Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium

In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).

Using Multiple Linear Regression and Artificial Neural Network Techniques for Predicting CCR5 Binding Affinity of Substituted 1-(3, 3-Diphenylpropyl)-Piperidinyl Amides and Ureas

Quantitative structure–activity relationship (QSAR) models were developed to predict for CCR5 binding affinity of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using multiple linear regression (MLR) and artificial neural network (ANN) techniques. A model with four descriptors, including Hydrogen-bonding donors HBD(R7), the partition coefficient between n-octanol and water logP and logP(R1) and Molecular weight MW(R7), showed good statistics both in the regression and artificial neural network with a configuration of (4-3-1) by using Bayesian and Leven-berg-Marquardt Methods. Comparison of the descriptor’s contribution obtained in MLR and ANN analysis shows that the contribution of some of the descriptors to activity may be non-linear.

Application of artificial neural networks for unfolding neutron spectra by using a scintillation detector

The unfolding of neutron spectra from the pulse height distribution measured by a BC501A scintillation detector is accomplished by the application of artificial neural networks (ANN). A simple linear neural network without biases and hidden layers is adopted. A set of monoenergetic detector response functions in the energy range from 0.25 MeV to 16 MeV with an energy interval of 0.25 MeV are generated by the Monte Carlo code O5S in the training phase of the unfolding process. The capability of ANN was demonstrated successfully using the Monte Carlo data itself and experimental data obtained from the Am-Be neutron source and D-T neutron source.

人工神经网络在大肠癌患者术后5年生存期预测中的应用

目的探讨人工神经网络在大肠癌患者术后5年生存期预测中的应用。方法在大肠癌生物医学研究的基础上,建立用于大肠癌患者术后5年生存期预测的人工神经网络模型,通过随访收集到53例数据,按照4:1的比例,随机分为训练集和测试集,用训练集训练网络,用测试集检验网络。结果人工神经网络可以用于大肠癌患者术后5年生存期的预测。结论神经网络在生存分析中有很大的灵活性;在模型中可以容纳非线性效应,不需要对数据的随机特征如分布等作出假设,不要求满足H0假定,有较广泛的应用前景。

La_(1-x-y-z)Ce_xPr_yNd_zB_5电极材料组成的优化与设计

利用正交设计方法对ＡＢ５型混合稀土镍系合金电极材料Ｌａ１－ｘ－ｙ－ｚＣｅｘＰｒｙＮｄｚＢ５的容量、高倍率放电性能、循环寿命和电压平台等性能进行了实验测定，以进一步优化电极材料的Ａ组元，提高电极性能。通过模式识别对实验数据进行分类并据此设计了新的样本。利用人工神经网络方法对新样本的电性能进行了预报，结果与实验值基本一致，新设计的样本点具有比较好的电性能。

AB_5型储氢合金初始放电容量的预测

将模拟退火算法用于神经网络,对AB5_型储氢合金的初始放电容量性能进行预测。通过实验确定了冷却进度表中各项参数并讨论了其对网络预测性能的影响,提出了实用的冷却制度;将模拟退火算法与传统的梯度下降法结合,用于人工神经网络的优化与预测,得到了预测效果等与迭代时间性能更好的神经网络。

Predictive model for 5.year mortality after breast cancer surgery in Taiwan residents

Background:Few studies of breast cancer surgery outcomes have used longitudinal data for more than 2 years.This study aimed to validate the use of the artificial neural network(ANN)model to predict the 5?year mortality of breast cancer patients after surgery and compare predictive accuracy between the ANN model,multiple logistic regression(MLR)model,and Cox regression model.Methods:This study compared the MLR,Cox,and ANN models based on clinical data of 3632 breast cancer patients who underwent surgery between 1996 and 2010.An estimation dataset was used to train the model,and a validation dataset was used to evaluate model performance.The sensitivity analysis was also used to assess the relative signifi?cance of input variables in the prediction model.Results:The ANN model significantly outperformed the MLR and Cox models in predicting 5?year mortality,with higher overall performance indices.The results indicated that the 5?year postoperative mortality of breast cancer patients was significantly associated with age,Charlson comorbidity index(CCI),chemotherapy,radiotherapy,hormone therapy,and breast cancer surgery volumes of hospital and surgeon(all P<0.05).Breast cancer surgery volume of surgeon was the most influential(sensitive)variable affecting 5?year mortality,followed by breast cancer surgery volume of hospital,age,and CCI.Conclusions:Compared with the conventional MLR and Cox models,the ANN model was more accurate in predict?ing 5?year mortality of breast cancer patients who underwent surgery.The mortality predictors identified in this study can also be used to educate candidates for breast cancer surgery with respect to the course of recovery and health outcomes.

