AIMC Topic: Nonlinear Dynamics

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Multivariate time series forecasting method based on nonlinear spiking neural P systems and non-subsampled shearlet transform.

Neural networks : the official journal of the International Neural Network Society May 7, 2022
Multivariate time series forecasting remains a challenging task because of its nonlinear, non-stationary, high-dimensional, and spatial-temporal characteristics, along with the dependence between variables. To address this limitation, we propose a no...
Neural Networks, Computer Time Factors Nonlinear Dynamics Forecasting
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Sample entropy based prescribed performance control for tailless aircraft.

ISA transactions May 3, 2022
This paper proposes a sample entropy (SampEn) based prescribed performance controller (SPPC) for the longitudinal control of a supersonic tailless aircraft subject to model uncertainty and nonlinearity. Considering that SampEn can evaluate the system...
Nonlinear Dynamics Neural Networks, Computer Feedback Computer Simulation Algorithms
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Asymptotic Tracking Control for Uncertain MIMO Systems: A Biologically Inspired ESN Approach.

IEEE transactions on neural networks and learning systems May 2, 2022
In this study, a biologically inspired echo state network (ESN)-based method is established for the asymptotic tracking control of a class of uncertain multi-input multi-output (MIMO) systems. By mimicking the characters of real biological systems, a...
Nonlinear Dynamics Neural Networks, Computer Uncertainty Feedback Computer Simulation
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Compound FAT-based prespecified performance learning control of robotic manipulators with actuator dynamics.

ISA transactions Apr 21, 2022
In the framework of the backstepping algorithm, this article proposes a new function approximation technique (FAT)-based compound learning control law for electrically-driven robotic manipulators with output constraint. The Fourier series expansion i...
Robotics Nonlinear Dynamics Algorithms Computer Simulation Neural Networks, Computer
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Event-triggered integral reinforcement learning for nonzero-sum games with asymmetric input saturation.

Neural networks : the official journal of the International Neural Network Society Apr 21, 2022
In this paper, an event-triggered integral reinforcement learning (IRL) algorithm is developed for the nonzero-sum game problem with asymmetric input saturation. First, for each player, a novel non-quadratic value function with a discount factor is d...
Neural Networks, Computer Feedback Computer Simulation Nonlinear Dynamics Algorithms
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Development of a Soft Sensor for Flow Estimation in Water Supply Systems Using Artificial Neural Networks.

Sensors (Basel, Switzerland) Apr 18, 2022
A water supply system is considered an essential service to the population as it is about providing an essential good for life. This system typically consists of several sensors, transducers, pumps, etc., and some of these elements have high costs an...
Neural Networks, Computer Transducers Nonlinear Dynamics Water Supply Water
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Distributed Adaptive Consensus of Nonlinear Heterogeneous Agents With Delayed and Sampled Neighbor Measurements.

IEEE transactions on cybernetics Apr 5, 2022
In this article, the adaptive output consensus problem of high-order nonlinear heterogeneous agents is addressed using only delayed, sampled neighbor output measurements. A class of auxiliary variables is introduced which are n -times differentiable ...
Nonlinear Dynamics Consensus Neural Networks, Computer Feedback Computer Simulation
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Dynamic Event-Triggering Neural Learning Control for Partially Unknown Nonlinear Systems.

IEEE transactions on cybernetics Apr 5, 2022
This article presents an event-sampled integral reinforcement learning algorithm for partially unknown nonlinear systems using a novel dynamic event-triggering strategy. This is a novel attempt to introduce the dynamic triggering into the adaptive le...
Nonlinear Dynamics Algorithms Communication Feedback Neural Networks, Computer
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Removing Feasibility Conditions on Adaptive Neural Tracking Control of Nonlinear Time-Delay Systems With Time-Varying Powers, Input, and Full-State Constraints.

IEEE transactions on cybernetics Apr 5, 2022
This article investigates the tracking control for input and full-state-constrained nonlinear time-delay systems with unknown time-varying powers, whose nonlinearities do not impose any growth assumption. By utilizing the auxiliary control signal and...
Neural Networks, Computer Research Design Nonlinear Dynamics Feasibility Studies Computer Simulation
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A data-driven approach to characterizing nonlinear elastic behavior of soft materials.

Journal of the mechanical behavior of biomedical materials Mar 25, 2022
The Autoprogressive (AutoP) method is a data-driven inverse method that leverages finite element analysis (FEA) and machine learning (ML) techniques to build constitutive relationships from measured force and displacement data. Previous applications ...
Finite Element Analysis Stress, Mechanical Agar Phantoms, Imaging Models, Biological Elasticity Neural Networks, Computer Nonlinear Dynamics
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