AIMC Topic: Feedback

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Low precision decentralized distributed training over IID and non-IID data.

Neural networks : the official journal of the International Neural Network Society
Decentralized distributed learning is the key to enabling large-scale machine learning (training) on the edge devices utilizing private user-generated local data, without relying on the cloud. However, practical realization of such on-device training...

Augmented tactile-perception and haptic-feedback rings as human-machine interfaces aiming for immersive interactions.

Nature communications
Advancements of virtual reality technology pave the way for developing wearable devices to enable somatosensory sensation, which can bring more comprehensive perception and feedback in the metaverse-based virtual society. Here, we propose augmented t...

A Nonlinear Finite-Time Robust Differential Game Guidance Law.

Sensors (Basel, Switzerland)
In this paper, a robust differential game guidance law is proposed for the nonlinear zero-sum system with unknown dynamics and external disturbances. First, the continuous-time nonlinear zero-sum differential game problem is transformed into solving ...

Online Optimal Adaptive Control of Partially Uncertain Nonlinear Discrete-Time Systems Using Multilayer Neural Networks.

IEEE transactions on neural networks and learning systems
This article intends to address an online optimal adaptive regulation of nonlinear discrete-time systems in affine form and with partially uncertain dynamics using a multilayer neural network (MNN). The actor-critic framework estimates both the optim...

Distributed Intelligent Learning and Decision Model Based on Logic Predictive Control.

Computational intelligence and neuroscience
By the method of documentation and logical analysis, based on the data, based on logic and based on the knowledge of three kinds of artificial intelligence in the sports education, the intelligent learning system feedback delay are studied, combined ...

Comparison of Deep Learning and Deterministic Algorithms for Control Modeling.

Sensors (Basel, Switzerland)
Controlling nonlinear dynamics arises in various engineering fields. We present efforts to model the forced van der Pol system control using physics-informed neural networks (PINN) compared to benchmark methods, including idealized nonlinear feedforw...

Synergetic learning structure-based neuro-optimal fault tolerant control for unknown nonlinear systems.

Neural networks : the official journal of the International Neural Network Society
In this paper, a synergetic learning structure-based neuro-optimal fault tolerant control (SLSNOFTC) method is proposed for unknown nonlinear continuous-time systems with actuator failures. Under the framework of the synergetic learning structure (SL...

Fuzzy Multiple Hidden Layer Recurrent Neural Control of Nonlinear System Using Terminal Sliding-Mode Controller.

IEEE transactions on cybernetics
This study designs a fuzzy double hidden layer recurrent neural network (FDHLRNN) controller for a class of nonlinear systems using a terminal sliding-mode control (TSMC). The proposed FDHLRNN is a fully regulated network, which can be simply conside...

Feedback Pinning Control of Successive Lag Synchronization on a Dynamical Network.

IEEE transactions on cybernetics
In nature and human society, successive lag synchronization (SLS) is an important synchronization phenomenon. Compared with other synchronization patterns, the control theory of SLS is very lacking. To this end, we first introduce a complex dynamical...

Event-Triggered Adaptive Neural Control for Fractional-Order Nonlinear Systems Based on Finite-Time Scheme.

IEEE transactions on cybernetics
This article addresses the finite-time event-triggered adaptive neural control for fractional-order nonlinear systems. Based on the backstepping technique, a novel adaptive event-triggered control scheme is proposed, and finite-time stability criteri...