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Nonlinear Dynamics

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Multi-level feature interaction image super-resolution network based on convolutional nonlinear spiking neural model.

Neural networks : the official journal of the International Neural Network Society
Image super-resolution (ISR) is designed to recover lost detail information from low-resolution images, resulting in high-quality and high-definition high-resolution images. In the existing single ISR (SISR) methods based on convolutional neural netw...

Neural critic learning with accelerated value iteration for nonlinear model predictive control.

Neural networks : the official journal of the International Neural Network Society
In practical industrial processes, the receding optimization solution of nonlinear model predictive control (NMPC) is always a very knotty problem. Based on adaptive dynamic programming, the accelerated value iteration predictive control (AVI-PC) alg...

LordNet: An efficient neural network for learning to solve parametric partial differential equations without simulated data.

Neural networks : the official journal of the International Neural Network Society
Neural operators, as a powerful approximation to the non-linear operators between infinite-dimensional function spaces, have proved to be promising in accelerating the solution of partial differential equations (PDE). However, it requires a large amo...

Modelling for disability: How does artificial intelligence affect unemployment among people with disability? An empirical analysis of linear and nonlinear effects.

Research in developmental disabilities
There is a growing debate among scholars regarding the impact of artificial intelligence (AI) on the employment opportunities and professional development of people with disability. Although there has been an increasing body of empirical research on ...

Multitask Adversarial Networks Based on Extensive Nonlinear Spiking Neuron Models.

International journal of neural systems
Deep learning technology has been successfully used in Chest X-ray (CXR) images of COVID-19 patients. However, due to the characteristics of COVID-19 pneumonia and X-ray imaging, the deep learning methods still face many challenges, such as lower ima...

Estimation of instantaneous peak flows in Canadian rivers: an evaluation of conventional, nonlinear regression, and machine learning methods.

Water science and technology : a journal of the International Association on Water Pollution Research
Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Comp...

Learning spiking neuronal networks with artificial neural networks: neural oscillations.

Journal of mathematical biology
First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be hard to build and expensive to simulate for complex living systems. On ...

Mixing neural networks, continuation and symbolic computation to solve parametric systems of non linear equations.

Neural networks : the official journal of the International Neural Network Society
We consider a square non linear parametric equations system F(P,X) = 0 which is constituted of n non differential equations in the n unknowns {x,…,x} that are the components of X while P={p,…,p} is a set of m parameters that play a role in the defini...

Physics-informed neural wavefields with Gabor basis functions.

Neural networks : the official journal of the International Neural Network Society
Recently, Physics-Informed Neural Networks (PINNs) have gained significant attention for their versatile interpolation capabilities in solving partial differential equations (PDEs). Despite their potential, the training can be computationally demandi...

Observer-based resilient dissipativity control for discrete-time memristor-based neural networks with unbounded or bounded time-varying delays.

Neural networks : the official journal of the International Neural Network Society
This work focuses on the issue of observer-based resilient dissipativity control of discrete-time memristor-based neural networks (DTMBNNs) with unbounded or bounded time-varying delays. Firstly, the Luenberger observer is designed, and additionally ...