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Piecewise linear approximations of multivariate functions: A multiresolution-based compression algorithm suitable for circuit implementation

机译:多元函数的分段线性逼近:适用于电路实现的基于多分辨率的压缩算法

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This paper is concerned with a multiresolution approach to the piecewise-linear approximation of multivariate nonlinear continuous functions. The proposed technique has no restrictions on the number of variables the functions depend on, and is based on the use of piecewise-linear "hat" functions. The approximation levels are related to nested function spaces. The multiresolution approach allows one to define a simple model reduction strategy that is based on a proper error definition. The interest in the piecewise-linear approximations and in the hat functions is motivated by the simplicity of their circuit implementations. The efficiency of the method is tested via two benchmark examples, one of which concerns the approximation of the vector field of a nonlinear dynamical system.
机译:本文涉及多元非线性连续函数的分段线性逼近的多分辨率方法。所提出的技术对函数所依赖的变量数目没有限制,并且基于分段线性“帽子”函数的使用。近似级别与嵌套函数空间有关。多分辨率方法允许基于适当的错误定义来定义一种简单的模型简化策略。对分段线性近似和帽函数的兴趣是由其电路实现的简单性引起的。通过两个基准示例测试了该方法的效率,其中一个示例涉及非线性动力系统的矢量场的近似。

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