基于ANFIS的太阳能-空气源热泵供暖系统温度控制研究

针对太阳能-空气源热泵供暖系统运行环境复杂多变,模糊控制器的设计高度依赖人工经验的问题,提出一种基于自适应神经模糊推理系统(ANFIS)改进的模糊控制方法。该方法利用ANFIS在复杂系统建模中的优势,结合供暖系统温度响应特性和实际运行数据,建立联合供暖系统变工况ANFIS模型,生成与系统性能适配的模糊规则库并传递给模糊控制器执行。Matlab/Simulink仿真对比试验表明,与单一的模糊控制相比,该方法的控制精度提高了17.65%,调节时间缩短了36.4%,具有更高的控制精度和响应速度。

基于ANFIS系统的无刷直流电机控制仿真研究

无刷直流电机运用环境多变复杂,众多不确定因素都会影响无刷直流电机运行的稳定性和响应速度。传统的模糊控制参数调整困难、性能不稳定、计算开销大。针对无刷直流电机速度控制的优化,本文提出了一种采用评论家学习方案下循环全局反馈的ANFIS系统,其中包括三层级联神经网络和模糊控制算法的结合。ANFIS系统通过采样无刷直流电机实际转速,对比参考转速,实现对PID参数的预测;同时ANFIS系统采用双曲正切作为传递函数来调整权值,缩放共轭梯度法作为优化器来减小训练误差,减法聚类方法来提高预测的适应性和准确性。

基于ANFIS-IPD的飞行模拟器座椅舒适度评价模型

为提升现有飞行模拟器座椅舒适度,减轻驾驶员长时间训练的疲劳感,基于自适应神经模糊推理系统(Adaptive Network-based Fuzzy Inference System,ANFIS)和理想压力分布(Ideal Pressure Distribution,IPD)构建一种飞行模拟器座椅舒适度评价模型。通过座椅压力分布采集系统获取压力分布数据,采用IPD将所得数据归一化作为模型输入数据,同时收集受试人员主观评分作为模型输出数据。采用非均匀有理B样条曲线(Non-Uniform Rational B-Splines,NURBS)对座椅进行改进设计,将处理后的试验数据通过ANFIS工具训练模型进行验证,并用评价模型评估设计方案及优越性。结果表明:该评价模型对飞行模拟器座椅舒适度有较好的预测能力,为提升飞行模拟器座椅舒适度提供指导和参考。

基于ANFIS-LSSVM的计算颜色恒常性算法研究

计算颜色恒常性是指消除场景光源的影响从而再现物体真实颜色的能力。目前,深度神经网络的应用使颜色恒常性精度显著提高,但大多数深度学习算法训练时间长、计算复杂度高,且需要大量的训练样本。针对此问题,提出了一种结合自适应神经模糊推理系统(ANFIS)和最小二乘支持向量机(LSSVM)的简单有效的方法。该方法分为训练和预测两个阶段:在训练阶段,首先提取图像特征分别训练ANFIS、LSSVM两种初始光源估计模型,接着利用核函数变换将两种模型融合,然后利用预留训练样本进一步训练得到多元线性回归光源估计模型;在预测阶段,提取测试图像特征后,直接由训练所得模型预测得到该测试图像最终的场景光源颜色值。实验结果表明,与深度学习方法相比,本文所提方法计算复杂度较低,即使在小训练样本中也能有很好的光源估计性能。

工程车辆座椅悬架ANFIS控制策略研究

以某型工程车辆的座椅振动系统为对象,利用自学习和模糊推理(Adaptive Network-based Fuzzy Inference System,ANFIS),提出了基于ANFIS的座椅悬架振动半主动控制方法,解决了工程车辆工作过程中座椅振动过大的问题。首先,分析工程车辆座椅悬架的振动过程,建立座椅悬架的动力学模型;然后,加入前馈、反馈环节,设计座椅悬架的模糊神经控制系统;最后,分别观测振动激励频率不同、控制器控制对象不同和以实测座椅信号为输入时的控制效果。结果表明,模糊神经振动控制系统减小了座椅的振动能量,降低了座椅的振动频率,座椅振动位移均方根值为被动悬架座椅的52%~66%。

