基于神经网络的异位妊娠发病率发展趋势研究

本文阐述了ＢＰ神经网络的基本算法,并根据近十年来搜集整理的异位妊娠发病率的统计资料;采用 ＢＰ神经网络对异位妊娠的发病率的发展趋势进行预测;预测结果和实际结果比较吻合．进一步指出了神经网络可作为一种新的预测方法．     

病毒基因组启动子识别的人工神经网络方法

本文运用神经网络方法,并结合病毒基因分子生物学有关理论与统计事实,对病毒基因启动子区域进行了识别,文中选择了共35个基因组,作为研究对象．学习组选择了28个基因组,预测组选择了7个基因组,结果表明,将神经网络模型与病毒基因有关理论相结合,能够运用计算方法,以大量的可能启动子组合中排列出唯一的启动子区域．     

非线性脉冲中立型时滞抛物方程组解的振动性

讨论一类非线性脉冲中立型时滞抛物方程组解的振动性,利用一阶脉冲中立型微分不等式给出了该类方程组在Robin,Dirichlet边界条件下所有解振动的若干充分条件．所得结果充分反映了脉冲和时滞在振动中的影响作用．     

具有可变时滞的高阶非自治中立型差分方程的振动性

研究具有可变时滞的高阶非自治中立型差分方程△~m(X_n-cx_(n-k)) h(n,x_(n-l_n))=0 (n=0,1,2,...)的振动性．利用Banach空间的压缩映象原理,得到了这类差分方程振动的必要条件．利用偏序集中的Knaster不动点定理,得到了这类方程振动的充分必要条件．同时得到了这类差分方程存在最终正解的准则．     

用人工神经网络方法确定真核基因启动子

本文运用神经网络方法,并结合分子生物学的有关理论与实验统计事实,对真核基因启动子区域进行了识别．文中选择了人类、牛、猪、猫、山羊、兔、绵羊、大鼠、小鼠、小马、仓鼠、鸡、鸭、大豆等共13种真核生物360个基因组,作为研究对象．学习组选择了300个基因组,预测组选择了60基因组．结果表明,将神经网络模型与基因理论相结合,能够运用计算方法,从大量的可能启动子组合中排列出唯一的启动子区域．     

农药醚菊酯类似物构效关系的人工神经网络方法研究

本文以醚菊酯类似物作为研究对象,尝试使用神经网络方法进行构效关系分析,并对该种农药活性进行了预测。在所研究的样本集中,由结构预测活性的成功率可达100％,本文的研究表明：神经网络方法以其极强的非线性能力,可望成为农药构效关系研究的一种有效的工具．     

BP人工神经网络紫外分光光度法同时测定三种氨基酸

应用人工BP神经网络紫外分光光度法,不经分离,同时测定了色氮酸、酪氮酸和苯丙氮酸,采用均匀实验设计法,确定了最佳网络运行参数;比较了三层和四层BP神经网络的预测能力;用预测能力较好的三层BP神经网络同时测定了复方氮基酸注射液中色氮酸、酪氮酸和苯丙氮酸,相对平均误差分别为1．09％、3．71％和2．40％。     

真核基因PolyA信号的人工神经网络识别研究

本文采用神经网络方法对真核基因的PolyA信号进行识别．文中选择了人类、牛、猪、猫、山羊、兔、绵羊、大鼠、小鼠、小马、鸡、仓鼠共12种真核生物的270个基因组作为研究对象，训练组包括230个基因，预测组包括40个基因．结果表明，结合PolyA上游的碱基顺序特异性（紧挨PolyA的上游地GC），可以从神经网络识别的大量可能PolyA信号中得到正确PolyA信号．     

具有时滞的北美鹑增长模型的振动性和全局吸引性

本文研究了离散Ｂｏｂｗｈｉｔｅ Ｑｕａｉｌ模型的振动性和全局吸引性．获得了该方程的一切解关于正平衡常数Ｎ＝振动的充分条件以及其所有正解趋近于正平衡常数Ｎ的充分条件．     

变系数变时滞BAM神经网络概周期解的存在性与全局吸引性

利用指数二分性、Banach不动点定理与微分不等式分析技巧,在不要求激活函数有界的条件下,给出了变系数变时滞的BAM神经网络概周期解的存在唯一性和全局吸引性的充分条件．所得结果推广和改进了相应文献的结果。对设计BAM神经网络概周期振荡有重要意义．     

Automatic strawberry leaf scorch severity estimation via faster R-CNN and few-shot learning

Using deep learning to estimate strawberry leaf scorch severity often achieves unsatisfactory results when a strawberry leaf image contains complex background information or multi-class diseased leaves and the number of annotated strawberry leaf images is limited. To solve these issues, in this paper, we propose a two-stage method including object detection and few-shot learning to estimate strawberry leaf scorch severity. In the first stage, Faster R-CNN is used to mark the location of strawberry leaf patches, where each single strawberry leaf patch is clipped from original strawberry leaf images to compose a new strawberry leaf patch dataset. In the second stage, the Siamese network trained on the new strawberry leaf patch dataset is used to identify the strawberry leaf patches and then estimate the severity of the original strawberry leaf scorch images according to the multi-instance learning concept. Experimental results from the first stage show that Faster R-CNN achieves better mAP in strawberry leaf patch detection than other object detection networks, at 94.56%. Results from the second stage reveal that the Siamese network achieves an accuracy of 96.67% in the identification of strawberry disease leaf patches, which is higher than the Prototype network. Comprehensive experimental results indicate that compared with other state-of-the-art models, our proposed two-stage method comprising the Faster R-CNN (VGG16) and Siamese networks achieves the highest estimation accuracy of 96.67%. Moreover, our trained two-stage model achieves an estimation accuracy of 88.83% on a new dataset containing 60 strawberry leaf images taken in the field, which indicates its excellent generalization ability.     

