共查询到19条相似文献,搜索用时 62 毫秒
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提出一种基于自组织特征映射神经网络(SOFM)的零件加工尺寸类型识别方法.首先从三维CAD软件中获取包含零件特征数据的XML文件,并从文件中提取相应的加工特征及尺寸.然后以零件加工特征作为SOFM的输入层的神经元,经处理后作为SOFM的输入向量,利用SOFM自学习和自组织能力对输入向量进行训练.训练好的网络可以实现对零件加工尺寸类型进行较好的识别.最后通过对某零件的尺寸类型识别,验证了所提方法对平面、内孔、外圆和定位四类典型加工尺寸类型识别的有效性. 相似文献
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考试成绩是评估教学质量的一项重要依据,相比于k-means算法需要确定合适聚类数目的缺点,利用自组织神经网络(SOM)对学生成绩自动聚类。根据聚类结果分析学生的学习差异和学习特点,以提高和完善教师的教学方法。 相似文献
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神经网络在图像压缩技术中的应用 总被引:3,自引:0,他引:3
综述了神经网络作为图象压缩信号处理工具的方法,由于神经网络的大规模并行及其分布结构,使之成为解决数据压缩的有力工具;网络的特征与人类视觉系统的特征相类似,这就使我们更容易处理视觉信息,例如,多层感知可作为差分脉冲编码调制的非线性预测器,已这种预测器较线性预测器可改进预测效果,另一活跃的研究领域是Hebbian学习规则获取主分量。主分量是理想的线性KL变换的基向量。这些学习算法的计算更优越于传统特征 相似文献
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Ashir Javeed Ana Luiza Dallora Johan Sanmartin Berglund Arif Ali Peter Anderberg Liaqat Ali 《计算机、材料和连续体(英文)》2023,75(2):2491-2508
Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic systems developed that use genetic algorithms for feature selection, while artificial neural network and deep neural network are used for dementia classification. The proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%, sensitivity of 93.15%, specificity of 91.59%, MCC of 0.4788, and performed superior to other 11 machine learning techniques which were presented in the past for dementia prediction. The identified best predictors were: age, past smoking habit, history of infarct, depression, hip fracture, single leg standing test with right leg, score in the physical component summary and history of TIA/RIND. The identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset. 相似文献
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Recently, the effectiveness of neural networks, especially convolutional neural networks, has been validated in the field of natural language processing, in which, sentiment classification for online reviews is an important and challenging task. Existing convolutional neural networks extract important features of sentences without local features or the feature sequence. Thus, these models do not perform well, especially for transition sentences. To this end, we propose a Piecewise Pooling Convolutional Neural Network (PPCNN) for sentiment classification. Firstly, with a sentence presented by word vectors, convolution operation is introduced to obtain the convolution feature map vectors. Secondly, these vectors are segmented according to the positions of transition words in sentences. Thirdly, the most significant feature of each local segment is extracted using max pooling mechanism, and then the different aspects of features can be extracted. Specifically, the relative sequence of these features is preserved. Finally, after processed by the dropout algorithm, the softmax classifier is trained for sentiment classification. Experimental results show that the proposed method PPCNN is effective and superior to other baseline methods, especially for datasets with transition sentences. 相似文献
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特征金字塔网络(FPN)是CNN网络对图像信息进行表达输出的一种有效方法,在目标检测网络中广泛应用.然而,FPN没有充分地将浅层的细节信息传递到深层的语义特征,存在特征融合不足的缺陷,因而只能依靠深层语义信息来进行预测,从而忽略了网络低层细节信息,对各种视觉学习的效果造成了一定的影响.针对FPN存在的以上问题,本文提出基于特征金字塔的多尺度特征融合网络模型,在FPN主干网络的基础上,设计了混合特征金字塔和金字塔融合模块,并结合注意力机制,对特征金字塔进行了多尺度的深度融合.本文在PASCAL VOC2012和MS COCO2014数据集上,以Faster R-CNN作为基础检测器进行实验,验证了MFPN对特征融合的有效性. 相似文献
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I. N. Tansel F. Inanc N. Reen P. Chen X. Wang C. Kropas-Hughes A. Yenilmez 《Journal of Nondestructive Evaluation》2006,25(2):53-66
Back propagation (BP) type artificial neural networks (ANN) have been trained and used for thickness estimations from radiographic images. Test objects have been assembled from different materials and radiographic images of the test objects were obtained for thickness estimations. While some of the study has been based on the synthetic images formed through the radiographic simulation program XRSIM, the rest of the study has used actual radiographic images. The average estimation errors were 7% and 9% when two and three synthetic radiographic images obtained at different x-ray tube settings were used. With the actual images, the thickness of only one of the materials has been estimated and the material was identified. This has been due to the fact that scattering of x-rays by the test object results in a non uniform gray scale variation in the radiographic images even though the object thickness is uniform. 相似文献
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改进一种基于瞬时最优控制的神经网络训练算法。本方法以瞬间最优控制价值函数最小化为训练目标,考虑了地震输入的能量,利用最速下降梯度法计算权值的改变量,并对敏感度矩阵进行近似处理,可解决神经网络控制中神经网络控制器难以获得的训练输入/输出样本对的难题。该方法适合多输入/多输出结构体系,整个推导过程都是针对此体系进行的。文中通过对一个三层框架结构体系进行有效的仿真计算,说明了算法的有效性。 相似文献