多硝基含能化合物5 s爆发点的定量构效关系

为精确预测含能材料的5 s爆发点,解决大量新型含能材料实验测试难度大、安全数据不全等问题,基于定量构效关系(QSPR)原理,研究多硝基含能材料分子结构与5 s爆发点(ln T E)间的内在定量关系。应用集成学习算法随机森林(RF)筛选出8个对5 s爆发点具有显著影响的分子描述符;采用人工神经网络(ANN)建立90种多硝基含能材料5 s爆发点的预测模型。73种训练集的复决定系数为0.918,均方根误差为0.036,平均绝对误差为0.027。17个检验样本的复决定系数为0.903,均方根误差为0.061,平均绝对误差为0.053。对模型进行了验证以及应用域评价。结果表明:模型具备较好的预测性和泛化性能,可用于对多硝基含能材料的5 s爆发点进行精度较高的预测,有效解决现有含能材料的爆发点数据不够全面的问题,为相关产品研制与生产安全提供参考。

A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improving towards diversification and intelligence.However,the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive enough.Hence,a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the paper.Firstly,four classical neural network models are illustrated:Back Propagation(BP)network,Deep Belief Network(DBN),LeNet5 network,and olfactory bionic model(KIII model),and the neuron transmission mode and equation,network structure,and weight updating principle of the models are analyzed qualitatively.The analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models,and the LeNet5 network simulates the nervous system in depth.Secondly,evaluation indexes of ANN are constructed from the perspective of bionics in this paper:small-world,synchronous,and chaotic characteristics.Finally,the network model is quantitatively analyzed by evaluation indexes from the perspective of bionics.The experimental results show that the DBN network,LeNet5 network,and BP network have synchronous characteristics.And the DBN network and LeNet5 network have certain chaotic characteristics,but there is still a certain distance between the three classical neural networks and actual biological neural networks.The KIII model has certain small-world characteristics in structure,and its network also exhibits synchronization characteristics and chaotic characteristics.Compared with the DBN network,LeNet5 network,and the BP network,the KIII model is closer to the real biological neural network.

小波变换-人工神经网络用于烟酸片的近红外快速、无损定量测定

目的：研究采用近红外漫反射光谱法结合小波变换-人工神经网络算法用于烟酸片的无损含量测定。方法：所有光谱通过载有InGaAs检测器和光纤探头的傅立叶变换近红外装置获得。所有样品在12000cm^-1到4000cm^-1扫描，每个样品光谱扫描64次，光谱预处理采用一阶导数。特定波段7999．599～3999．8cm^-1用于建模，线性范围：50％～150％。结果：预示集平均回收率100．06％，RSD％为1．5。为确证近红外漫反射光谱法应用的可行性，预示集与参比方法紫外分光光度法测定结果进行比较，采用配对比较t检验，近红外法与紫外光谱法比较无显著性差异。结论：近红外漫反射光谱法快速，简便，无损，能够用于烟酸片含量测定。

利用模糊神经网络识别墙体材料的种类

由超声脉冲法获得４种检测信息———声速、波幅、频率和波形，从中提取３个特征量：平均声速、声速的离差系数和波幅的离差系数，利用模糊分析方法和人工神经网络相结合对ＧＲＣ、加气混凝土、空心砖、实心砖、砌块５种墙体材料进行种类识别，形成一个智能化的、具有类似人脑功能的系统．经现场实测证明。

Sol-gel法制备WO_3电致变色薄膜

本文采用Ｓｏｌ－ｇｅｌ法，用Ｎａ２ＷＯ４水溶液经质子交换树脂获得ＷＯ３溶胶，用浸涂法制备无色透明非晶ＷＯ３薄膜．ＷＯ３非晶薄膜显微结构疏松，表面有微裂纹，是良好的快离子导体，加负电压使其注入Ｌｉ＋离子引起钨离子还原而着色．经５００℃热处理后的ＷＯ３薄膜主要含斜方ＷＯ３晶相，显微结构致密，表面微裂纹闭合，不利于Ｌｉ＋离子注入，几乎无变色现象．

径向基函数网络模型在水质评价中的应用

建立了一个水库水质评价的径向基函数人工神经网络 ( RBF- ANN)模型 ,应用一种简便、快速的最小二乘算法训练 RBF- ANN.在水库水质评价的应用结果表明 ,RBF- ANN模型及其算法是合理。

基于人工神经网络的在线设备状态监测系统的研究(英文)

研究了基于人工神经网络在线设备状态监测系统，简要介绍了人工神经网络的基础理论，描述了基于人工神经网络在线设备状态监测系的结构和工作过程，给出该系统对卷烟机MK9-5的状态监测和故障诊断的结果。实验结果表明，将多层前馈人工种经网络用于设备在线状态监测具有较好的效果，并可对设备故障进行可靠诊断。

小波分析和人工神经网络方法用于重叠色谱峰的解析

将神经网络与小波分析相结合,提出一种利用小波变换提取重叠色谱峰信息,再用人工神经网络解析的方法,为色谱峰重叠难于分开物质的测定提供一种方法,提高了色谱分析的准确度,并将此方法应用于高效液相色谱中,建立了混合体系中3,4-二甲酚和2,5-二甲酚同时测定的新化学计量学方法.

