo state network (ESN) exhibits exclusive strengths and a unique training structure. Based on ESN model, a pooling activation algorithm consisting noise value and adjusted pooling algorithm is proposed to enric...

Learning long-term motor timing/patterns on an orthogonal basis in random neural networks.

Neural networks : the official journal of the International Neural Network Society
The ability of the brain to generate complex spatiotemporal patterns with specific timings is essential for motor learning and temporal processing. An approach that can model this function, using the spontaneous activity of a random neural network (R...

A Prediction Model Based on Gated Nonlinear Spiking Neural Systems.

International journal of neural systems
Nonlinear spiking neural P (NSNP) systems are one of neural-like membrane computing models, abstracted by nonlinear spiking mechanisms of biological neurons. NSNP systems have a nonlinear structure and can show rich nonlinear dynamics. In this paper,...

Resilient fixed-time stabilization of switched neural networks subjected to impulsive deception attacks.

Neural networks : the official journal of the International Neural Network Society
This article focuses on the resilient fixed-time stabilization of switched neural networks (SNNs) under impulsive deception attacks. A novel theorem for the fixed-time stability of impulsive systems is established by virtue of the comparison principl...

Fixed-time synchronization of delayed memristive neural networks with impulsive effects via novel fixed-time stability theorem.

Neural networks : the official journal of the International Neural Network Society
In this study, the fixed-time synchronization (FXTS) of delayed memristive neural networks (MNNs) with hybrid impulsive effects is explored. To investigate the FXTS mechanism, we first propose a novel theorem about the fixed-time stability (FTS) of i...

Fixed/prescribed-time synchronization of BAM memristive neural networks with time-varying delays via convex analysis.

Neural networks : the official journal of the International Neural Network Society
The synchronization problem of bidirectional associative memory memristive neural networks (BAMMNNs) with time-varying delays plays an essential role in the implementation and application of neural networks. Firstly, under the framework of the Filipp...

Adaptive fixed-time output synchronization for complex dynamical networks with multi-weights.

Neural networks : the official journal of the International Neural Network Society
This paper addresses fixed-time output synchronization problems for two types of complex dynamical networks with multi-weights (CDNMWs) by using two types of adaptive control methods. Firstly, complex dynamical networks with multiple state and output...

EBi-LSTM: an enhanced bi-directional LSTM for time-series data classification by heuristic development of optimal feature integration in brain computer interface.

Computer methods in biomechanics and biomedical engineering
Generally, time series data is referred to as the sequential representation of data that observes from different applications. Therefore, such expertise can use Electroencephalography (EEG) signals to fetch data regarding brain neural activities in b...

Deep Learning Prediction for Distal Aortic Remodeling After Thoracic Endovascular Aortic Repair in Stanford Type B Aortic Dissection.

Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists
PURPOSE: This study aimed to develop a deep learning model for predicting distal aortic remodeling after proximal thoracic endovascular aortic repair (TEVAR) in patients with Stanford type B aortic dissection (TBAD) using computed tomography angiogra...