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Models and Algorithms for Optimal Piecewise-Linear Function Approximation

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  • Eduardo Camponogara
  • Luiz Fernando Nazari

Abstract

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.

Suggested Citation

  • Eduardo Camponogara & Luiz Fernando Nazari, 2015. "Models and Algorithms for Optimal Piecewise-Linear Function Approximation," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-9, July.
    • Handle: RePEc:hin:jnlmpe:876862
    DOI: 10.1155/2015/876862
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    Cited by:

    1. Vitaly Promyslov & Kirill Semenkov, 2021. "Non-Statistical Method for Validation the Time Characteristics of Digital Control Systems with a Cyclic Processing Algorithm," Mathematics, MDPI, vol. 9(15), pages 1-16, July.
    2. Nicole Škorupová & Petr Raunigr & Petr Bujok, 2022. "Usage of Selected Swarm Intelligence Algorithms for Piecewise Linearization," Mathematics, MDPI, vol. 10(5), pages 1-24, March.

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