AIMC Topic: Nonlinear Dynamics

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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-...

A Salient Object Detection Network Enhanced by Nonlinear Spiking Neural Systems and Transformer.

International journal of neural systems
Although a variety of deep learning-based methods have been introduced for Salient Object Detection (SOD) to RGB and Depth (RGB-D) images, existing approaches still encounter challenges, including inadequate cross-modal feature fusion, significant er...

Modeling Higher-Order Interactions in Sparse and Heavy-Tailed Neural Population Activity.

Neural computation
Neurons process sensory stimuli efficiently, showing sparse yet highly variable ensemble spiking activity involving structured higher-order interactions. Notably, while neural populations are mostly silent, they occasionally exhibit highly synchronou...

VKAD: A novel fault detection and isolation model for uncertainty-aware industrial processes.

Neural networks : the official journal of the International Neural Network Society
Fault detection and isolation (FDI) are essential for effective monitoring of industrial processes. Modern industrial processes involve dynamic systems characterized by complex, high-dimensional nonlinearities, posing significant challenges for accur...

The butterfly effect in neural networks: Unveiling hyperbolic chaos through parameter sensitivity.

Neural networks : the official journal of the International Neural Network Society
Neural networks often excel in short-horizon tasks, but their long-term reliability is less assured. We demonstrate that even a minimal architecture, trained on near-periodic data, can exhibit hyperbolic chaotic behavior after a small parameter pertu...

Fixed-time adaptive neural network compensation control for uncertain nonlinear systems.

Neural networks : the official journal of the International Neural Network Society
Uncertainties are the main obstacle to improving the control performance of nonlinear systems. To address this challenge, this paper proposes a fixed-time adaptive neural network compensation control method for a class of high-order nonlinear systems...

Neuroadaptive fixed-time fault-tolerant containment control of high-order MIMO Nonlinear multi-agent systems in affine strict-feedback form.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with the fixed-time containment control problem for high-order MIMO nonlinear multi-agent systems with external disturbances and actuator faults. First, in the backstepping framework, a neuroadaptive fixed-time containment con...