Performance and uncertainty analysis of a short-term climate reconstruction based on multi-source data in the Tianshan Mountains region,China

Short-term climate reconstruction,i.e.,the reproduction of short-term(several decades)historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area,can extend the length of climatic time series and offset the shortage of observations.This can be used to assess regional climate change over a much longer time scale.Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5(CMIP5)dataset for the period of 1850–2000,the Climatic Research Unit(CRU)dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region(TMR)of China during the period of 1961–2011,we calibrated and validated monthly average temperature(MAT)and monthly accumulated precipitation(MAP)in the TMR using the delta,physical scaling(SP)and artificial neural network(ANN)methods.Performance and uncertainty during the calibration(1971–1999)and verification(1961–1970)periods were assessed and compared using traditional performance indices and a revised set pair analysis(RSPA)method.The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables,different data sources,and/or different methods used.According to traditional performance indices,both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999.However,the results differed from those obtained by the RSPA method.This showed that the CRU dataset produced a low degree of uncertainty(positive connection degree)during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset.Overall,the calibrated and verified MAP had a high degree of uncertainty(negative connection degree)regardless of the dataset or reconstruction method used.Therefore,the reconstructed time series of MAT for the period of 1850(or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study.The results of this study will be useful for short-term(several decades)regional climate reconstruction and longer-term(100 a or more)assessments of regional climate change.

Climate change impacts on the streamflow of Zarrineh River,Iran

Zarrineh River is located in the northwest of Iran,providing more than 40%of the total inflow into the Lake Urmia that is one of the largest saltwater lakes on the earth.Lake Urmia is a highly endangered ecosystem on the brink of desiccation.This paper studied the impacts of climate change on the streamflow of Zarrineh River.The streamflow was simulated and projected for the period 1992-2050 through seven CMIP5(coupled model intercomparison project phase 5)data series(namely,BCC-CSM1-1,BNU-ESM,CSIRO-Mk3-6-0,GFDL-ESM2G,IPSL-CM5A-LR,MIROC-ESM and MIROC-ESM-CHEM)under RCP2.6(RCP,representative concentration pathways)and RCP8.5.The model data series were statistically downscaled and bias corrected using an artificial neural network(ANN)technique and a Gamma based quantile mapping bias correction method.The best model(CSIRO-Mk3-6-0)was chosen by the TOPSIS(technique for order of preference by similarity to ideal solution)method from seven CMIP5 models based on statistical indices.For simulation of streamflow,a rainfall-runoff model,the hydrologiska byrans vattenavdelning(HBV-Light)model,was utilized.Results on hydro-climatological changes in Zarrineh River basin showed that the mean daily precipitation is expected to decrease from 0.94 and 0.96 mm in 2015 to 0.65 and 0.68 mm in 2050 under RCP2.6 and RCP8.5,respectively.In the case of temperature,the numbers change from 12.33℃ and 12.37℃ in 2015 to 14.28℃ and 14.32℃ in 2050.Corresponding to these climate scenarios,this study projected a decrease of the annual streamflow of Zarrineh River by half from 2015 to 2050 as the results of climatic changes will lead to a decrease in the annual streamflow of Zarrineh River from 59.49 m^(3)/s in 2015 to 22.61 and 23.19 m^(3)/s in 2050.The finding is of important meaning for water resources planning purposes,management programs and strategies of the Lake's endangered ecosystem.

Synthesized Multi-Method to Detect and Classify Epileptic Waves in EEG

In order to sufficiently exploit the advantages of different signal processing methods, such as wavelet transformation (WT), artificial neural networks (ANN) and expert rules (ER),a synthesized multi-method was introduced to detect and classify the epileptic waves in the EEG data. Using this method, at first, the epileptic waves were detected from pre-processed EEG data at different scales by WT, then the characteristic parameters of the chosen candidates of epileptic waves were extracted and sent into the well-trained ANN to identify and classify the true epileptic waves,and at last, the detected epileptic waves were certificated by ER. The statistic results of detection and classification show that, the synthesized multi-method has a good capacity to extract signal features and to shield the signals from the random noise. This method is especially fit for the analysis of the biomedical signals in biomedical engineering which are usually non-placid and nonlinear.

一种神经网络专家系统的故障诊断方法

提出将设备故障分为底层结构故障和高层功能故障的新概念，在此基础上研究用人工神经网络（ＡＮＮ） 诊断设备结构故障和用专家系统（ＥＳ） 诊断设备功能故障的设备故障诊断新方法，同时讨论了故障特征信号的获取和故障知识的组织，并以ＭＫ９ － ５ 卷烟机为对象进行实验研究，取得了较好的效果。

小波变换和神经网络用于红外光谱定量分析

为扣除开放光路法红外光谱中的背景干扰 ,发挥开放光路法的优势 ,提出了针对多组分气体体系的小波变换(WT)和人工神经网络 (ANN)相结合的红外光谱定量方法。该方法在数据处理阶段利用小波变换方法扣除了样品光谱中的背景干扰 ,然后通过计算谱峰强度和组分浓度之间相关系数的方法确定了待测组分的特征峰 ,最后利用 ANN技术实现了定量分析。该方法用现场实测光谱进行了检验。结果显示其综合性能优于其它几种常用的方法 。