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基于人工神经网络在赤潮预测中应用的基础,利用遗传算法对其网络结构进行优化之后,用于预测浮游植物密度有良好的成效,对赤潮预警系统的研究有实际的应用价值.文章介绍了浮游植物密度预测的人工神经网络模型和经过优化后的人工神经网络预测计算. 相似文献
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文章简要地介绍了人工神经网络的特点,并与传统方法作了简单的比较,总结了人工神经网络在材料性能和配方研究方面的应用,讨论了人工神经网络用于无石棉密封材料配方优化和制品性能预测的优点及其应用状况. 相似文献
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基于人工神经网络在赤潮预测中应用的基础,利用遗传算法对其网络结构进行优化之后,用于预测浮游植物密度有良好的成效,对赤潮预警系统的研究有实际的应用价值。文章介绍了浮游植物密度预测的人工神经网络模型和经过优化后的人工神经网络预测计算。 相似文献
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利用人工神经网络预测离心泵性能的探索 总被引:6,自引:2,他引:6
介绍人工神经网络和BP算法的一般知识,并将首次应用到离心泵性能预测性,预测结果具有一定的精度,指出应用人工神经网络来预测离心泵的效率具有可行性。 相似文献
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在线趋势预测是实现以先进的预知维护取代传统的以时间为基础的预防性维护的关键技术,人工神经网络在线预测是进行机械设备趋势预测的新途径.探讨了将神经网络应用于油田大型注水泵的趋势预测技术,建立了注水泵人工神经网络趋势预测模型,进行了工业现场注水泵神经网络在线趋势预测技术的应用研究及实践验证. 相似文献
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基于BP神经网络的铣削力仿真技术研究 总被引:2,自引:1,他引:2
应用人工神经网络技术建立了铣削力仿真的BP网络模型。通过正交试验,获取训练样本,并对网络进行了训练。最后将网络预测结果与实验数据进行比较和误差分析,证明了人工神经网络能够准确地预测铣削力的大小。 相似文献
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一种网格并行任务执行时间预测算法 总被引:1,自引:0,他引:1
广泛研究了网格环境中并行任务执行时间的预测方法,提出了一种基于案例和人工神经网络的预测算法.该算法充分利用了历史有效信息,尤其是对于同一个任务的多次求解而获得的相似记录,通过建立任务特征模板,将历史任务,即案例进行分类,并利用指数平均值或者线性回归方法进行预测.但是由于网格环境的复杂性,以及有限元求解器在求解问题时的复杂性,导致相似性很难定义,在无法根据模板找到相似性案例的时候,利用人工神经网络预测方法进行预测.该算法在面向多学科应用的模拟与可视化环境中进行了实验,证明该方法具有较好的预测性能. 相似文献
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基坑变形人工神经网络预测受网络参数的影响较大,选取适当的网络参数才能得到较优的预测结果。本文介绍了人工神经网络原理及其网络参数的优化方法。以挡土桩桩顶水平位移预测为例,说明其具体预测步骤及网络参数优化方法。 相似文献
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Majid Karimi Farid Najafi Hossein Sadati Mozafar Saadat 《The International Journal of Advanced Manufacturing Technology》2008,39(5-6):559-569
In this article the results of the application of a flexible structure artificial neural network for controlling the angular velocity of a servo-hydraulic rotary actuator are discussed. A mathematical model for the system is derived, and a flexible artificial neural network (ANN)-based controller with the feedback error learning method as a learning algorithm is applied to the system. The neural network-based controller has a feed-forward structure and three layers. The flexible bipolar sigmoid function was used as the activation function of the network. The simulation and experimental results show good performance of the developed method in learning the inverse dynamic of the system and controlling the angular velocity of the rotary hydro motor. The advantages of the developed method for servo-hydraulic actuators over other traditional approaches are discussed. 相似文献
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利用模糊神经网络的推理和学习能力,在对机械加工过程中的切削参数进行自动选择的基础上,研究了一套基于机械加工参数自动选择的数控编程系统。运用VC++开发的系统实现了网络参数替换源程序中车削参数,实现了数控加工程序的自动生成。对此编程系统进行了详细的介绍,介绍了该软件的实现过程,并且用具体的实验测试了该软件并得到了标准的指令代码G代码。 相似文献
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In this paper, a practical bushing model is proposed to improve the accuracy of the vehicle dynamic analysis The results of the nubber bushing are used to develop an empirical bushing model with an artificial neural network A back propagation algorithm is used to obtain the weighting factor of the neural network Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra algorithm of ‘NARMAX’ form is employed to considet these effects A numerical example is earned out to verify the developed bushing model Then, a full car dynamic model with artificial neural network bushings is simulated to show the feasibility of the proposed bushing model 相似文献
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根据基板引脚图像的特征,建立了基于人工神经网络中BP算法的网络模型,并利用VC 6.0实现了图像特征提取和BP神经网络的算法,将它们集成在一个基板引脚图像识别软件包内。实验结果表明:该软件不仅可以准确识别基板引脚图像,而且具有较好的人机交互界面,为进一步开发全自动引线键合机控制系统奠定了基础。 相似文献
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某型飞机的作动器已有的故障诊断系统在故障检测方面较为有效,但在故障隔离方面性能较差,为此采用ANN技术时其诊断系统进行改善.针对作动器的典型故障,采用了三层前馈人工神经网络结构,介绍了共轭梯度BP算法及其编程实现过程,给出了诊断结果.诊断结果表明人工神经网络可以准确识别作动器的三种典型故障,提高了故障诊断效率及故障隔离能力. 相似文献
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STUDY ON INJECTION AND IGNITION CONTROL OF GASOLINE ENGINE BASED ON BP NEURAL NETWORK 总被引:4,自引:1,他引:4
Zhang Cuiping Yang QingfoCollege of Mechanical Engineering Taiyuan University of Technology Taiyuan China 《机械工程学报(英文版)》2003,16(4):441-444
According to advantages of neural network and characteristics of operating procedures ofengine, a new strategy is rapresented on the control of fuel injection and ignition timing of gasolineengine based on improved BP network algorithm. The optimum ignition advance angle and fuelinjection pulse band of engine under different speed and load are tested for the samples trainingnetwork, focusing on the study of the design method and procedure of BP neural network in engine 相似文献
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Optimization of injection molding process parameters using integrated artificial neural network model and expected improvement function method 总被引:1,自引:1,他引:0
Huizhuo Shi Yuehua Gao Xicheng Wang 《The International Journal of Advanced Manufacturing Technology》2010,48(9-12):955-962
In this study, an adaptive optimization method based on artificial neural network model is proposed to optimize the injection molding process. The optimization process aims at minimizing the warpage of the injection molding parts in which process parameters are design variables. Moldflow Plastic Insight software is used to analyze the warpage of the injection molding parts. The mold temperature, melt temperature, injection time, packing pressure, packing time, and cooling time are regarded as process parameters. A combination of artificial neural network and design of experiment (DOE) method is used to build an approximate function relationship between warpage and the process parameters, replacing the expensive simulation analysis in the optimization iterations. The adaptive process is implemented by expected improvement which is an infilling sampling criterion. Although the DOE size is small, this criterion can balance local and global search and tend to the global optimal solution. As examples, a cellular phone cover and a scanner are investigated. The results show that the proposed adaptive optimization method can effectively reduce the warpage of the injection molding parts. 相似文献