AI Medical Compendium Topic:
Nonlinear Dynamics

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Output-feedback adaptive neural control for stochastic nonlinear time-varying delay systems with unknown control directions.

IEEE transactions on neural networks and learning systems
This paper presents an adaptive output-feedback neural network (NN) control scheme for a class of stochastic nonlinear time-varying delay systems with unknown control directions. To make the controller design feasible, the unknown control coefficient...

A two-layer recurrent neural network for nonsmooth convex optimization problems.

IEEE transactions on neural networks and learning systems
In this paper, a two-layer recurrent neural network is proposed to solve the nonsmooth convex optimization problem subject to convex inequality and linear equality constraints. Compared with existing neural network models, the proposed neural network...

Adaptive neural control of nonlinear MIMO systems with time-varying output constraints.

IEEE transactions on neural networks and learning systems
In this paper, adaptive neural control is investigated for a class of unknown multiple-input multiple-output nonlinear systems with time-varying asymmetric output constraints. To ensure constraint satisfaction, we employ a system transformation techn...

Adaptive neural control for dual-arm coordination of humanoid robot with unknown nonlinearities in output mechanism.

IEEE transactions on cybernetics
To achieve an excellent dual-arm coordination of the humanoid robot, it is essential to deal with the nonlinearities existing in the system dynamics. The literatures so far on the humanoid robot control have a common assumption that the problem of ou...

Hodge-Kodaira decomposition of evolving neural networks.

Neural networks : the official journal of the International Neural Network Society
Although it is very important to scrutinize recurrent structures of neural networks for elucidating brain functions, conventional methods often have difficulty in characterizing global loops within a network systematically. Here we applied the Hodge-...

Optimizing functional brain network analysis by incorporating nonlinear factors and frequency band selection with machine learning models.

Medicine
The accurate assessment of the brain's functional network is seen as crucial for the understanding of complex relationships between different brain regions. Hidden information within different frequency bands, which is often overlooked by traditional...

Intelligent Control to Suppress Epileptic Seizures in the Amygdala: In Silico Investigation Using a Network of Izhikevich Neurons.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Closed-loop electricalstimulation of brain structures is one of the most promising techniques to suppress epileptic seizures in drug-resistant refractory patients who are also ineligible to ablative neurosurgery. In this work, an intelligent controll...

Toward Self-Propelled Microrobots: A Systems Chemistry that Induces Non-Linear Phenomena of Oil Droplets in Surfactant Solution.

Journal of oleo science
Biological activities observed in living systems occur as the output of which nanometer-, submicrometer-, and micrometer-sized structures and tissues non-linearly and dynamically behave through chemical reaction networks, including the generation of ...

CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution.

Briefings in bioinformatics
Predicting the strength of promoters and guiding their directed evolution is a crucial task in synthetic biology. This approach significantly reduces the experimental costs in conventional promoter engineering. Previous studies employing machine lear...

Using backward adjustment with model predictive control for adaptive control of nonlinear soft artificial muscle.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Soft artificial muscles possess inherent compliance and safety features, rendering them highly suitable for applications in wearable robots and unstructured environments. However, accurately modeling the nonlinearity of soft actuators proves to be a ...