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Fuzzy regression by fuzzy number neural networks

机译:模糊数神经网络的模糊回归

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摘要

In this paper, we describe a method for nonlinear fuzzy regression using neural network models. In earlier work, strong assumptions were made on the form of the fuzzy number parameters: symmetric triangular, asymmetric triangular, quadratic, trapezoidal, and so on. Our goal here is to substantially generalize both linear and nonlinear fuzzy regression using models with general fuzzy number inputs, weights, biases, and outputs. This is accomplished through a special training technique for fuzzy number neural networks. The technique is demonstrated with data from an industrial quality control problem.
机译:在本文中,我们描述了一种使用神经网络模型进行非线性模糊回归的方法。在较早的工作中,对模糊数参数的形式做出了强有力的假设:对称三角形,非对称三角形,二次形,梯形等等。我们的目标是使用具有一般模糊数输入,权重,偏差和输出的模型来大致概括线性和非线性模糊回归。这是通过针对模糊数神经网络的特殊训练技术来完成的。一项工业质量控制问题的数据证明了该技术。

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