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A Neural Network Approach To The Fuzzy Transform

机译：模糊变换的神经网络方法

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摘要

The paper deals with the F-transform technique which was introduced as a method for an approximate representation ofrncontinuous functions. The same task is solved by many methods from different areas. Neural networks also belong to techniques which have been proved to be powerful for approximation objectives. They provide us with many advantages, especially incremental way of learning parameters. The paper inherits neural approaches to the F-transform method and presents experiments justifying the proposed approach. Incremental way of determination of certain parameters of the F-transform method which were up to now given by batch formula enriches possible areas of application of the method by fast on-line processes. Moreover, the neural approach is demonstrated to be an appropriate one for finding such fuzzy partition of a domain which respects better a given set of measured samples which are to be approximated by a continuous function with no predetermined shape.
机译：本文介绍了F变换技术，该技术作为连续函数的近似表示方法而引入。可以通过来自不同领域的许多方法来解决同一任务。神经网络也属于已被证明对逼近目标具有强大功能的技术。它们为我们提供了许多优势，尤其是增量学习参数的方式。本文继承了F变换方法的神经方法，并提出了证明该方法合理的实验。到目前为止，批处理公式给出的确定F变换方法某些参数的增量方式通过快速的在线过程丰富了该方法的可能应用领域。而且，神经方法被证明是一种合适的方法，用于找到这样的域的模糊分区，该模糊分区更好地尊重给定的一组测量样本，这些样本将由没有预定形状的连续函数来近似。

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