A Genetic Algorithm Based Clustering Approach for Piecewise Linearization of Nonlinear Functions

Ghosh, Subhojit; Ray, Asmini; Yadav, Deepak Kumar; Karan, Biswajit

doi:10.1109/icdecom.2011.5738489

Cited by 8 publications

(11 citation statements)

References 5 publications

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“…However, fuzzy clustering is dependent on the initial parameters and can reach a local minimum. In order to reduce the chance of yielding a local minimum, genetic algorithms have been proposed for clustering [7]. Other works rely on local searches that introduce linear segments iteratively until satisfying a stopping criterion [8,9].…”

Section: Introductionmentioning

confidence: 99%

Models and Algorithms for Optimal Piecewise-Linear Function Approximation

Camponogara

Nazari

2015

Mathematical Problems in Engineering

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Piecewise-linear functions can approximate nonlinear and unknown functions for which only sample points are available. This paper presents a range of piecewise-linear models and algorithms to aid engineers to find an approximation that fits best their applications. The models include piecewise-linear functions with a fixed and maximum number of linear segments, lower and upper envelopes, strategies to ensure continuity, and a generalization of these models for stochastic functions whose data points are random variables. Derived from recursive formulations, the algorithms are applied to the approximation of the production function of gas-lifted oil wells.

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Section: Introductionmentioning

confidence: 99%

Models and Algorithms for Optimal Piecewise-Linear Function Approximation

Camponogara

Nazari

2015

Mathematical Problems in Engineering

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“…In general, objective of standard classification is to obtain highest accuracy of classification. However, for the data mining tasks, we have to organize cost-sensitive learning with formulated cost matrix, class probability estimate, misclassification costs and other types of costs involved in the learning process [10].…”

Section: Development Of the Piecewise-linear Approach For The Conmentioning

confidence: 99%

Piecewise-Linear Approach to Classification Based on Geometrical Transformation Model for Imbalanced Dataset

Doroshenko

2018

2018 IEEE Second International Conference on Data Stream Mining &Amp; Processing (DSMP)

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“…From (15), it can be inferred that the pressure differential variation disturbs the flow through the valve's orifices. Thus, for maintaining the flow rate, specifically through the main motor, the proportional valve's spool displacement must be adjusted to compensate for this disturbance.…”

Section: -D Linearizationmentioning

confidence: 99%

“…A simple and common linearization strategy consists of building a linear interpolation between samples of the nonlinear function over a uniform partition of its domain. A tradeoff between increasing the approximation accuracy and simplifying the approximation by the minimum number of linearized sectors can be obtained using genetic algorithms [15].…”

Section: Introductionmentioning

confidence: 99%

Optimum Adaptive Piecewise Linearization: An Estimation Approach in Wind Power

Vaezi

Khayyer

Izadian

2017

IEEE Trans. Contr. Syst. Technol.

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This paper introduces an effective piecewise linearization technique to obtain an estimation of nonlinear models when their input-output domains include multidimensional operating points. The algorithm of a forward adaptive approach is introduced to identify the effective operating points for model linearization and adjust their domains for the maximum coverage and the minimum model linearization error. The technique obtains a minimum number of linearized models and the continuity of their domains. The algorithm also yields global minimum model linearization error. The introduced algorithm is formulated for a wind power transfer system for a 2-D set of input domains. The linearization error can be arbitrarily minimized in exchange for a higher number of models. The results demonstrate a significant improvement in the linearization of nonlinear models.

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A Genetic Algorithm Based Clustering Approach for Piecewise Linearization of Nonlinear Functions

Cited by 8 publications

References 5 publications

Models and Algorithms for Optimal Piecewise-Linear Function Approximation

Models and Algorithms for Optimal Piecewise-Linear Function Approximation

Piecewise-Linear Approach to Classification Based on Geometrical Transformation Model for Imbalanced Dataset

Optimum Adaptive Piecewise Linearization: An Estimation Approach in Wind Power

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