The main purpose of our investigation is to propose two models of the DSWIM under static eccentricity by using two ... of the air-gap function. The second one gives a novel expression of the air-gap ...

Polynomials and power functions are the foundation for modelling non-linear relationships. Polynomial functions such as quadratic, cubic and quartic model variables raised to exponents of different ...

Additionally, it can use a single predictor variable and still produce a flexible model. However, polynomial regression can also overfit the data if the degree of the polynomial is too high ...

The problem is to learn either the generative model μ or a ... where the function 1. The kernels are well-defined on the entire Euclidean space and their theoretical properties are well-investigated.

estimation for nonlinear systems represented by polynomial fuzzy models via piecewise polynomial Lyapunov functions (PPLFs). At first, two SOS-based global stability criteria are proposed by applying ...

Linear models and algorithms ... For example, you can use polynomial features, logarithmic functions, exponential functions, or trigonometric functions to create new features that can better ...

In [1] a new model ... Polynomial Chaos (PC)/MC methods were used to compute uncertainty bounds. The effect of the parametric uncertainty is investigated at the end. In the current work a robust ...

This repository contains a TensorFlow/Keras implementation of a dual polynomial model that shares a set of coefficients. The model is intended to be used with two datasets, each represented by a ...

Using piecewise discontinuous layers the model can match the function exactly. With piecewise continuous. With polynomial using similar number of parameters. Using 2D polynomial "layer" this is just a ...