基于神经网络模型预测涤纶FDY油剂及其乳液的外观

将基础油剂、偏亲水乳化剂和偏亲油乳化剂按不同配比制备涤纶全拉伸丝(FDY)油剂以及油剂质量分数为1%的乳液,定量分析油剂及乳液的外观随基础油剂含量、偏亲水乳化剂含量的变化规律;建立以基础油剂含量及偏亲水乳化剂含量为输入、油剂及乳液的外观为输出的分类神经网络模型,用采集的不同配方下油剂及乳液的外观进行网络训练,用新配方下油剂及乳液的外观进行模型验证。结果表明:油剂及乳液的外观是关于基础油剂含量、偏亲水乳化剂含量的分段函数,在基础油剂含量确定时,随着偏亲水乳化剂含量增加,油剂及乳液的外观依次呈现出白色乳液、略带蓝光白色乳液、蓝白色半透明液体、透明液体和暗灰色液体,且油剂分层;分类神经网络模型经训练后,准确率达98.3%,模型拟合效果好,能准确预测新配方下油剂及乳液的外观,可以为判断新油剂配方下能否制备微乳液体系提供帮助。

小波神经网络多属性综合评价及其应用

本文针对现存多属性综合评价方法的不足,借助小波神经网络思想、原理,构造了一种新的分类小波神经网络多属性综合评价模型,并进行了实例验证,表明此分类小波神经网络多属性综合评价方法是正确、可行的,且能有效地提高综合评价精度与收敛速度。

基于关键词加权的混合特征文本快速分类仿真

电子文本形式的网络信息不仅数量多,且混合特征具有较高相似性,很难达到特征的平均分布。特征项在类别间的不均性导致文本权重计算易出现偏差,影响类别特征词的提取,导致文本分类难度较大。为此,提出一种基于关键词加权的混合特征文本快速分类方法。采用词频逆文本频率指数信息检索方法对文本加权,计算不同权重下文本关键词在中心集合中出现的频率。根据频率阈值提取关键特征,确定文本集合中类中心点。计算与类中心相关性最高的文本数据,提取关联度特征。建立神经网络分类模型,预先设定一组包含详细特征的文本集,作为初始值输入到神经网络中,每个层次根据目标特征逐一比对实现有效分类。实验证明,所研究方法的查全率更高,文本混合特征提取的召回率高于40%,说明研究方法应用性能更优,对不同种类的文本集均能完成精准分类。

基于Petri网的模型与GIS集成研究

以BP神经网络遥感分类模型为例 ,探讨了模型与地理信息系统的集成问题。指出问题的实质是对象状态数据模型、对象模拟模型和对象分析处理模型的综合表达与处理。提出了建立在元数据和元模型基础上基于数据处理流程的集成方案的一般结构。分析了Petri网的演绎形Derivation网的特点 ,设计了Deriva tion +网。提出了Derivation +网对元数据和元模型的管理方案以及Derivation +网内部校验方法。在此基础上以Derivation +网为核心 ,以元数据和元模型为接口 。

扩展的树增强朴素贝叶斯网络信用评估模型

针对信用评估问题的特点,在推导混合数据极大似然函数的基础上,提出了扩展的树增强朴素贝叶斯网络信用评估模型,用10层交叉验证在真实数据集上进行了测试并与神经网络分类模型进行了比较.测试结果表明扩展的树增强朴素贝叶斯网络信用评估模型具有较高的分类精度,在信用评估中具有优势.

Dynamic vaccine distribution model based on epidemic diffusion rule and clustering approach

Due to the fact that the emergency medicine distribution is vital to the quick response to urgent demand when an epidemic occurs, the optimal vaccine distribution approach is explored according to the epidemic diffusion rule and different urgency degrees of affected areas with the background of the epidemic outbreak in a given region. First, the SIQR （susceptible, infected, quarantined,recovered） epidemic model with pulse vaccination is introduced to describe the epidemic diffusion rule and obtain the demanded vaccine in each pulse. Based on the SIQR model, the affected areas are clustered by using the self-organizing map （SOM） neutral network to qualify the results. Then, a dynamic vaccine distribution model is formulated, incorporating the results of clustering the affected areas with the goals of both reducing the transportation cost and decreasing the unsatisfied demand for the emergency logistics network. Numerical study with twenty affected areas and four distribution centers is carried out. The corresponding numerical results indicate that the proposed approach can make an outstanding contribution to controlling the affected areas with a relatively high degree of urgency, and the comparison results prove that the performance of the clustering method is superior to that of the non-clustering method on controlling epidemic diffusion.

