• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文OA文献 >Soft computing applications in dynamic model identification of polymer extrusion process
【2h】

Soft computing applications in dynamic model identification of polymer extrusion process

机译：软计算在聚合物挤出过程动力学模型识别中的应用

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper proposes the application of soft computing to deal with the constraints in conventional modelling techniques of the dynamic extrusion process. The proposed technique increases the efficiency in utilising the available information during the model identification. The resultant model can be classified as a ‘grey-box model’ or has been termed as a ‘semi-physical model’ in the context. The extrusion process contains a number of parameters that are sensitive to the operating environment. Fuzzy ruled-based system is introduced into the analytical model of the extrusion by means of sub-models to approximate those operational-sensitive parameters. In drawing the optimal structure for the sub-models, a hybrid algorithm of genetic algorithm with fuzzy system (GA-Fuzzy) has been implemented. The sub-models obtained show advantages such as linguistic interpretability, simpler rule-base and less membership functions. The developed model is adaptive with its learning ability through the steepest decent error back-propagation algorithm. This ability might help to minimise the deviation of the model prediction when the operational-sensitive parameters adapt to the changing operating environment in the real situation. The model is first evaluated through simulations on the consistency of model prediction to the theoretical analysis. Then, the effectiveness of adaptive sub-models in approximating the operational-sensitive parameters during the operation is further investigated.
机译：本文提出了软计算的应用，以解决动态挤压过程的常规建模技术中的约束。所提出的技术提高了模型识别期间利用可用信息的效率。生成的模型可以归类为“灰箱模型”，或者在上下文中被称为“半物理模型”。挤出过程包含许多对操作环境敏感的参数。基于模糊规则的系统通过子模型被引入到挤压的分析模型中，以近似那些操作敏感参数。在为子模型绘制最佳结构时，已实现了遗传算法与模糊系统的混合算法（GA-Fuzzy）。获得的子模型显示出诸如语言可解释性，更简单的规则库和更少的隶属函数等优点。通过最陡的体面误差反向传播算法，所开发的模型具有自适应能力，具有学习能力。当操作敏感参数适应实际情况下变化的操作环境时，此功能可能有助于最小化模型预测的偏差。首先通过模拟对模型预测与理论分析的一致性进行评估。然后，进一步研究了自适应子模型在操作期间近似于操作敏感参数的有效性。

著录项

  • 作者

    Tan, LP; Lotfi, A; Lai, E; Hull, B;

  • 作者单位
  • 年度 2004
  • 总页数
  • 原文格式 PDF
  • 正文语种 {"code":"en","name":"English","id":9}
  • 中图分类

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号