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基于免疫神经网络的纺丝过程双向智能优化模型

发表时间:2013-08-31  浏览量:1077  下载量:513
全部作者: 梁霄,丁永生,郝矿荣,王华平
作者单位: 东华大学信息科学与技术学院;上海富欣智能交通控制有限公司;东华大学数字化纺织服装技术教育部工程研究中心;东华大学材料科学与工程学院
摘 要: 基于人工免疫机制(artificial immunity mechanism, AIM)增强的神经网络优化模型,提出一种双向纺丝工艺建模和智能优化方法及其专家系统,通过对大量生产数据进行处理和分析,形成双向优化体系,一方面可以得到纺丝生产线参数的合理配置方案,另一方面也可以对纤维产品的性能进行预测和评估。实验结果表明:提出的模型能够实现在工艺配置和产品性能之间的双向建模,其计算性能优于目前常用的神经网络优化模型,不仅有利于揭示纤维生产过程及其相应的产品质量之间的内在联系,也为生产人员提供了一种用于辅助纤维产品开发和设计的有益工具。
关 键 词: 控制理论;双向智能优化模型;免疫神经网络;人工免疫系统;纺丝过程
Title: Bi-directional intelligent optimizing model for the spinning process based on an immune neural network
Author: LIANG Xiao, DING Yongsheng, HAO Kuangrong, WANG Huaping
Organization: College of Information Sciences and Technology, Donghua University; Shanghai Fuxin Intelligent Transportation Solutions Co., Ltd.
Abstract: In this paper, a bi-directional intelligent optimizing model for the spinning process was proposed based on an artificial immunity mechanism (AIM), along with the expert system in which the proposed model was embedded. The bi-directional optimizing mechanism was formed by analyzing large numbers of data in production. With such a mechanism, the reasonable plans for the parameter configuration of the spinning production line could be acquired, and the performance of the fibers could also be predicted and evaluated. Simulation results showed that the proposed model had the ability to establish the bi-directional model between the production configuration and the fiber quality indices, which outperformed the conventional neural network-based model. The proposed model can not only reveal the internal connections between the manufacturing process of fiber and its corresponding quality indices, but provide a useful tool for the manufacturing personnel on the development and design of fiber products as well.
Key words: control theory; bi-directional intelligent optimizing model; immune neural network; artificial immune systems; spinning process
发表期数: 2013年8月第16期
引用格式: 梁霄,丁永生,郝矿荣,等. 基于免疫神经网络的纺丝过程双向智能优化模型[J]. 中国科技论文在线精品论文,2013,6(16):1558-1568.
 
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