scholarly journals Polynomial Response Surface based on basis function selection by multitask optimization and ensemble modeling

2021 ◽  
Author(s):  
Yong Zhao ◽  
Siyu Ye ◽  
Xianqi Chen ◽  
Yufeng Xia ◽  
Xiaohu Zheng
Keyword(s):  
Basis Function ◽  
Information Exchange ◽  
Polynomial Regression ◽  
Information Criterion ◽  
Negative Influence ◽  
Engineering Problem ◽  
Basis Functions ◽  
Ensemble Modeling ◽  
Polynomial Response Surface ◽  
Function Selection

AbstractPolynomial Regression Surface (PRS) is a commonly used surrogate model for its simplicity, good interpretability, and computational efficiency. The performance of PRS is largely dependent on its basis functions. With limited samples, how to correctly select basis functions remains a challenging problem. To improve prediction accuracy, a PRS modeling approach based on multitask optimization and ensemble modeling (PRS-MOEM) is proposed for rational basis function selection with robustness. First, the training set is partitioned into multiple subsets by the cross validation method, and for each subset a sub-model is independently constructed by optimization. To effectively solve these multiple optimization tasks, an improved evolutionary algorithm with transfer migration is developed, which can enhance the optimization efficiency and robustness by useful information exchange between these similar optimization tasks. Second, a novel ensemble method is proposed to integrate the multiple sub-models into the final model. The significance of each basis function is scored according to the error estimation of the sub-models and the occurrence frequency of the basis functions in all the sub-models. Then the basis functions are ranked and selected based on the bias-corrected Akaike’s information criterion. PRS-MOEM can effectively mitigate the negative influence from the sub-models with large prediction error, and alleviate the uncertain impact resulting from the randomness of training subsets. Thus the basis function selection accuracy and robustness can be enhanced. Seven numerical examples and an engineering problem are utilized to test and verify the effectiveness of PRS-MOEM.

scholarly journals A comparison of subset selection and adaptive basis function construction for polynomial regression model building

2009 ◽  
Vol 38 (38) ◽  
pp. 187-197 ◽  
Author(s):  
Gints Jēkabsons ◽  
Jurijs Lavendels
Keyword(s):  
Regression Model ◽  
Basis Function ◽  
Model Building ◽  
Polynomial Regression ◽  
Subset Selection ◽  
Predictive Performance ◽  
Basis Functions ◽  
Multivariate Adaptive Regression Splines ◽  
Function Construction ◽  
Polynomial Regression Model

A comparison of subset selection and adaptive basis function construction for polynomial regression model buildingThe approach of subset selection in polynomial regression model building assumes that the chosen fixed full set of predefined basis functions contains a subset that is sufficient to describe the target relation sufficiently well. However, in most cases the necessary set of basis functions is not known and needs to be guessed - a potentially non-trivial (and long) trial and error process. In our previous research we considered an approach for polynomial regression model building which is different from the subset selection - letting the regression model building method itself construct the basis functions necessary for creating a model of arbitrary complexity without restricting oneself to the basis functions of a predefined full model. The approach is titled Adaptive Basis Function Construction (ABFC). In the present paper we compare the two approaches for polynomial regression model building - subset selection and ABFC - both theoretically and empirically in terms of their underlying principles, computational complexity, and predictive performance. Additionally in empirical evaluations the ABFC is compared also to two other well-known regression modelling methods - Locally Weighted Polynomials and Multivariate Adaptive Regression Splines.

A Meshless Method for Solving the Mathematical Model Associated the Leakage Problem in Gas Pipeline

2014 ◽  
Vol 986-987 ◽  
pp. 1418-1421
Author(s):  
Jun Shan Li
Keyword(s):  
Differential Equation ◽  
Mathematical Model ◽  
Partial Differential Equation ◽  
Inverse Problem ◽  
Basis Function ◽  
Meshless Method ◽  
Numerical Solutions ◽  
Basis Functions ◽  
Gas Pipeline ◽  
The Mathematical Model

In this paper, we propose a meshless method for solving the mathematical model concerning the leakage problem when the pressure is tested in the gas pipeline. The method of radial basis function (RBF) can be used for solving partial differential equation by writing the solution in the form of linear combination of radius basis functions, that is, when integrating the definite conditions, one can find the combination coefficients and then the numerical solution. The leak problem is a kind of inverse problem that is focused by many engineers or mathematical researchers. The strength of the leak can find easily by the additional conditions and the numerical solutions.

