基于ANNs耦合GA算法的煤层含气量预判

为了对煤层含气量进行有效分析,以实现煤层气可靠抽采及瓦斯灾害预防,提出了遗传算法(GA)优化人工神经网络(ANNs)煤层含气量的预判方法.GA算法通过对ANNs网络的权值及阈值的寻优,构建了基于ANNs耦合GA算法的煤层含气量非线性预判模型,并结合现场实测数据进行了分析.仿真结果显示:耦合模型的预判的最大相对误差为1.47%,较之其他模型具有更高的预判精度和更好的泛化能力,能实现煤层含气量的有效预测.

利用ANNS的空间信息处理服务智能集成算法

ANNS,即人工神经网络因其自身对非线性复杂关系的逼近和基于网络神经元的信息鲁棒性与容错性近年来被广泛应用在社会生产、生活的多个领域,特别是网络信息技术领域。为了进一步提高网络数据资源的利用效率,通过对ANNS的服务语义和匹配算法进行描述,进而对ANNS的空间信息在服务智能集成算法中的应用展开了深入研究。

Predicting pollutant removal in constructed wetlands using artificial neural networks(ANNs)

Growth in urban population,urbanisation,and economic development has increased the demand for water,especially in water-scarce regions.Therefore,sustainable approaches to water management are needed to cope with the effects of the urbanisation on the water environment.This study aimed to design novel configurations of tidal-flow vertical subsurface flow constructed wetlands(VFCWs)for treating urban stormwater.A series of laboratory experiments were conducted with semi-synthetic influent stormwater to examine the effects of the design and operation variables on the performance of the VFCWs and to identify optimal design and operational strategies,as well as maintenance requirements.The results show that the VFCWs can significantly reduce pollutants in urban stormwater,and that pollutant removal was related to specific VFCW designs.Models based on the artificial neural network(ANN)method were built using inputs derived from data exploratory techniques,such as analysis of variance(ANOVA)and principal component analysis(PCA).It was found that PCA reduced the dimensionality of input variables obtained from different experimental design conditions.The results show a satisfactory generalisation for predicting nitrogen and phosphorus removal with fewer variable inputs,indicating that monitoring costs and time can be reduced.

Hydrodynamic Performance Prediction of Stepped Planing Craft Using CFD and ANNs

In the present paper,the hydrodynamic performance of stepped planing craft is investigated by computational fluid dynamics(CFD)analysis.For this purpose,the hydrodynamic resistances of without step,one-step,and two-step hulls of Cougar planing craft are evaluated under different distances of the second step and LCG from aft,weight loadings,and Froude numbers(Fr).Our CFD results are appropriately validated against our conducted experimental test in National Iranians Marine Laboratory(NIMALA),Tehran,Iran.Then,the hydrodynamic resistance of intended planing crafts under various geometrical and physical conditions is predicted using artificial neural networks(ANNs).CFD analysis shows two different trends in the growth rate of resistance to weight ratio.So that,using steps for planing craft increases the resistance to weight ratio at lower Fr and decreases it at higher Fr.Additionally,by the increase of the distance between two steps,the resistance to weight ratio is decreased and the porpoising phenomenon is delayed.Furthermore,we obtained the maximum mean square error of ANNs output in the prediction of resistance to weight ratio equal to 0.0027.Finally,the predictive equation is suggested for the resistance to weight ratio of stepped planing craft according to weights and bias of designed ANNs.

Agent Modeling of User Preferences Based on Fuzzy Classified ANNs in Automated Negotiation

In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user requirements to implement ecommerce activities like automated transactions.The difficulty lies in the uncertainty of user preferences that include uncertain description and contents,non-linear and dynamic variability.In this paper,fuzzy language was used to describe the uncertainty and combine with multiple classified artificial neural networks(ANNs) for self-adaptive learning of user preferences.The refinement learning results of various negotiation contracts' satisfaction degrees in the extent of fuzzy classification can be achieved.Compared to unclassified computation,the experimental results illustrate that the learning ability and effectiveness of agents have been improved.

