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Nonlinear Dynamics

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Deterministic learning-based neural identification and knowledge fusion.

Neural networks : the official journal of the International Neural Network Society
Recent deterministic learning methods have achieved locally-accurate identification of unknown system dynamics. However, the locally-accurate identification means that the neural networks can only capture the local dynamics knowledge along the system...

Synergetic learning for unknown nonlinear H control using neural networks.

Neural networks : the official journal of the International Neural Network Society
The well-known H control design gives robustness to a controller by rejecting perturbations from the external environment, which is difficult to do for completely unknown affine nonlinear systems. Accordingly, the immediate objective of this paper is...

Adaptive optimal control of affine nonlinear systems via identifier-critic neural network approximation with relaxed PE conditions.

Neural networks : the official journal of the International Neural Network Society
This paper considers an optimal control of an affine nonlinear system with unknown system dynamics. A new identifier-critic framework is proposed to solve the optimal control problem. Firstly, a neural network identifier is built to estimate the unkn...

Adaptive dynamic programming-based hierarchical decision-making of non-affine systems.

Neural networks : the official journal of the International Neural Network Society
In this paper, the problem of multiplayer hierarchical decision-making problem for non-affine systems is solved by adaptive dynamic programming. Firstly, the control dynamics are obtained according to the theory of dynamic feedback and combined with ...

An unsupervised wavelet neural network model for approximating the solutions of non-linear nervous stomach model governed by tension, food and medicine.

Computer methods in biomechanics and biomedical engineering
The human stomach is a complex organ. Its role is to degrade food particles by using mechanical forces and chemical reactions in order to release nutrients. All ingested items, including our nutrition, should first pass through the stomach, making it...

The deep arbitrary polynomial chaos neural network or how Deep Artificial Neural Networks could benefit from data-driven homogeneous chaos theory.

Neural networks : the official journal of the International Neural Network Society
Artificial Intelligence and Machine learning have been widely used in various fields of mathematical computing, physical modeling, computational science, communication science, and stochastic analysis. Approaches based on Deep Artificial Neural Netwo...

Hybridization of the swarming and interior point algorithms to solve the Rabinovich-Fabrikant system.

Scientific reports
In this study, a trustworthy swarming computing procedure is demonstrated for solving the nonlinear dynamics of the Rabinovich-Fabrikant system. The nonlinear system's dynamic depends upon the three differential equations. The computational stochasti...

Neural-Network-Based Adaptive Control of Uncertain MIMO Singularly Perturbed Systems With Full-State Constraints.

IEEE transactions on neural networks and learning systems
This article investigates the tracking control problem for a class of nonlinear multi-input-multi-output (MIMO) uncertain singularly perturbed systems (SPSs) with full-state constraints. The underlying issues become more challenging because two-time-...

Data-Driven H Optimal Output Feedback Control for Linear Discrete-Time Systems Based on Off-Policy Q-Learning.

IEEE transactions on neural networks and learning systems
This article develops two novel output feedback (OPFB) Q -learning algorithms, on-policy Q -learning and off-policy Q -learning, to solve H static OPFB control problem of linear discrete-time (DT) systems. The primary contribution of the proposed alg...

Local Stability and Convergence Analysis of Neural Network Controllers With Error Integral Inputs.

IEEE transactions on neural networks and learning systems
This article investigates the local stability and local convergence of a class of neural network (NN) controllers with error integrals as inputs for reference tracking. It is formally proved that if the input of the NN controller consists exclusively...