Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein...
Neural networks : the official journal of the International Neural Network Society
Jun 25, 2018
This paper investigates the distributed differential game tracking problem for nonlinear multi-agent systems with output constraint under a fixed directed graph. Each follower can be taken as strict-feedback structure with uncertain nonlinearities an...
Neural networks : the official journal of the International Neural Network Society
Jun 1, 2018
This study addresses the issue of the adaptive output tracking control for a category of uncertain nonstrict-feedback delayed incommensurate fractional-order systems in the presence of nonaffine structures, unmeasured pseudo-states, unknown control d...
BACKGROUND: Identifying patients with early stages of Parkinson's disease (PD) in a home environment is an important area of neurological disorder research, because it is of therapeutic and economic benefits to optimal intervention and management of ...
Neural networks : the official journal of the International Neural Network Society
May 26, 2018
In this paper, we develop a novel optimal control strategy for a class of uncertain nonlinear systems with unmatched interconnections. To begin with, we present a stabilizing feedback controller for the interconnected nonlinear systems by modifying a...
IEEE transactions on neural networks and learning systems
May 10, 2018
This paper is concerned with the adaptive event-triggered control problem for a class of pure-feedback nonlinear systems. Unlike the existing results where the control execution is periodic, the new proposed scheme updates the controller and the neur...
IEEE transactions on neural networks and learning systems
Apr 19, 2018
This paper investigates the dynamical multisynchronization and static multisynchronization problem for delayed coupled multistable neural networks with fixed and switching topologies. To begin with, a class of activation functions as well as several ...
We propose a neural network model for reinforcement learning to control a robotic manipulator with unknown parameters and dead zones. The model is composed of three networks. The state of the robotic manipulator is predicted by the state network of t...
IEEE transactions on neural networks and learning systems
Apr 12, 2018
Practical time-varying formation tracking problems for second-order nonlinear multiagent systems with multiple leaders are investigated using adaptive neural networks (NNs), where the time-varying formation tracking error caused by time-varying exter...
Neural networks : the official journal of the International Neural Network Society
Apr 12, 2018
Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more co...