We present a survey on the application of fluid approximations, in the form of mean-field models, to the design of control strategies in swarm robotics. Mean-field models that consist of ordinary differential equations, partial differential equations...
For many biological applications, exploration of the massive parametric space of a mechanism-based model can impose a prohibitive computational demand. To overcome this limitation, we present a framework to improve computational efficiency by orders ...
Neural networks : the official journal of the International Neural Network Society
Sep 7, 2019
This paper studies the synchronous problem of Markovian switching complex networks associated with partly unknown transitional rates, stochastic noise, and randomly coupling strength. In order to achieve the synchronization for these array networks, ...
Research investigating the dynamics of coupled physical systems has demonstrated that small feedback delays can allow a dynamic response system to anticipate chaotic behavior. This counterintuitive phenomenon, termed anticipatory synchronization, has...
In this paper, to investigate the exponential synchronization of stochastic neural networks, a new periodically intermittent discrete observation control (PIDOC) is first proposed. Different from the existing periodically intermittent control, our co...
BACKGROUND: Despite the greater sensitivity of the new dengue clinical classification proposed by the World Health Organization (WHO) in 2009, there is a need for a better definition of warning signs and clinical progression of dengue cases. Classic ...
Neural networks : the official journal of the International Neural Network Society
Jul 15, 2019
Accounting for the morphology of shale formations, which represent highly heterogeneous porous media, is a difficult problem. Although two- or three-dimensional images of such formations may be obtained and analyzed, they either do not capture the na...
A restricted Boltzmann machine (RBM) is an unsupervised machine learning bipartite graphical model that jointly learns a probability distribution over data and extracts their relevant statistical features. RBMs were recently proposed for characterizi...
Neural networks : the official journal of the International Neural Network Society
May 20, 2019
Stochastic computing (SC) is a promising computing paradigm that can help address both the uncertainties of future process technology and the challenges of efficient hardware realization for deep neural networks (DNNs). However the impreciseness and ...
Computer methods and programs in biomedicine
Apr 17, 2019
BACKGROUND AND OBJECTIVES: With rapid development of telehealth system and cloud platform, traditional 12-ECG signals with high resolution generate heavy burdens in data storage and transmission. This problem is increasingly addressed with various EC...