Application of ANNs Model with the SDSM for the Hydrological Trend Prediction in the Sub-catchment of Kurau River, Malaysia

The paper describes the application of SDSM （statistical downscaling model） and ANNs （artificial neural networks） models for prediction of the hydrological trend due to the climate-change. The SDSM has been calibrated and generated for the possible future scenarios of meteorological variables, which are temperature and rainfall by using GCMs （global climate models）. The GCM used is SRES A2. The downscaled meteorological variables corresponding to SDSM were then used as input to the ANNs model calibrated with observed station data to simulate the corresponding future streamflow changes in the sub-catchment of Kurau River. This study has discovered the hydrological trend over the catchment. The projected monthly streamflow has shown a decreasing trend due to the increase in the, mean of temperature for overall months, except the month of August and November.

ANFIS and ANNs model for prediction of moisture diffusivity and specific energy consumption potato,garlic and cantaloupe drying under convective hot air dryer

The main purpose of this study was to develop and apply an adaptive neuro-fuzzy inference system(ANFIS)and Artificial Neural Networks(ANNs)model for predicting the drying characteristics of potato,garlic and cantaloupe at convective hot air dryer.Drying experiments were conducted at the air temperatures of 40,50,60 and 70
C and the air speeds of 0.5,1 and l.5 m/s.Drying properties were including kinetic drying,effective moisture diffusivity(Deff)and specific energy consumption(SEC).The highest value of Deff obtained 9.76×10^-9,0.13×10^-9 and 9.97×10^-10 m^2/s for potato,garlic,and cantaloupe,respectively.The lowest value of SEC for potato,garlic,and cantaloupe were calculated 1.94
105,4.52
105 and 2.12
105 kJ/kg,respectively.Results revealed that the ANFIS model had the high ability to predict the Deff(R^2=0.9900),SEC(R^2=0.9917),moisture ratio(R^2=0.9974)and drying rate(R^2=0.9901)during drying.So ANFIS method had the high ability to evaluate all output as compared to ANNs method.

Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs

It is especially significant for a manufacturing company to select a proper maintenance policy because maintenance impacts not only on economy, reliability and availability but also on personnel safety. This article re- ports on research in the backlash error data interpretation and compensation for intelligent predictive maintenance in machine centers based on artificial neural networks （ANNs）. The backlash error, measurement system and prediction methods are analyzed in detail. The result indicates that it is possible to predict and compensate for the backlash error in both forward and backward directions in machine centers.

Artificial neural network (ANNs) and mathematical modelling of hydration of green chickpea

The present study was aimed to model the hydration characteristics of green chickpea(GC)using mathematical modelling and examine predictive ability of artificial neural network(ANN)modelling.Hydration of GC was performed at different temperatures 25,35,45,55 and 65℃.Different mathematical models were tested for the hydration at different temperatures.In ANN modelling,the hydration time and hydration temperature were used as input variables and moisture ratio,moisture content and hydration ratio were taken as output variables.Peleg model best described the hydration behavior at 25℃;while hydration at high-temperature was better described by Page model and Ibarz et al.model.The optimum temperature obtained for hydration was 35℃.Effective mass diffusion coefficient(D_(e))increased from 1.5510^(-11)-1.7910^(-9) m^(2)/s with the increase in the hydration temperature.The low activation energy(39.66 kJ/moL)shows the low-temperature sensitiveness of GC.Low temperature hydration(25℃)required higher time(>200 min)to achieve the equilibrium moisture content(EMC),however high temperature hydration(35–65℃)reduced the EMC time(150 min).ANN was used to predict the hydration behavior and K fold cross validation was performed to check the over fitting of ANN model.Results show that the LOGSIGMOID transfer function showed better performance when used at the hidden layer input node in conjunction to both PURELIN and TANSIGMOID.TANSIGMOID was found suitable for moisture ratio(MR)and hydration ratio(HR)prediction,as opposed to PURELIN for moisture content(MC)data.Satisfactory model prediction was obtained when the number of neurons in the hidden layer for MC,MR and HR was 12,8 and 15,respectively.Mathematical and ANN modelling results are useful to improve/predict the MC,MR and HR during hydration process of GC at different temperature and other similar process.