Tongue image segmentation and tongue color classification based on deep learning

Objective To propose two novel methods based on deep learning for computer-aided tongue diagnosis,including tongue image segmentation and tongue color classification,improving their diagnostic accuracy.Methods LabelMe was used to label the tongue mask and Snake model to optimize the labeling results.A new dataset was constructed for tongue image segmentation.Tongue color was marked to build a classified dataset for network training.In this research,the Inception+Atrous Spatial Pyramid Pooling(ASPP)+UNet(IAUNet)method was proposed for tongue image segmentation,based on the existing UNet,Inception,and atrous convolution.Moreover,the Tongue Color Classification Net(TCCNet)was constructed with reference to ResNet,Inception,and Triple-Loss.Several important measurement indexes were selected to evaluate and compare the effects of the novel and existing methods for tongue segmentation and tongue color classification.IAUNet was compared with existing mainstream methods such as UNet and DeepLabV3+for tongue segmentation.TCCNet for tongue color classification was compared with VGG16 and GoogLeNet.Results IAUNet can accurately segment the tongue from original images.The results showed that the Mean Intersection over Union(MIoU)of IAUNet reached 96.30%,and its Mean Pixel Accuracy(MPA),mean Average Precision(mAP),F1-Score,G-Score,and Area Under Curve(AUC)reached 97.86%,99.18%,96.71%,96.82%,and 99.71%,respectively,suggesting IAUNet produced better segmentation than other methods,with fewer parameters.Triplet-Loss was applied in the proposed TCCNet to separate different embedded colors.The experiment yielded ideal results,with F1-Score and mAP of the TCCNet reached 88.86% and 93.49%,respectively.Conclusion IAUNet based on deep learning for tongue segmentation is better than traditional ones.IAUNet can not only produce ideal tongue segmentation,but have better effects than those of PSPNet,SegNet,UNet,and DeepLabV3+,the traditional networks.As for tongue color classification,the proposed network,TCCNet,had better F1-Score and mAP values as compared with other neural networks such as VGG16 and GoogLeNet.

RESEARCH AND APPLICATION OF A NEURAL NETWORK CLASSIFIER BASED ON DYNAMIC THRESHOLD

In this study, a Multi-Layer BP neural network(MLBP) with dynamic thresholds is employed to build a classifier model.As to the design of the neural network structure, theoretical guidance and plentiful experiments are combined to optimize the hidden layers' parameters which include the number of hidden layers and their node numbers.The classifier with dynamic thresholds is used to standardize the output for the first time, and it improves the robustness of the model to a high level.Finally, the classifier is applied to forecast box office revenue of a movie before its theatrical release.The comparison results with the MLP method show that the MLBP classifier model achieves more satisfactory results, and it is more reliable and effective to solve the problem.

流空间视角下武汉都市圈城市空间联系格局及影响因素

进入高质量发展阶段,都市圈作为国家新型城镇化战略格局中承上启下的关键环节,已成为引领区域发展的重要空间单元。文章以武汉都市圈为研究区域,基于手机信令、百度指数等多源大数据,采用要素流模型、SOM神经网络分级模型和QAP关系回归分析等得出城市空间联系格局、发展规律及影响因素。结果表明:①武汉都市圈各要素流动形成的空间格局呈多中心组团式结构,且内部流动强度不一;②根据SOM神经网络分级模型结果将武汉都市圈城市的对外联系能力划分为4个等级,其中武汉的对外联系能力与其他城市之间呈现出显著差异;③城市发展水平和信息化水平差异对不同城市间要素流强度具有显著负影响,城市规模和开放程度差异具有显著的正向作用,共同影响了都市圈内城市空间联系格局的形成。最后,针对武汉都市圈城市空间联系格局的优化提出建议。

A biologically inspired model for pattern recognition

In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent.