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The Research on Detection Method of Cotton Contamination Based on Improved RBF Neutral Network

机译:基于改进RBF神经网络的棉污染检测方法研究

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For the low automation level and accuracy in detection of cotton contaminations, this paper adopts GMDH clustering algorithm to define the number of hidden nodes and the center of primary function in RBF neutral network adaptively. The improved RBF neutral network is applied to detect the cotton contaminations. The result shows that the RBF neutral network based on GMDH clustering algorithm could locate contaminations in cotton precisely, and has high location accuracy. That has significance in improving the quality of fabrics and reducing production costs.
机译:对于棉花污染检测的低自动化级别和准确性,本文采用GMDH集群算法,自适应地定义RBF中性网络中的隐藏节点数量和初级功能中心。改进的RBF中性网络被应用于检测棉污染物。结果表明,基于GMDH聚类算法的RBF中性网络可以精确地定位棉花中的污染,并且具有高位置精度。这具有改善织物质量和降低生产成本的重要性。

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