神经网络提高肝细胞癌磁共振波谱诊断正确率

通过评价31磷磁共振波谱(31Phosphorus Magnetic Resonance Spectroscopy,31P-MRS)来辨别三种诊断类型:肝细胞癌,正常肝和肝硬化。运用反向传输神经网络(BP)和径向基函数神经网络(RBF)分析31P-MRS数据,分别建立神经网络模型,进行肝细胞癌的诊断分类以期提高识别率。实验结果证明,应用神经网络模型后,31P-MR波谱对活体肝细胞癌的诊断正确率从89.47%提高到97.3%,且BP更优于RBF。     

Evaluation and identification of markers of damage in mushrooms (Agaricus bisporus) postharvest using a GC/MS metabolic profiling approach

The aim of this research was to use the gas chromatography-mass spectrometry (GC/MS) profiling method coupled with chemometric tools to profile mechanically damaged and undamaged mushrooms during storage and to identify specific metabolites that may be used as markers of damage. Mushrooms grown under controlled conditions were bruise damaged by vibration to simulate damage during normal transportation. Three damage levels were evaluated; undamaged, damaged for 20 min and damaged for 40 min and two time levels studied; day zero and day one after storage at 4oC. Applying this technique over 100 metabolites were identified, quantified and compiled in a library. Random forest classification models were used to predict damage in mushrooms producing models with error rates of >10% using cap and stipe tissue. Fatty acids were found to be the most important group of metabolites for predicting damage in mushrooms. PLS models were also developed producing models with low error rates. With a view to exploring biosynthetic links between metabolites, a pairwise correlation analysis was performed for all polar and non-polar metabolites. The appearance of high correlation between linoleic acid and pentadecanoic acid in the non-polar phase of damaged mushrooms indicated the switching on of a metabolic pathway when a mushroom is damaged.     

草莓种苗壮苗指数模型的构建与质量评价

随着我国草莓栽培面积逐年增加,草莓种苗需求量越来越大,为了确保种苗育苗质量,亟需开展壮苗评价的研究。本研究以生长40 d的‘红颜’穴盘苗为对象,在测定地上部和地下部生长、鲜重、干重等16项指标的基础上,分别构建单项指标隶属函数,使用加权模糊评判法计算种苗综合评价指数;利用主成分分析筛选的关键指标组成多个壮苗指数模型,与种苗综合评价指数进行相关性分析后,确定最佳草莓壮苗指数模型并进行验证。结果表明: 随机选取的320株草莓种苗16项指标存在显著差异,综合评价指数为0.165～0.817,可作为壮苗指数模型构建和种苗质量评价的依据。主成分分析将16项指标划分为地上部相关指标、地下部相关指标和色素指标3个主成分,累计贡献率达到79.7%;从每个主成分中选择贡献值最大的3个指标随机组成27种壮苗指数模型,通过相关性分析筛选出与综合评价指数相关性最大的5个壮苗指数模型,其中“地上干重×根系表面积×叶绿素a”的相关性最高,用‘红颜’、‘香野’和‘甜查理’种苗验证相关系数均最大,分别为0.879、0.924和0.975,确定可作为草莓壮苗指数计算模型。以综合评价指数为种苗质量分级依据,可将种苗健壮程度分为3个等级:等级Ⅰ(综合评价指数≥0.5,壮苗指数≥4.0)为优质苗,等级Ⅱ(综合评价指数0.3～0.5,壮苗指数0.5～4.0)为合格苗,等级Ⅲ(综合评价指数≤0.3,壮苗指数≤0.5)为弱苗。研究结果可为草莓或其他种苗壮苗指数计算和种苗健壮程度评价提供理论依据与科学方法。     

Panel semiparametric quantile regression neural network for electricity consumption forecasting

Addressing the forecasting issues is one of the core objectives of developing and restructuring of electric power industry in China. However, there are not enough efforts that have been made to develop an accurate electricity consumption forecasting procedure. In this paper, a panel semiparametric quantile regression neural network (PSQRNN) is developed by combining an artificial neural network and semiparametric quantile regression for panel data. By embedding penalized quantile regression with least absolute shrinkage and selection operator (LASSO), ridge regression and backpropagation, PSQRNN keeps the flexibility of nonparametric models and the interpretability of parametric models simultaneously. The prediction accuracy is evaluated based on China's electricity consumption data set, and the results indicate that PSQRNN performs better compared with three benchmark methods including BP neural network (BP), Support Vector Machine (SVM) and Quantile Regression Neural Network (QRNN).     

