Aero-engine Thrust Estimation Based on Ensemble of Improved Wavelet Extreme Learning Machine

Aero-engine direct thrust control can not only improve the thrust control precision but also save the operating cost by reducing the reserved margin in design and making full use of aircraft engine potential performance.However,it is a big challenge to estimate engine thrust accurately.To tackle this problem,this paper proposes an ensemble of improved wavelet extreme learning machine(EW-ELM)for aircraft engine thrust estimation.Extreme learning machine(ELM)has been proved as an emerging learning technique with high efficiency.Since the combination of ELM and wavelet theory has the both excellent properties,wavelet activation functions are used in the hidden nodes to enhance non-linearity dealing ability.Besides,as original ELM may result in ill-condition and robustness problems due to the random determination of the parameters for hidden nodes,particle swarm optimization(PSO)algorithm is adopted to select the input weights and hidden biases.Furthermore,the ensemble of the improved wavelet ELM is utilized to construct the relationship between the sensor measurements and thrust.The simulation results verify the effectiveness and efficiency of the developed method and show that aero-engine thrust estimation using EW-ELM can satisfy the requirements of direct thrust control in terms of estimation accuracy and computation time.

A novel heterogeneous ensemble of extreme learning machinesand its soft sensing application

To obtain an accurate and robust soft sensor model in dealing with the increasingly complex industrial modeling data,an effective heterogeneous ensemble of extreme learning machines(HEELM)is proposed.Specifically,the kernel extreme learning machine(KELM)and four common extreme learning machine(ELM)models that have different internal activations are contained in the HEELM for enriching the diversity of sub-models.The number of hidden layer nodes of the extreme learning machine is determined by the trial and error method,and the optimal parameters of the kernel extreme learning machine model are determined by cross validation.Moreover,to obtain the best output of the ensemble model,least squares regression is applied to aggregate the outputs of all individual models.Two complex data sets of practical industrial processes are used to test the HEELM performance.The simulation results show that the HEELM has a high prediction accuracy.Compared with the individual ELM models,bagging ELM ensemble model,BP and SVM models,the prediction accuracy of the HEELM model is improved by 4.5%to 8.7%,and the HEELM model can obtain better generalization capability.

Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble

Precipitation is a significant index to measure the degree of drought and flood in a region,which directly reflects the local natural changes and ecological environment.It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy.In order to accurately predict precipitation,a new precipitation prediction model based on extreme learning machine ensemble(ELME)is proposed.The integrated model is based on the extreme learning machine(ELM)with different kernel functions and supporting parameters,and the submodel with the minimum root mean square error(RMSE)is found to fit the test data.Due to the complex mechanism and factors affecting precipitation change,the data have strong uncertainty and significant nonlinear variation characteristics.The mean generating function(MGF)is used to generate the continuation factor matrix,and the principal component analysis technique is employed to reduce the dimension of the continuation matrix,and the effective data features are extracted.Finally,the ELME prediction model is established by using the precipitation data of Liuzhou city from 1951 to 2021 in June,July and August,and a comparative experiment is carried out by using ELM,long-term and short-term memory neural network(LSTM)and back propagation neural network based on genetic algorithm(GA-BP).The experimental results show that the prediction accuracy of the proposed method is significantly higher than that of other models,and it has high stability and reliability,which provides a reliable method for precipitation prediction.

Fast cross validation for regularized extreme learning machine

A method for fast 1-fold cross validation is proposed for the regularized extreme learning machine （RELM）. The computational time of fast l-fold cross validation increases as the fold number decreases, which is opposite to that of naive 1-fold cross validation. As opposed to naive l-fold cross validation, fast l-fold cross validation takes the advantage in terms of computational time, especially for the large fold number such as l 〉 20. To corroborate the efficacy and feasibility of fast l-fold cross validation, experiments on five benchmark regression data sets are evaluated.

