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Nonlinear Dynamics

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Stable isotope and trace element analyses with non-linear machine-learning data analysis improved coffee origin classification and marker selection.

Journal of the science of food and agriculture
BACKGROUND: This study investigated the geographical origin classification of green coffee beans from continental to country and regional levels. An innovative approach combined stable isotope and trace element analyses with non-linear machine learni...

Improved disturbance observer-based fixed-time adaptive neural network consensus tracking for nonlinear multi-agent systems.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with the problem of fixed-time consensus tracking for a class of nonlinear multi-agent systems subject to unknown disturbances. Firstly, a modified fixed-time disturbance observer is devised to estimate the unknown mismatched ...

Nonlinear function-on-scalar regression via functional universal approximation.

Biometrics
We consider general nonlinear function-on-scalar (FOS) regression models, where the functional response depends on multiple scalar predictors in a general unknown nonlinear form. Existing methods either assume specific model forms (e.g., additive mod...

Neural-Network-Based Immune Optimization Regulation Using Adaptive Dynamic Programming.

IEEE transactions on cybernetics
This article investigates optimal regulation scheme between tumor and immune cells based on the adaptive dynamic programming (ADP) approach. The therapeutic goal is to inhibit the growth of tumor cells to allowable injury degree and maximize the numb...

A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge.

Scientific reports
Discovering a meaningful symbolic expression that explains experimental data is a fundamental challenge in many scientific fields. We present a novel, open-source computational framework called Scientist-Machine Equation Detector (SciMED), which inte...

A Neural Learning Approach for a Data-Driven Nonlinear Error Correction Model.

Computational intelligence and neuroscience
A nonlinear error correction model (ECM) is developed to fit nonlinear relationships between the nonstationary time series in a cointegration relationship. Different from the previous parametric methods, this paper constructs a hybrid neural network ...

A novel optimization method for belief rule base expert system with activation rate.

Scientific reports
Although the belief rule base (BRB) expert system has many advantages, such as the effective use of semi-quantitative information, objective description of uncertainty, and efficient nonlinear modeling capability, it is always limited by the problem ...

Hybrid fuzzy inference rules of descent method and wavelet function for volatility forecasting.

PloS one
This research employs the gradient descent learning (FIR.DM) approach as a learning process in a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) to improve volatility prediction of daily stock market prices using Sa...

Quantitative measurement of blood glucose influenced by multiple factors via photoacoustic technique combined with optimized wavelet neural networks.

Journal of biophotonics
In this work, the photoacoustic (PA) quantitative measurement of blood glucose concentration (BGC) influenced by multiple factors was firstly investigated. A set of PA detection system of blood glucose considering the comprehensive influence of five ...

Deep Neural Network-Embedded Stochastic Nonlinear State-Space Models and Their Applications to Process Monitoring.

IEEE transactions on neural networks and learning systems
Process complexities are characterized by strong nonlinearities, dynamics, and uncertainties. Monitoring such a complex process requires a high-quality model describing the corresponding nonlinear dynamic behavior. The proposed model is constructed u...