IEEE transactions on neural networks and learning systems
Feb 4, 2021
This article studies the adaptive neural controller design for a class of uncertain multiagent systems described by ordinary differential equations (ODEs) and beams. Three kinds of agent models are considered in this study, i.e., beams, nonlinear ODE...
In recent years, machine learning methods have been applied to various prediction scenarios in time-series data. However, some processing procedures such as cross-validation (CV) that rearrange the order of the longitudinal data might ruin the serial...
IEEE transactions on neural networks and learning systems
Jan 4, 2021
This article presents new theoretical results on global exponential synchronization of nonlinear coupled delayed memristive neural networks with reaction-diffusion terms and Dirichlet boundary conditions. First, a state-dependent memristive neural ne...
Neural networks : the official journal of the International Neural Network Society
Dec 23, 2020
This paper is primarily concentrated on finite-time cluster synchronization of fractional-order complex-variable networks with nonlinear coupling by utilizing the non-decomposition method. Firstly, two control strategies are designed which are releva...
Neural networks : the official journal of the International Neural Network Society
Dec 14, 2020
Feature selection is central to contemporary high-dimensional data analysis. Group structure among features arises naturally in various scientific problems. Many methods have been proposed to incorporate the group structure information into feature s...
Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological systems from sparse experimental data. In the present work, BINNs are train...
IEEE transactions on neural networks and learning systems
Nov 30, 2020
The aim of this article is to investigate the trajectory tracking problem of systems with uncertain models and state restrictions using differential neural networks (DNNs). The adaptive control design considers the design of a nonparametric identifie...
Computational intelligence and neuroscience
Nov 21, 2020
Considering the limitation of machine and technology, we study the stability for nonlinear impulsive control system with some uncertainty factors, such as the bounded gain error and the parameter uncertainty. A new sufficient condition for this syste...
Neural networks : the official journal of the International Neural Network Society
Nov 11, 2020
In this paper, a local tracking control (LTC) scheme is developed via particle swarm optimized neural networks (PSONN) for unknown nonlinear interconnected systems. With the local input-output data, a local neural network identifier is constructed to...
Neural networks : the official journal of the International Neural Network Society
Nov 7, 2020
Echo State Networks (ESNs) are efficient recurrent neural networks (RNNs) which have been successfully applied to time series modeling tasks. However, ESNs are unable to capture the history information far from the current time step, since the echo s...