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A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization

Moêz Soltani, Abdelkader Chaari, Fayçal Ben Hmida (2012)

International Journal of Applied Mathematics and Computer Science

This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained...

Adaptive control scheme based on the least squares support vector machine network

Tarek A. Mahmoud (2011)

International Journal of Applied Mathematics and Computer Science

Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine...

An efficient algorithm for estimating the parameters of superimposed exponential signals in multiplicative and additive noise

Jiawen Bian, Huiming Peng, Jing Xing, Zhihui Liu, Hongwei Li (2013)

International Journal of Applied Mathematics and Computer Science

This paper considers parameter estimation of superimposed exponential signals in multiplicative and additive noise which are all independent and identically distributed. A modified Newton-Raphson algorithm is used to estimate the frequencies of the considered model, which is further used to estimate other linear parameters. It is proved that the modified Newton-Raphson algorithm is robust and the corresponding estimators of frequencies attain the same convergence rate with Least Squares Estimators...

Determination of the initial stress tensor from deformation of underground opening in excavation process

Josef Malík, Alexej Kolcun (2022)

Applications of Mathematics

A method for the detection of the initial stress tensor is proposed. The method is based on measuring distances between pairs of points located on the wall of underground opening in the excavation process. This methods is based on solving twelve auxiliary problems in the theory of elasticity with force boundary conditions, which is done using the least squares method. The optimal location of the pairs of points on the wall of underground openings is studied. The pairs must be located so that the...

Determination of the initial stress tensor from deformation of underground opening -- theoretical background and applications

Malík, Josef, Kolcun, Alexej (2023)

Programs and Algorithms of Numerical Mathematics

In this paper a method for the detection of initial stress tensor is proposed. The method is based on measuring distances between some pairs of points located on the wall of underground opening in the excavation process. This methods is based on the solution of eighteen auxiliary problems in the theory of elasticity with force boundary conditions. The optimal location of the pairs of points on the wall of underground work is studied. The pairs must be located so that the condition number of a certain...

Efficient measurement of higher-order statistics of stochastic processes

Wladyslaw Magiera, Urszula Libal, Agnieszka Wielgus (2018)

Kybernetika

This paper is devoted to analysis of block multi-indexed higher-order covariance matrices, which can be used for the least-squares estimation problem. The formulation of linear and nonlinear least squares estimation problems is proposed, showing that their statements and solutions lead to generalized `normal equations', employing covariance matrices of the underlying processes. Then, we provide a class of efficient algorithms to estimate higher-order statistics (generalized multi-indexed covariance...

Finding vertex-disjoint cycle cover of undirected graph using the least-squares method

Lamač, Jan, Vlasák, Miloslav (2023)

Programs and Algorithms of Numerical Mathematics

We investigate the properties of the least-squares solution of the system of equations with a matrix being the incidence matrix of a given undirected connected graph G and we propose an algorithm that uses this solution for finding a vertex-disjoint cycle cover (2-factor) of the graph G .

On one approach to local surface smoothing

Nikolay Dikoussar, Csaba Török (2007)

Kybernetika

A bicubic model for local smoothing of surfaces is constructed on the base of pivot points. Such an approach allows reducing the dimension of matrix of normal equations more than twice. The model enables to increase essentially the speed and stability of calculations. The algorithms, constructed by the aid of the offered model, can be used both in applications and the development of global methods for smoothing and approximation of surfaces.

On parameter estimation in the bass model by nonlinear least squares fitting the adoption curve

Darija Marković, Dragan Jukić (2013)

International Journal of Applied Mathematics and Computer Science

The Bass model is one of the most well-known and widely used first-purchase diffusion models in marketing research. Estimation of its parameters has been approached in the literature by various techniques. In this paper, we consider the parameter estimation approach for the Bass model based on nonlinear weighted least squares fitting of its derivative known as the adoption curve. We show that it is possible that the least squares estimate does not exist. As a main result, two theorems on the existence...

Performance analysis of least squares algorithm for multivariable stochastic systems

Ziming Wang, Yiming Xing, Xinghua Zhu (2023)

Kybernetika

In this paper, we consider the parameter estimation problem for the multivariable system. A recursive least squares algorithm is studied by minimizing the accumulative prediction error. By employing the stochastic Lyapunov function and the martingale estimate methods, we provide the weakest possible data conditions for convergence analysis. The upper bound of accumulative regret is also provided. Various simulation examples are given, and the results demonstrate that the convergence rate of the...

Piecewise approximation and neural networks

Martina Révayová, Csaba Török (2007)

Kybernetika

The paper deals with the recently proposed autotracking piecewise cubic approximation (APCA) based on the discrete projective transformation, and neural networks (NN). The suggested new approach facilitates the analysis of data with complex dependence and relatively small errors. We introduce a new representation of polynomials that can provide different local approximation models. We demonstrate how APCA can be applied to especially noisy data thanks to NN and local estimations. On the other hand,...

Sard's approximation processes and oblique projections

G. Corach, J. I. Giribet, A. Maestripieri (2009)

Studia Mathematica

Three problems arising in approximation theory are studied. These problems have already been studied by Arthur Sard. The main goal of this paper is to use geometrical compatibility theory to extend Sard's results and get characterizations of the sets of solutions.

The output least squares identifiability of the diffusion coefficient from an H 1 –observation in a 2–D elliptic equation

Guy Chavent, Karl Kunisch (2002)

ESAIM: Control, Optimisation and Calculus of Variations

Output least squares stability for the diffusion coefficient in an elliptic equation in dimension two is analyzed. This guarantees Lipschitz stability of the solution of the least squares formulation with respect to perturbations in the data independently of their attainability. The analysis shows the influence of the flow direction on the parameter to be estimated. A scale analysis for multi-scale resolution of the unknown parameter is provided.

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