基于GA-ANFIS-FCM算法的电力能源预测

电力公司为居民和企业提供不间断的电力供应是其主要责任。预测电力能源需求总是保证电力供应的最佳方案。将遗传算法(GA)、聚类算法(FCM)和自适应神经模糊推理系统(ANFIS)相结合,构造了GA–ANFIS–FCM混合算法,并将算法用于电力能源预测。通过与独立的ANFIS–FCM模型比较,验证了GA–ANFIS–FCM3为最佳子模型。

基于ANFIS的多AUV协同定位系统量测异常检测方法

针对异常水声测距信息对多自主水下航行器(Autonomous underwater vehicles,AUV)协同定位系统的不利影响,以及传统故障检测方法在多水声测距信息交替混淆的情况下检测效率低的问题,提出一种基于自适应神经模糊推理系统(Adaptive neuro-fuzzy inference system,ANFIS)的量测异常检测方法.首先,分别建立与各水声测距系统相对应的ANFIS模型;然后,基于自适应容积卡尔曼滤波(Adaptive cubature Kalman filter,ACKF)和马氏距离构造反映量测异常的特征信息作为ANFIS的输入;其次,基于预定义的量测异常信息建立了初始混合数据库以训练ANFIS模型实现对量测异常的在线实时检测与隔离;最后,利用湖水实验数据进行了AUV协同定位仿真验证.实验结果表明该方法可以准确识别异常水声测距信息,与传统故障检测方法相比,误报率(False positive rate,FPR)与漏检率(False negative rate,FNR)均减少70%以上.

Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro-fuzzy inference system(ANFIS)

One of the most important characters of blasting,a basic step of surface mining,is rock fragmentation because it directly effects on the costs of drilling and economics of the subsequent operations of loading,hauling and crushing in mines.Adaptive neuro-fuzzy inference system(ANFIS)and radial basis function(RBF)show potentials for modeling the behavior of complex nonlinear processes such as those involved in fragmentation due to blasting of rocks.We developed ANFIS and RBF methods for modeling of sizing of rock fragmentation due to bench blasting by estimation of 80%passing size(K_(80))of Golgohar iron mine of Sirjan.Iran.Comparing the results of ANFIS and RBF models shows that although the statistical parameters RBF model is acceptable but ANFIS proposed model is superior and also simpler because ANFIS model is constructed using only two input parameters while seven input parameters used for construction of RBF model.

An Application Expert System for Evaluating Effective Factors on Trust in B2C WebsitesTrust, Security, ANFIS, Fuzzy Logic, Rule Based Systems, Electronic Commerce

In new environments of trading, customer's trust is vital for the extended progress and development of electronic commerce. This paper proposes that in addition to known factors of electronic commerce B2C websites such a design of websites, security of websites and familiarity of website influence customers trust in online transactions. This paper presents an application of expert system on trust in electronic commerce. Based on experts’ judgment, a frame of work was proposed. The proposed model applies ANFIS and Mamdani inference fuzzy system to get the desired results and then results of two methods were compared. Two questionnaires were used in this study. The first questionnaire was developed for e-commerce experts, and the second one was designed for the customers of electronic websites. Based on AHP method, Expert Choice software was used to determine the priority of factors in the first questionnaire, and MATLAB and Excel were used for developing the fuzzy rules. Finally, the fuzzy logical kit was used to analyze the generated factors in the model. Our study findings show that trust in EC transactions is strongly mediated by perceived security.

A Novel Approach in Estimation of the Soilcrete Column’s Diameter and Optimization of the High Pressure Jet Grouting Using Adaptive Neuro Fuzzy Inference System (ANFIS)

For achieving optimized jet grout parameters and W/C ratio it is concluded to set trial tests in constant local soil as the conclusion depends on local soil and presence of the extensive range of the effective parameters. Considering the benefits, due to abundance of the involved variables and the intrinsic geological complexity, this system follows a great expense in the trial and implementation phases. Utilizing the soft computing methods, this paper proposes a new approach to reduce or to eliminate the cost of the trial phase. Therefore, the Adaptive Neuro Fuzzy Inference System (ANFIS) was utilized to study the possibility of anticipating the diameter of the jet grout (Soilcrete) columns on the trial phase based on the Trial and Error procedure. Data were collected from several projects and formed three sets of data. Consequently, parameters were held constant (as input) and the diameters of the Soilcrete columns were recorded (as output). To increase the precision, aforementioned data sets were combined and ten different data sets were created and studied, with all the results being assessed in two different approaches. Accordingly, Gaussian Function results in a huge number of precise and acceptable outcomes among available functions. Based on the measurements, Gaussian Function achieves the values of the R which are frequently more than 0.8 and lower values of the RMSE. Therefore, utilizing Gaussian Function, mainly a congruent relation between the R and RMSE is experienced and it leads to close proximity of the actual and predicted values of the Soilcrete diameter.

