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About 10,000 papers for “Nonlinear model”

Nonlinear Marginal Model

Wiley series in probability and statistics · 2004

This chapter includes the following topics: Fixed Matrix of Random Effects Varied Matrix of Random Effects Three Types of Nonlinear Marginal Models Total Generalized Estimating Equations Approach Summary Points

Cited by 0

Nonlinear Conveyer Model

Ken HIRASE, Hidenori Kimura · Transactions of the Society of Instrument and Control Engineers · 1975

Cited by 1Open access

Nonlinear model reduction :

Kevin Carlberg · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2018

Abstract not provided.

Cited by 0Open access

Nonlinear ARMA model

Marek Šabata · Digital Repository (National Repository of Grey Literature) · 2015

The thesis regards theory of nonlinear ARMA models and its application on financial mar- kets data. First of all, we present general framework of time series modeling. Afterwards the theory of linear ARMA models is layed out, since it plays a key role in the theory of nonlinear models as well. The nonlinear models presented are threshold autoregressive model (TAR), autoregressive conditional heteroscedastic model (ARCH) and generalized autoregressive conditional heteroscedastic model (GARCH). For each model, we derive a method for esti- mating the model's parameters, asymptotic properties of the estimators and consequently confidence regions and intervals for testing hypotheses about the parameters. The theory is then applied on financial data, speficically on the data from Standard and Poor's 500 index (S&P500). All models are implemented in statistical software R. 1

Cited by 0

The nonlinear model.

Yao Li (154923), Lai-Sang Young (7026818) · Figshare · 2021

<p>A. Size-tuning curves. The 4 panels are, from left to right, size-tuning curves for 4E, 4I, 6E and 6I respectively. B. Activity maps for the entire model. Top row: Excitatory groups in L4, bottom row: E-groups in L6. Responses are to gratings with R = 0.4, 0.8, 1.5.</p>

Cited by 0Open access

Nonlinear Model Predictive Controller Design for Identified Nonlinear Parameter Varying Model

Jiangang Lu, Jie You, Qinmin Yang · TELKOMNIKA (Telecommunication Computing Electronics and Control) · 2012

In this paper, a novel nonlinear model predictive controller (MPC) is proposed based on an identified nonlinear parameter varying (NPV) model. First, an NPV model scheme is present for process identification, which is featured by its nonlinear hybrid Hammerstein model structure and varying model parameters. The hybrid Hammerstein model combines a normalized static artificial neural network with a linear transfer function to identify general nonlinear systems at each fixed working point. Meanwhile, a model interpolating philosophy is utilized to obtain the global model across the whole operation domain. The NPV model considers both the nonlinearity of transition dynamics due to the variation of the working-point and the nonlinear mapping from the input to the output at fixed working points. Moreover, under the new NPV framework, the control action is computed via a multistep linearization method aimed for nonlinear optimization problems. In the proposed scheme, only low cost tests are needed for system identification and the controller can achieve better output performance than MPC methods based on linear parameter varying (LPV) models. Numerical examples validate the effectiveness of the proposed approach.

Cited by 10Open access

GALACTIC NONLINEAR DYNAMIC MODEL

C. A. M. DE MELO, S. T. RESENDE · International Journal of Modern Physics A · 2009

We develop a model for spiral galaxies based on a nonlinear realization of the Newtonian dynamics starting from the momentum and mass conservations in the phase space. The radial solution exhibits a rotation curve in qualitative accordance with the observational data.

Cited by 1Open access

NonLinear Coupling Model B

Martinazzo, Rocco · Università degli Studi di Milano · 2024

Dissipative tunneing rates and correlation function details for a non-linear coupling model (Model B)

Cited by 0Open access

Linear-nonlinear model analysis.

Genadiy Vasserman (206694), Elad Schneidman (135930), Ronen Segev (178535) · Figshare · 2015

<p>(A) Schematic overview of the LN model. The red and blue stimuli were convolved with red () and blue () linear filters, respectively, and the product was passed through a static nonlinearity (), resulting in a prediction of the LN model for ganglion cell firing rate. (B-D) Testing the LN model for two colour contrast conditions. Left column: high red (24%) and low blue (12%) colour contrast. Right column: high blue (24%) and low red (12%) colour contrast. (B) Intensities of red and blue light stimuli (red and blue curves) used in the experiment. (C) LN model prediction for two example cells compared to the experimental data. Data was averaged over 20 trials (bin size 33 ms). The LN model output (magenta coloured line), constructed from a separate data set, follows the data (green line) closely (correlation coefficients were 0.72 and 0.75 for the Cell1 (upper panel) and 0.36 and 0.61 for Cell2 (lower panel) for high red and high blue contrasts respectively).</p>

Cited by 0Open access