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Fuzzy Neural Model for Flatness Pattern Recognition

机译:平面度模式识别的模糊神经模型

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摘要

For the problems occurring in a least square method model,a fuzzy model,and a neural network model for flatness pattern recognition,a fuzzy neural network model for flatness pattern recognition with only three-input and three-output signals was proposed with Legendre orthodoxy polynomial as basic pattern,based on fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm.The model not only had definite physical meanings in its inner nodes,but also had strong self-adaptability,anti-interference ability,high recognition precision,and high velocity,thereby meeting the demand of high-precision flatness control for cold strip mill and providing a convenient,practical,and novel method for flatness pattern recognition.
机译:针对最小二乘模型,模糊模型和神经网络模型进行平面度模式识别的问题,提出了基于Legendre正统多项式的仅具有三输入和三输出信号的模糊神经网络模型。该模型以模糊逻辑专家的经验知识和遗传BP混合优化算法为基本模型。该模型不仅在内部节点具有明确的物理意义,而且具有较强的自适应性,抗干扰能力,识别精度高,高速,从而满足冷轧机高精度平面度控制的要求,并提供了一种方便,实用,新颖的平面度图案识别方法。

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