Discussion on Colour Design of Medical Product Based on Visual Negative Afterimage Theory

2010 ◽  
Vol 102-104 ◽  
pp. 335-338
Author(s):  
Xing Fu Xiong ◽  
Chen Xiong
Keyword(s):  
Information Exchange ◽  
Special Function ◽  
Negative Influence ◽  
Medical Product ◽  
Space Environment ◽  
Negative Effects ◽  
Future Design ◽  
Medical Environment ◽  
Human Orientation ◽  
Organically Combine

This paper mainly analyses and discusses the application and significance of the visual negative afterimage on the colour design of medical product based on the visual negative afterimage theory. The design for the colour of medical product based on the visual negative afterimage theory focuses more human orientation in aspect of the visual effect design. The paper also points out that the trend of future design is to organically combine the art, the therapy features and the humanity.The complementary colour equilibrium of the visual negative afterimage will play a more and more important role in the colour design of the medical product. In the modern medical environment, the colour of the medical product has a decisive effect on the information exchange between people and the space environment. Although the visual negative afterimage is common in our daily life, the colour is not taken into consideration in the colour design of the medical product, which will result in negative effects. The colour is a strong and extremely attractive expression tool. Therefore, when the colour is designed for the medical product, it should be applied reasonably. The application of colour must be based on the special function requirements and the use of different people, so as to show its special function and requirements, which not only avoids the negative influence caused by the visual negative afterimage, but also creates a more humanistic atmosphere for the medical environment.

scholarly journals Broadband Adaptive RCS Computation through Characteristic Basis Function Method

2014 ◽  
Vol 2014 ◽  
pp. 1-5
Author(s):  
Guohua Wang ◽  
Yufa Sun
Keyword(s):  
Cross Section ◽  
Basis Function ◽  
Function Method ◽  
High Accuracy ◽  
Wide Frequency Range ◽  
Singular Value ◽  
Basis Functions ◽  
Frequency Range ◽  
Arbitrary Frequency ◽  
Value Decomposition

A broadband radar cross section (RCS) calculation approach is proposed based on the characteristic basis function method (CBFM). In the proposed approach, the desired arbitrary frequency band is adaptively divided into multiple subband in consideration of the characteristic basis functions (CBFs) number, which can reduce the universal characteristic basis functions (UCBFs) numbers after singular value decomposition (SVD) procedure at lower subfrequency band. Then, the desired RCS data can be obtained by splicing the RCS data in each subfrequency band. Numerical results demonstrate that the proposed method achieve a high accuracy and efficiency over a wide frequency range.

scholarly journals Accurate radial basis functions technique for competitive and efficient solutions of non-linear Black-Scholes equations

2020 ◽  
Vol 20 (4) ◽  
pp. 60-83
Author(s):  
Vinícius Magalhães Pinto Marques ◽  
Gisele Tessari Santos ◽  
Mauri Fortes
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Efficient Solutions ◽  
Basis Functions ◽  
Adaptive Scheme ◽  
Call Options ◽  
Black Scholes ◽  
Non Linear ◽  
Radial Basis ◽  
Complex Models

ABSTRACTObjective: This article aims to solve the non-linear Black Scholes (BS) equation for European call options using Radial Basis Function (RBF) Multi-Quadratic (MQ) Method.Methodology / Approach: This work uses the MQ RBF method applied to the solution of two complex models of nonlinear BS equation for prices of European call options with modified volatility. Linear BS models are also solved to visualize the effects of modified volatility.  Additionally, an adaptive scheme is implemented in time based on the Runge-Kutta-Fehlberg (RKF) method.

scholarly journals A New Improved Penalty Avoiding Rational Policy Making Algorithm for Keepaway with Continuous State Spaces

2009 ◽  
Vol 13 (6) ◽  
pp. 675-682 ◽  
Author(s):  
Takuji Watanabe ◽  
◽  
Kazuteru Miyazaki ◽  
Hiroaki Kobayashi ◽  
◽  
...  
Keyword(s):  
Basis Function ◽  
Function Approximation ◽  
Policy Making ◽  
Basis Functions ◽  
State Spaces ◽  
Soccer Game ◽  
Continuous State ◽  
Current Input ◽  
Multiagent Environments ◽  
Parp 1

The penalty avoiding rational policy making algorithm (PARP) [1] previously improved to save memory and cope with uncertainty, i.e., IPARP [2], requires that states be discretized in real environments with continuous state spaces, using function approximation or some other method. Especially, in PARP, a method that discretizes state using a basis functions is known [3]. Because this creates a new basis function based on the current input and its next observation, however, an unsuitable basis function may be generated in some asynchronous multiagent environments. We therefore propose a uniform basis function and range extent of the basis function is estimated before learning. We show the effectiveness of our proposal using a soccer game task called “Keepaway.”

scholarly journals The Conical Radial Basis Function for Partial Differential Equations

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
J. Zhang ◽  
F. Z. Wang ◽  
E. R. Hou
Keyword(s):  
Radial Basis Function ◽  
Boundary Value Problems ◽  
Basis Function ◽  
Boundary Value ◽  
Computational Cost ◽  
Basis Functions ◽  
Simple Scheme ◽  
Collocation Points ◽  
Radial Basis ◽  
Radial Basis Function Method

The performance of the parameter-free conical radial basis functions accompanied with the Chebyshev node generation is investigated for the solution of boundary value problems. In contrast to the traditional conical radial basis function method, where the collocation points are placed uniformly or quasi-uniformly in the physical domain of the boundary value problems in question, we consider three different Chebyshev-type schemes to generate the collocation points. This simple scheme improves accuracy of the method with no additional computational cost. Several numerical experiments are given to show the validity of the newly proposed method.

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