an investigation of radial basis function approximation methods with application in dynamic investment model

Integrated multiquadric radial basis function approximation methods

Promising numerical results using once and twice integrated radial basis functions have been recently presented. In this work we investigate the integrated radial basis function (IRBF) concept in greater detail, connect to the existing RBF theory, and make conjectures about the properties of IRBF approximation methods. The IRBF methods are used to solve PDEs. c © 2006 Elsevier Science Ltd. All ...

an application of equilibrium model for crude oil tanker ships insurance futures in iran

با توجه به تحریم های بین المملی علیه صنعت بیمه ایران امکان استفاده از بازارهای بین المملی بیمه ای برای نفتکش های ایرانی وجود ندارد. از طرفی از آنجایی که یکی از نوآوری های اخیر استفاده از بازارهای مالی به منظور ریسک های فاجعه آمیز می باشد. از اینرو در این پایان نامه سعی شده است با استفاده از این نوآوری ها با طراحی اوراق اختیارات راهی نو جهت بیمه گردن نفت کش های ایرانی ارائه نمود. از آنجایی که بر...

APPROXIMATION BY RADIAL BASIS FUNCTION NETWORKS Application to Option Pricing

We propose a method of function approximation by radial basis function networks. We will demonstrate that this approximation method can be improved by a pre-treatment of data based on a linear model. This approximation method will be applied to option pricing. This choice justifies itself through the known nonlinear nature of the behavior of options price and through the effective contribution ...

Approximation of a Fuzzy Function by Using Radial Basis Functions Interpolation

In the present paper, Radial Basis Function interpolations are applied to approximate a fuzzy function $tilde{f}:Rrightarrow mathcal{F}(R)$, on a discrete point set $X={x_1,x_2,ldots,x_n}$, by a fuzzy-valued function $tilde{S}$. RBFs are based on linear combinations of terms which include a single univariate function. Applying RBF to approximate a fuzzy function, a linear system wil...

Universal Approximation Using Radial-Basis-Function Networks

Approximation of nonlinear systems with radial basis function neural networks

A technique for approximating a continuous function of n variables with a radial basis function (RBF) neural network is presented. The method uses an n-dimensional raised-cosine type of RBF that is smooth, yet has compact support. The RBF network coefficients are low-order polynomial functions of the input. A simple computational procedure is presented which significantly reduces the network tr...