基于ANN和XGB算法的锈蚀钢筋混凝土高温粘结强度预测方法

为准确评估锈蚀钢筋混凝土(CRC)结构在突发火灾下的结构承载力,锈蚀钢筋混凝土高温粘结强度的统一预测方法研究亟待开展。然而,粘结退化机理复杂,粘结因素众多,实验方法不能考虑所有粘结因素的相关复杂关系的影响。在现有大量试验数据的基础上,采用机器学习方法可以有效地通过数据建立输入和输出特征之间的回归关系。该文利用ANN和XGB两种机器学习算法建立了一个统一的锈蚀钢筋混凝土高温粘结强度预测模型。基于612组高温锈蚀钢筋混凝土的试验研究数据,进行模型训练和测试。结果表明:ML模型的预测结果与实验结果十分吻合。此外,针对机器学习算法本身存在的黑盒子问题,使用SHAP方法来解决锈蚀钢筋混凝土高温粘结强度预测过程中的模型可解释性问题。同时,还将ML模型的计算结果与三种理论计算公式的结果进行了比较,结果表明:ML模型具有明显的优势。新构建的混合机器学习模型很有可能成为准确评估CRC结构经受高温后的损伤程度问题的新选择。

基于ANN的RECFST短柱轴压承载力预测

目的针对相关设计规范和文献在计算圆端形截面钢管混凝土短柱轴压承载力上的局限性,开发高精高效的轴压承载力预测模型。方法首先,基于国内外已有的RECFST短柱轴压试验研究结果建立有限元模型,并通过验证;其次,基于Python脚本批量生成有限元模型,建立涵盖广泛输入参数的数据集;然后,利用数据集开发高精度的ANN模型并与相关规范和文献结果进行比较;最后,基于ANN模型开发GUI图形用户界面工具。结果ANN模型预测值与试验结果之比的平均值N ANN/N u=0.98,模型预测误差远低于相关规范和文献公式预测误差;ANN模型的均方误差K MSE=7.3734×10-7,总数据样本回归值R=0.99963,表明了ANN模型的有效性以及预测结果的精确性。结论ANN模型可以准确预测RECFST短柱的轴压承载力,基于模型开发的GUI工具简便实用。

A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

基于SEM与ANN混合方法的社交问答平台用户转移行为

社交问答平台得到了用户的广泛使用,但由于各平台的功能类似,用户转移起来较为容易,这将导致用户流失。基于PPM模型,研究社交问答平台用户转移行为。采集447份有效数据,采用SEM(结构方程模型)与ANN(人工神经网络)混合方法进行分析。结果发现:推力因素(不满意度、厌倦性)和拉力因素(内容质量、用户体验)正向影响用户的转移意向,锚定因素(转移成本)负向影响转移意向,且负向调节推力因素和拉力因素的作用。ANN结果显示:不满意度是影响转移意向的最重要因素。因此,社交问答平台需要提高内容质量,改善用户体验,降低用户的不满意度和厌倦性,从而防止用户的转移行为,实现用户保持。

基于GA改进ANN算法的车载网控系统故障诊断

车载网控系统是保证运行安全的一类重要控制设备,也是确保系统稳定运行的核心部件。为了提高车载网控系统故障诊断效率,通过遗传算法(GA)具有的全局寻优功能来实现对神经网络初始阈值与权值的优化,把寻优结果代到神经网络内完成训练过程;使ANN泛化方法具有的映射性能获得充分利用可以防止产生局部极小值情况,获得更高的分类精度;利用实例分析方式测试车载故障诊断过程的有效性。研究结果表明:采用GA改进ANN算法可以有效优化平均误差及数据正确率,有效降低迭代次数,表明可以通过GA改进ANN方法来提升神经网络运算性能。经过遗传算法优化处理的ANN在训练过程中可以获得比初始ANN更快时收敛速率。