Prediction of length-of-day using extreme learning machine

Traditional artificial neural networks （ANN） such as back-propagation neural networks （BPNN） provide good predictions of length-of-day （LOD）. However, the determination of network topology is difficult and time consuming. Therefore, we propose a new type of neural network, extreme learning machine （ELM）, to improve the efficiency of LOD predictions. Earth orientation parameters （EOP） C04 time-series provides daily values from International Earth Rotation and Reference Systems Service （IERS）, which serves as our database. First, the known predictable effects that can be described by functional models-such as the effects of solid earth, ocean tides, or seasonal atmospheric variations--are removed a priori from the C04 time-series. Only the residuals after the subtraction of a priori model from the observed LOD data （i.e., the irregular and quasi-periodic variations） are employed for training and predictions. The predicted LOD is the sum of a prior extrapolation model and the ELM predictions of the residuals. Different input patterns are discussed and compared to optimize the network solution. The prediction results are analyzed and compared with those obtained by other machine learning-based prediction methods, including BPNN, generalization regression neural networks （GRNN）, and adaptive network-based fuzzy inference systems （ANFIS）. It is shown that while achieving similar prediction accuracy, the developed method uses much less training time than other methods. Furthermore, to conduct a direct comparison with the existing prediction tech- niques, the mean-absolute-error （MAE） from the proposed method is compared with that from the EOP prediction comparison campaign （EOP PCC）. The results indicate that the accuracy of the proposed method is comparable with that of the former techniques. The implementation of the proposed method is simple.

Constrained voting extreme learning machine and its application

Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.

A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine

The electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy. Substantial data is generated by the EEG recordings of ambulatory recording systems, and detection of epileptic activity requires a time-consuming analysis of the complete length of the EEG time series data by a neurology expert. A variety of automatic epilepsy detection systems have been developed during the last ten years. In this paper, we investigate the potential of a recently-proposed statistical measure parameter regarded as Sample Entropy (SampEn), as a method of feature extraction to the task of classifying three different kinds of EEG signals (normal, interictal and ictal) and detecting epileptic seizures. It is known that the value of the SampEn falls suddenly during an epileptic seizure and this fact is utilized in the proposed diagnosis system. Two different kinds of classification models, back-propagation neural network (BPNN) and the recently-developed extreme learning machine (ELM) are tested in this study. Results show that the proposed automatic epilepsy detection system which uses sample entropy (SampEn) as the only input feature, together with extreme learning machine (ELM) classification model, not only achieves high classification accuracy (95.67%) but also very fast speed.

Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning

Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.

Assessment of glaucoma using extreme learning machine and fractal feature analysis

Dear Sir,Iam Dr.Kavitha S,from the Department of Electronics and Communication Engineering,Nandha Engineering College,Erode,Tamil Nadu,India.I write to present the detection of glaucoma using extreme learning machine(ELM)and fractal feature analysis.Glaucoma is the second most frequent cause of permanent blindness in industrial

Misfire identification of automobile engines based on wavelet packet and extreme learning machine

Due to non-stationary characteristics of the vibration signal acquired from cylinder head,a misfire fault diagnosis system of automobile engines based on correlation coefficient gained by wavelet packet and extreme learning machine(ELM)is proposed.Firstly,the original signal is decomposed by wavelet packet,and correlation coefficients between the reconstructed signal of each sub-band and the original signal as well as the energy entropy of each sample are obtained.Then,the eigenvectors established by the correlation coefficients method and the energy entropy method fused with kurtosis are inputted to the four kinds of classifiers including BP neural network,KNN classifier,support vector machine and ELM respectively for training and testing.Experimental results show that the method proposed in this paper can effectively reflect the differences that the fault produces and identify the single-cylinder misfire accurately,which has the advantages of higher accuracy and shorter training time.

