AIMC Topic: Nonlinear Dynamics

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Differential-game for resource aware approximate optimal control of large-scale nonlinear systems with multiple players.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose a novel differential-game based neural network (NN) control architecture to solve an optimal control problem for a class of large-scale nonlinear systems involving N-players. We focus on optimizing the usage of the computati...

Constructing a Consciousness Meter Based on the Combination of Non-Linear Measurements and Genetic Algorithm-Based Support Vector Machine.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
OBJECTIVE: Constructing a framework to evaluate consciousness is an important issue in neuroscience research and clinical practice. However, there is still no systematic framework for quantifying altered consciousness along the dimensions of both lev...

A causal discovery algorithm based on the prior selection of leaf nodes.

Neural networks : the official journal of the International Neural Network Society
In recent years, Linear Non-Gaussian Acyclic Model (LiNGAM) has been widely used for the discovery of causal network. However, solutions based on LiNGAM usually yield high computational complexity as well as unsatisfied accuracy when the data is high...

Ghost hunting in the nonlinear dynamic machine.

PloS one
Integrating dynamic systems modeling and machine learning generates an exploratory nonlinear solution for analyzing dynamical systems-based data. Applying dynamical systems theory to the machine learning solution further provides a pathway to interpr...

A novel machine learning based computational framework for homogenization of heterogeneous soft materials: application to liver tissue.

Biomechanics and modeling in mechanobiology
Real-time simulation of organs increases comfort and safety for patients during the surgery. Proper generalized decomposition (PGD) is an efficient numerical method with coordinate errors below 1 mm and response time below 0.1 s that can be used for ...

Fuzzy jump wavelet neural network based on rule induction for dynamic nonlinear system identification with real data applications.

PloS one
AIM: Fuzzy wavelet neural network (FWNN) has proven to be a promising strategy in the identification of nonlinear systems. The network considers both global and local properties, deals with imprecision present in sensory data, leading to desired prec...

Splice sites detection using chaos game representation and neural network.

Genomics
A novel method is proposed to detect the acceptor and donor splice sites using chaos game representation and artificial neural network. In order to achieve high accuracy, inputs to the neural network, or feature vector, shall reflect the true nature ...

An improved result on synchronization control for memristive neural networks with inertial terms and reaction-diffusion items.

ISA transactions
This paper investigates the synchronization issue of the memristive neural networks (MNNs) with inertial terms and reaction-diffusion items. In order to smoothly derive the controller gains and obtain an excellent control effect, the desired controll...

Hash Transformation and Machine Learning-Based Decision-Making Classifier Improved the Accuracy Rate of Automated Parkinson's Disease Screening.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Digitalized hand-drawn pattern is a noninvasive and reproducible assistive manner to obtain hand actions and motions for evaluating functional tremors and upper-limb movement disorders. In this study, spirals and straight lines in polar coordinates a...

A sparse deep belief network with efficient fuzzy learning framework.

Neural networks : the official journal of the International Neural Network Society
Deep belief network (DBN) is one of the most feasible ways to realize deep learning (DL) technique, and it has been attracting more and more attentions in nonlinear system modeling. However, DBN cannot provide satisfactory results in learning speed, ...