AIMC Topic: Feedback

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Aperiodic switching event-triggered stabilization of continuous memristive neural networks with interval delays.

Neural networks : the official journal of the International Neural Network Society
The stabilization problem is studied for memristive neural networks with interval delays under aperiodic switching event-triggered control. Note that, most of delayed memristive neural networks models studied are discontinuous, which are not the real...

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-...

Tree-structured neural networks: Spatiotemporal dynamics and optimal control.

Neural networks : the official journal of the International Neural Network Society
How the network topology drives the response dynamic is a basic question that has not yet been fully answered in neural networks. Elucidating the internal relation between topological structures and dynamics is instrumental in our understanding of br...

Optimal H tracking control of nonlinear systems with zero-equilibrium-free via novel adaptive critic designs.

Neural networks : the official journal of the International Neural Network Society
In this paper, a novel adaptive critic control method is designed to solve an optimal H tracking control problem for continuous nonlinear systems with nonzero equilibrium based on adaptive dynamic programming (ADP). To guarantee the finiteness of a c...

Dynamic event-triggered controller design for nonlinear systems: Reinforcement learning strategy.

Neural networks : the official journal of the International Neural Network Society
The current investigation aims at the optimal control problem for discrete-time nonstrict-feedback nonlinear systems by invoking the reinforcement learning-based backstepping technique and neural networks. The dynamic-event-triggered control strategy...

Meta-learning biologically plausible plasticity rules with random feedback pathways.

Nature communications
Backpropagation is widely used to train artificial neural networks, but its relationship to synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely on feedback projections that are symmetric with feedforward connec...

A Novel Master-Slave Interventional Surgery Robot with Force Feedback and Collaborative Operation.

Sensors (Basel, Switzerland)
In recent years, master-slave vascular robots have been developed to address the problem of radiation exposure during vascular interventions for surgeons. However, the single visual feedback reduces surgeon immersion and transparency of the system. I...

A Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform.

Sensors (Basel, Switzerland)
Artificial intelligence (AI) techniques for intelligent mobile computing in healthcare has opened up new opportunities in healthcare systems. Combining AI techniques with the existing Internet of Medical Things (IoMT) will enhance the quality of care...

Improved disturbance observer-based fixed-time adaptive neural network consensus tracking for nonlinear multi-agent systems.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with the problem of fixed-time consensus tracking for a class of nonlinear multi-agent systems subject to unknown disturbances. Firstly, a modified fixed-time disturbance observer is devised to estimate the unknown mismatched ...

A Deep-Learning Framework for Analysing Students' Review in Higher Education.

Computational intelligence and neuroscience
As part of continuous process improvements to teaching and learning, the management of tertiary institutions requests students to review modules towards the end of each semester. These reviews capture students' perceptions about various aspects of th...