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基于模糊神经网络技术的定量秤研究 被引量:5

A Study of Quantitative Scale Based on Fuzzy Neural Network Technique
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摘要 以传送带、料门给料的动态称重过程为对象,提出了一种新型动态定量称重控制方法。该方法从过程对象的实际出发,基于多元复合控制思想,称重策略采取分段控制并引入模糊神经网络控制技术,调节量采取给料门和传送带两个自由度协调。研究结果表明:该方法能够比较理想地解决动态定量称重过程中速度与准确度的矛盾。动态称重设定值为1000g时,该装置定量误差为±0 5%,称重速度<8s 次。 By studying the weighing process composed of conveying belt and feeding gate, a new dynamic quantitative weighing control method is presented. The principle of the method is based on detailed analysis of the weighing process and the idea of multiple-compound control. A feeding gate and a conveying belt are adopted as tow regulated variables of the system. Especially, multi-stage control tactics and fuzzy neural networks control technique are applied to the system. The results show that the method can solve problem of the conflict between speed and accuracy of dynamic quantitative weighing process successfully. The quantitative error is ±0.5%, and weighing speed is less than 8 s/time when the set value of dynamic quantitative weighing is 1 000 g.
出处 《计量学报》 CSCD 北大核心 2004年第2期127-130,共4页 Acta Metrologica Sinica
基金 国家轻工业局科学基金(97032)
关键词 计量学 动态称重系统 定量秤 模糊神经网络 称重控制 Metrology Dynamic weighing system Quantitative scale Fuzzy neural networks Weighing control
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