AIMC Topic: Time Factors

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

Neural networks : the official journal of the International Neural Network Society
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 t...

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

Neural networks : the official journal of the International Neural Network Society
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 independen...

Finite-time synchronization of stochastic coupled neural networks subject to Markovian switching and input saturation.

Neural networks : the official journal of the International Neural Network Society
This paper addresses the problem of finite-time synchronization of stochastic coupled neural networks (SCNNs) subject to Markovian switching, mixed time delay, and actuator saturation. In addition, coupling strengths of the SCNNs are characterized by...

Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis.

Neuron
Perceptions, thoughts, and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it rema...

Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy.

Breast cancer research and treatment
BACKGROUND: Prevention of persistent pain following breast cancer surgery, via early identification of patients at high risk, is a clinical need. Supervised machine-learning was used to identify parameters that predict persistence of significant pain...

A Discrete-Time Projection Neural Network for Sparse Signal Reconstruction With Application to Face Recognition.

IEEE transactions on neural networks and learning systems
This paper deals with sparse signal reconstruction by designing a discrete-time projection neural network. Sparse signal reconstruction can be converted into an L -minimization problem, which can also be changed into the unconstrained basis pursuit d...

An Improved Multispectral Palmprint Recognition System Using Autoencoder with Regularized Extreme Learning Machine.

Computational intelligence and neuroscience
Multispectral palmprint recognition system (MPRS) is an essential technology for effective human identification and verification tasks. To improve the accuracy and performance of MPRS, a novel approach based on autoencoder (AE) and regularized extrem...

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.

PloS one
Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of ...

Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To train and evaluate a very deep dilated residual network (DD-ResNet) for fast and consistent auto-segmentation of the clinical target volume (CTV) for breast cancer (BC) radiotherapy with big data.