基于人工神经网络智能算法的9310钢本构模型优化

目的研究9310钢在变形温度为800~1200℃、应变速率为0.01~50s-1和高度压下量为70%条件下的热变形行为,建立预测效果相对较好的9310钢本构模型。方法使用Gleeble-3800热模拟机对9310钢进行等温恒应变速率热压缩实验,基于热压缩实验数据,分析了应变速率对9310钢流动软化效应的影响,建立了考虑应变补偿的Arrhenius本构模型与支持向量回归(SVR)本构模型,并进行了模型精度分析,之后引入人工神经网络(ANN)智能算法优化了Arrhenius本构模型。结果与变形温度相比,应变速率对9310钢流动软化效应的影响更为显著。相较于支持向量回归(SVR)本构模型,考虑应变补偿的Arrhenius本构模型精度更高,其相关系数R为0.9934,平均相对误差(AARE)和均方误差(MSE)分别为0.0556和89.362,它在预测高应变速率(1、10、50 s-1)流动应力时出现了较大偏差,经ANN智能算法优化后,相关系数R提高至0.9991,AARE和MSE分别降至0.0199和9.998,且绝对误差在±10MPa以内的预测流动应力占比为98.34%。结论在低应变速率(0.01 s-1)下软化效应更强,在高应变速率(10 s-1)下再结晶程度较低,软化效应较弱。ANN智能算法优化后的Arrhenius本构模型具有较高的精度,能较准确地预测9310钢的流动行为。

基于STM32的AI智能农业系统

本项目设计了以STM32F103ZET6单片机为控制核心的AI智能农业系统,系统具有多个采集数据的传感器,采集到的数据通过WiFi通信显示在云端上,可通过手机小程序查看植株的环境数据。当检测到的特定数据超过设定的阈值时或者根据摄像头处理识别植株的生长状态时,单片机会主动开启调控。实验结果表明:该系统性能稳定,能够保证环境数据指标控制在作物生长适宜的范围内,使系统简单化。

ANN模型与分段线性插值及回归模型的比较及应用

对ANN模型、分段线性插值模型和非线性回归模型从原理上进行了比较,ANN模型易于构建各影响因素与因变量间复杂关系,非线性回归模型和分段线性插值模型可以将自变量与因变量间的关系通过表达式直观表达。以荆江三口分流量与枝城流量的关系为应用算例,采用相关系数、纳什效率系数、均方根误差和平均绝对误差等4个评价指标对3个模型的拟合精度和误差大小进行了比较。结果表明:3个模型均可应用于模拟枝城流量与荆江三口分流量的关系,但3个模型的计算值与实际值间的误差大小存在差异,从4个评价指标综合来看,ANN模型计算值与实测值的误差最小,分段线性插值模型次之,回归模型计算精度相对较低。

人工智能助力Fenton法降解间甲酚废水的过程优化研究

采用Fenton氧化法进行人工智能芬顿氧化处理间甲酚废水实验,考察了Fe^(2+)质量浓度、H2O2体积分数、初始pH、反应时间和间甲酚初始质量分数对降解间甲酚反应的影响,利用响应面法(RSM)和人工神经网络(ANN)分别确定降解间甲酚的最佳方案,同时对TOC去除率的关系进行拟合优化对比。结果表明,利用ANN模型并采用枚举法获取的最佳优化条件:Fe^(2+)质量浓度为0.66 g/L、H_(2)O_(2)体积分数为6.00 mL/L、初始pH为3、反应时间为23.37 min、间甲酚初始质量分数为50μg/g,此时,TOC去除率为48.14%,优于响应面法的32.16%。

基于机器学习的超高性能混凝土成本优化

近年来,超高性能混凝土(UHPC)凭借其优异的力学性能和耐久性能成为热点研究方向之一,但高昂的成本始终限制其在工程中的应用。提出了一种基于机器学习的超高性能混凝土配合比优化的方法,以降低UHPC的成本。为实现这一目标,首先通过人工神经网络(ANN)建立了UHPC 28 d抗压强度与扩展度的预测模型,再以其为约束条件,同时考虑UHPC组分含量约束、组分比例约束,通过遗传算法(GA)降低UHPC的成本。研究结果表明,ANN模型的预测结果与实验结果的误差在10%以内,具有良好的预测精度;遗传算法优化后的UHPC成本降低至838.8美元,低于文献中1000美元的成本。