Prediction model of DnBP degradation based on BP neural network in AAO system

A laboratory-scale anaerobic-anoxic-oxic (AAO) system was established to investigate the fate of DnBP. A removal kinetic model including sorption and biodegradation was formulated, and kinetic parameters were evaluated with batch experiments under anaerobic, anoxic, oxic conditions. However, it is highly complex and is difficult to confirm the kinetic parameters using conventional mathematical modeling. To correlate the experimental data with available models or some modified empirical equations, an artificial neural network model based on multilayered partial recurrent back propagation (BP) algorithm was applied for the biodegradation of DnBP from the water quality characteristic parameters. Compared to the kinetic model, the performance of the network for modeling DnBP is found to be more impressive. The results showed that the biggest relative error of BP network prediction model was 9.95%, while the kinetic model was 14.52%, which illustrates BP model predicting effluent DnBP more accurately than kinetic model forecasting.     

基于BP神经网络和RBF神经网络预测老年痴呆症疾病进展的对比研究

目的:比较反向传播算法(BP)神经网络和径向基函数(RBF)神经网络预测老年痴呆症疾病进展的效果。方法:以老年痴呆症随访数据为研究对象,以性别、年龄、受教育程度、有无高血压、有无高胆固醇、有无心脏病、有无中风史、有无家族史8个指标作为输入变量,以五年随访的MMSE差值为输出变量,构建基于BP神经网络和RBF神经网络的老年痴呆症疾病进展预测模型。结果:与BP神经网络模型相比,RBF神经网络预测的结果更好,能够有效地预测老年痴呆症疾病进展。结论:神经网络模型将老年痴呆症疾病进展预测问题转化为随访数据中相关测量指标与MMSE差值的非线性问题,为复杂的老年痴呆症疾病进展预测提供了新思路。     

Optimization of auto-induction medium for G-CSF production by Escherichia coli using artificial neural networks coupled with genetic algorithm

Granulocyte colony-stimulating factor (G-CSF) is a cytokine widely used in cancer patients receiving high doses of chemotherapeutic drugs to prevent the chemotherapy-induced suppression of white blood cells. The production of recombinant G-CSF should be increased to meet the increasing market demand. This study aims to model and optimize the carbon source of auto-induction medium to enhance G-CSF production using artificial neural networks coupled with genetic algorithm. In this approach, artificial neural networks served as bioprocess modeling tools, and genetic algorithm (GA) was applied to optimize the established artificial neural network models. Two artificial neural network models were constructed: the back-propagation (BP) network and the radial basis function (RBF) network. The root mean square error, coefficient of determination, and standard error of prediction of the BP model were 0.0375, 0.959, and 8.49 %, respectively, whereas those of the RBF model were 0.0257, 0.980, and 5.82 %, respectively. These values indicated that the RBF model possessed higher fitness and prediction accuracy than the BP model. Under the optimized auto-induction medium, the predicted maximum G-CSF yield by the BP-GA approach was 71.66 %, whereas that by the RBF-GA approach was 75.17 %. These predicted values are in agreement with the experimental results, with 72.4 and 76.014 % for the BP-GA and RBF-GA models, respectively. These results suggest that RBF-GA is superior to BP-GA. The developed approach in this study may be helpful in modeling and optimizing other multivariable, non-linear, and time-variant bioprocesses.     

Prediction model of PSO-BP neural network on coliform amount in special food

Special food safety supervision by means of intelligent models and methods is of great significance for the health of local people and tourists. Models like BP neural network have the problems of low accuracy and poor robustness in food safety prediction. So, firstly, the principal component analysis was used to extract the key factors that influenced the amount of coliform communities, which was applied to reduce the dimension of this model as the input variable of BP neural network. Secondly, both the particle swarm optimization (PSO) and BP neural network were implemented to optimize initial weights and threshold to obtain the optimal parameter, and a model was constructed to predict the amount of coliform bacteria in Dai Special Snacks, Sa pie, based on PSO-BP neural network model. Finally, the predicted value of the model is verified. The results show that MSE is 0.0097, MAPE is 0.3198 and MAE is 0.0079, respectively. It was clear that PSO-BP model was better accuracy and robustness. That means, this model can effectively predict the amount of coliform. The research has important guiding significance for the quality and the production of Sa pie.     

Clustering-neural network models for freeway work zone capacity estimation

Two neural network models, called clustering-RBFNN and clustering-BPNN models, are created for estimating the work zone capacity in a freeway work zone as a function of seventeen different factors through judicious integration of the subtractive clustering approach with the radial basis function (RBF) and the backpropagation (BP) neural network models. The clustering-RBFNN model has the attractive characteristics of training stability, accuracy, and quick convergence. The results of validation indicate that the work zone capacity can be estimated by clustering-neural network models in general with an error of less than 10%, even with limited data available to train the models. The clustering-RBFNN model is used to study several main factors affecting work zone capacity. The results of such parametric studies can assist work zone engineers and highway agencies to create effective traffic management plans (TMP) for work zones quantitatively and objectively.