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.

In this paper, application of polynomial kernel functions in locally linear neurofuzzy models is shown. Using polynomial kernels in local models, better local approximations in prediction of chaotic ...

which is formed by a polynomial fuzzy model and a polynomial fuzzy controller connected in a closed loop. A switching polynomial Lyapunov function consisting of a number of local polynomial Lyapunov ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...