Predicting ERP User Satisfaction―an Adaptive Neuro Fuzzy Inference System (ANFIS) Approach

ERP projects’ failing to meet user expectations is a serious problem. This research develops an Adaptive Neuro Fuzzy Inference System (ANFIS) model, to predict the key ERP outcome “User Satisfaction” using causal factors present during an implementation as predictors. Data for training and testing the models was from a cross section of firms that had implemented ERPs. ANFIS is compared with other prediction techniques, ANN and MLRA. The results establish that ANFIS is able to predict outcome well with an error (RMSE) of 0.277 and outperforms ANN and MLRA with errors of 0.85 and 0.86 respectively. This study is expected to provide guidelines to managers and academia to predict ERP outcomes ex ante, and thereby enable corrective actions to redirect ailing projects.

A Valorized Scheme for Failure Prediction Using ANFIS: Application to Train Track Breaking System

In the rolling stock sector, the ability to protect passengers, freight and services relies on heavy inborn maintenance. Initiating an accurate model suitable to foresee the change of attitude on components when operating rolling stock systems will assist in reducing lock down and favors heavy productivity. In that light, this paper showcases a suitable methodology to track degradation of components through the blinding of physic laws and artificial intelligent techniques. This model used to foresee failure deterioration rate and remaining useful life (RUL) speculation is case study to showcase its quality and perfection, within which behavioral data are obtained through simulated models initiated in Mathlab. For feature extraction and forecasting issues, different neuro-fuzzy inference systems are designed, learnt and authenticated with powerful outputs gained during this process.

基于ANFIS的电磁换向阀寿命预测

以电磁换向阀的出入口压降作为失效判据,从流量退化角度深入分析,进行电磁换向阀的寿命预测研究。首先使用改进的集合经验模态分解(Modified Ensemble Empirical Mode Decomposition,MEEMD)方法多尺度分解测试数据,以欧式距离的IMFs分量筛选规则完成噪声信号的降噪重构。随后利用时域分析、频域分析和时频域分析提取特征参数,并用核主元(Kernel Principal Component Analysis,KPCA)方法融合处理,经过3次指数平滑处理,构建了电磁换向阀退化评估指标。融合退化评估指标,训练自适应模糊神经网络(Adaptive Network-based Fuzzy Inference System,ANFIS),通过对未失效样本进行压降趋势预测,实现了电磁换向阀的寿命预测。结果显示,ANFIS的预测指标与实际指标的差异性较小,预测结果准确。

An Adaptive Neuro-Fuzzy Inference System to Improve Fractional Order Controller Performance

The design and analysis of a fractional order proportional integral deri-vate(FOPID)controller integrated with an adaptive neuro-fuzzy inference system(ANFIS)is proposed in this study.Afirst order plus delay time plant model has been used to validate the ANFIS combined FOPID control scheme.In the pro-posed adaptive control structure,the intelligent ANFIS was designed such that it will dynamically adjust the fractional order factors(λandµ)of the FOPID(also known as PIλDµ)controller to achieve better control performance.When the plant experiences uncertainties like external load disturbances or sudden changes in the input parameters,the stability and robustness of the system can be achieved effec-tively with the proposed control scheme.Also,a modified structure of the FOPID controller has been used in the present system to enhance the dynamic perfor-mance of the controller.An extensive MATLAB software simulation study was made to verify the usefulness of the proposed control scheme.The study has been carried out under different operating conditions such as external disturbances and sudden changes in input parameters.The results obtained using the ANFIS-FOPID control scheme are also compared to the classical fractional order PIλDµand conventional PID control schemes to validate the advantages of the control-lers.The simulation results confirm the effectiveness of the ANFIS combined FOPID controller for the chosen plant model.Also,the proposed control scheme outperformed traditional control methods in various performance metrics such as rise time,settling time and error criteria.

