AI Medical Compendium Topic:
Nonlinear Dynamics

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Nonlinear System Identification Based on Convolutional Neural Networks for Multiple Drug Interactions.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In heart failure patients, hemodynamics can be regulated by therapeutic drugs. Although the cardiovascular responses to these drugs usually include nonlinearity and drug interactions, it is difficult to identify the characteristics of the dynamics un...

Action-Driven Visual Object Tracking With Deep Reinforcement Learning.

IEEE transactions on neural networks and learning systems
In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control track...

A novel step-by-step optimization method for interplant water networks.

Journal of environmental management
This paper evaluated the characteristics of interplant water networks along with superstructure models of such networks coordinated with intermediate pools, with the latter being assessed via nonlinear programming tools. To overcome the inherent diff...

Dynamics of coupled mode solitons in bursting neural networks.

Physical review. E
Using an electrically coupled chain of Hindmarsh-Rose neural models, we analytically derived the nonlinearly coupled complex Ginzburg-Landau equations. This is realized by superimposing the lower and upper cutoff modes of wave propagation and by empl...

Data-driven advice for applying machine learning to bioinformatics problems.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classific...

Adaptive Neural Network Control of a Flapping Wing Micro Aerial Vehicle With Disturbance Observer.

IEEE transactions on cybernetics
The research of this paper works out the attitude and position control of the flapping wing micro aerial vehicle (FWMAV). Neural network control with full state and output feedback are designed to deal with uncertainties in this complex nonlinear FWM...

Daily runoff prediction using the linear and non-linear models.

Water science and technology : a journal of the International Association on Water Pollution Research
Runoff prediction, as a nonlinear and complex process, is essential for designing canals, water management and planning, flood control and predicting soil erosion. There are a number of techniques for runoff prediction based on the hydro-meteorologic...

Adaptive control of an exoskeleton robot with uncertainties on kinematics and dynamics.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
In this paper, we propose a new adaptive control technique based on nonlinear sliding mode control (JSTDE) taking into account kinematics and dynamics uncertainties. This approach is applied to an exoskeleton robot with uncertain kinematics and dynam...

Nonlinear disturbance observer based sliding mode control of a cable-driven rehabilitation robot.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
This paper introduces a cable-driven robot for upper-limb rehabilitation. Kinematic and dynamic of this rehabilitation robot is analyzed. A sliding mode controller combined with a nonlinear disturbance observer is proposed to control this robot in th...

Gene essentiality prediction based on fractal features and machine learning.

Molecular bioSystems
Essential genes are required for the viability of an organism. Accurate and rapid identification of new essential genes is of substantial theoretical interest to synthetic biology and has practical applications in biomedicine. Fractals provide facili...