Automatic Heart Disease Diagnosis System Based on Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) Approaches

This paper aims to design and implement an automatic heart disease diagnosis system using?MATLAB. The Cleveland data set for heart diseases was used as the main database for training and testing the developed system. In order to train and test the Cleveland data set, two systems were developed. The first system is based on the Multilayer Perceptron (MLP) structure on the Artificial Neural Network (ANN), whereas the second system is based on the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach. Each system has two main modules, namely, training and testing,?where 80% and 20% of the Cleveland data set were randomly selected for training and testing?purposes respectively. Each system also has an additional module known as case-based module,?where the user has to input values for 13 required attributes as specified by the Cleveland data set,?in order to test the status of the patient whether heart disease is present or absent from that particular patient. In addition, the effects of different values for important parameters were investigated in the ANN-based and Neuro-Fuzzy-based systems in order to select the best parameters that obtain the highest performance. Based on the experimental work, it is clear that the Neuro-Fuzzy system outperforms the ANN system using the training data set, where the accuracy for each system was 100% and 90.74%, respectively. However, using the testing data set, it is clear that the ANN system outperforms the Neuro-Fuzzy system, where the best accuracy for each system was 87.04% and 75.93%, respectively.

基于AE-ANFIS的船舶柴油机故障诊断

柴油机作为船舶主要动力设备,在船舶行业应用极其广泛,但其工作环境恶劣,极易发生故障。为减小船舶航行时柴油机故障带来的经济损失,有必要对其进行故障诊断。通过柴油机实验台架模拟不同类型故障,并在柴油机缸盖处使用振动加速度传感器采集故障信号,选取在1缸缸盖处采集的信号作为样本数据进行数据分析。由于采集的原始信号是多激励源合成信号,其中包含传播噪声、环境噪声,为降低噪声对识别精度影响,首先使用变分模态分解(VariationalModeDecomposition,VMD)对信号进行分解降噪,把原始一维数据分解成能反映柴油机运行状态的多维数据;接着使用自编码器(Auto-Encode,AE)对分离信号进行特征提取,以降低分解信号间的干扰,提高识别准确率;再使用自适应模糊神经网络(Adaptive Fuzzy Neural Network,ANFIS)建立故障诊断模型,并将所提取特征作为诊断模型输入;最后根据诊断模型的识别准确度评价以上方法的可行性。

基于改进ANFIS的绝缘子紫外光斑评估方法

绝缘子运行状态的评估关乎到输电工程的安全运行。紫外成像技术提供了一种绝缘子评估的量化手段,为此,提出了一种基于改进自适应神经模糊推理系统(adaptiveneuro-fuzzyinference system,ANFIS)的绝缘子紫外光斑评估方法。首先,搭建了绝缘子污秽放电测试平台,开展了不同测试距离和增益下的绝缘子放电强度研究。其次,将增益以及紫外光斑面积作为训练数据,建立了基于贝叶斯推理的ANFIS模型。最后,进行了现场验证测试。结果表明,该方法具有良好的预测精度和测试效率,适用于绝缘子紫外成像量化评估,为绝缘子运行状态的评估提供了技术支撑。

基于ANFIS模型的草原土壤湿度预测

中国是一个资源大国,草地资源十分丰富,面积分布也在世界前列。草原生态系统不仅是维护我国生态系统稳定的重要屏障,同时还为我国的经济发展提供保障。近年来,快速发展的畜牧业使得草地退化严重,有的甚至出现了沙化现象。提供科学的草地管理方式迫在眉睫,故对土壤中的湿度预测对于草原的保护和开发具有重要的意义。本文对往年的统计数据进行分析,然后通过数学模型对2022、2023年的土壤湿度进行预测。首先对数据进行共线性分析,对于共线性强的数据采用Lasso回归的方法进行降维。之后用ARIMA时间序列方法对以往年月数据进行预测。最后建立输入(Lasso回归所筛选变量和土壤蒸发量变量)和输出(不同深度土壤湿度) ANFIS模型,对往年所测数据进行训练且整体数据集合训练拟合度均在85%以上,该模型的准确度较高。最后通过训练好的ANFIS模型预测2022年、2023年不同深度土壤湿度。