A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19

Many respiratory infections around the world have been caused by coronaviruses.COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate.There is a high need for computer-assisted diagnostics(CAD)in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems.Machine learning(ML)has been used to examine chest X-ray frames.In this paper,a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes,a pneumonia patient,a COVID-19 patient,or a normal person.First,three different pre-trainedConvolutionalNeuralNetwork(CNN)models(resnet18,resnet25,densenet201)are employed for deep feature extraction.Second,each feature vector is passed through the binary Butterfly optimization algorithm(bBOA)to reduce the redundant features and extract the most representative ones,and enhance the performance of the CNN models.These selective features are then passed to an improved Extreme learning machine(ELM)using a BOA to classify the chest X-ray images.The proposed paradigm achieves a 99.48%accuracy in detecting covid-19 cases.

Robust signal recognition algorithm based on machine learning in heterogeneous networks

There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio（SNR） circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recognition method based upon the original and latest updated version of the extreme learning machine（ELM） to help users to switch between networks. The ELM utilizes signal characteristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analytically, which result in lower complexity. Theoretically, the algorithm tends to offer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE models in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized successfully to achieve a 95% accuracy in a low SNR（0 dB） environment in the time-varying multipath Rayleigh fading channel.

Selective Ensemble Extreme Learning Machine Modeling of Effluent Quality in Wastewater Treatment Plants

Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.

A Heterogeneous Ensemble of Extreme Learning Machines with Correntropy and Negative Correlation

The Extreme Learning Machine（ELM） is an effective learning algorithm for a Single-Layer Feedforward Network（SLFN）. It performs well in managing some problems due to its fast learning speed. However, in practical applications, its performance might be affected by the noise in the training data. To tackle the noise issue, we propose a novel heterogeneous ensemble of ELMs in this article. Specifically, the correntropy is used to achieve insensitive performance to outliers, while implementing Negative Correlation Learning（NCL） to enhance diversity among the ensemble. The proposed Heterogeneous Ensemble of ELMs（HE2 LM） for classification has different ELM algorithms including the Regularized ELM（RELM）, the Kernel ELM（KELM）, and the L2-norm-optimized ELM（ELML2）. The ensemble is constructed by training a randomly selected ELM classifier on a subset of the training data selected through random resampling. Then, the class label of unseen data is predicted using a maximum weighted sum approach. After splitting the training data into subsets, the proposed HE2 LM is tested through classification and regression tasks on real-world benchmark datasets and synthetic datasets. Hence, the simulation results show that compared with other algorithms, our proposed method can achieve higher prediction accuracy, better generalization, and less sensitivity to outliers.

基于FSSA-ELM的模拟电路故障诊断方法

在大规模电路中,模拟电路的故障率高达80%。针对模拟电路故障诊断方法准确率低、耗时长的问题,提出了一种分数阶麻雀搜索算法结合极限学习机(FSSA-ELM)的模拟电路故障诊断方法。利用核主成分分析与局部线性嵌入(KPCA-LLE)联合方式对电路故障数据进行特征提取,通过分数阶与麻雀搜索算法(SSA)相融合,对极限学习机(ELM)的权重和阈值进行寻优,将提取后的特征数据输入到FSSA-ELM模型中进行训练和测试。T型反馈网络反相比例运算电路诊断实例表明,FSSA-ELM的故障诊断用时相较于SSA-ELM缩短了891 s,单故障诊断准确率可达972%,比SSA-ELM和ELM分别提高了19%和28%;双故障诊断准确率可达95%,分别提高了04%和10%。该故障诊断方法准确率高、耗时短,具有较强的模拟电路故障检测能力。

基于PSO−ELM的综采工作面液压支架姿态监测方法

针对基于惯性测量单元的液压支架姿态解算方法会产生累计误差、校正结果不准确的问题,提出一种基于粒子群优化(PSO)−极限学习机(ELM)的综采工作面液压支架姿态监测方法。以液压支架顶梁俯仰角为监测对象,采用倾角传感器和陀螺仪采集液压支架顶梁支护姿态实时信息,对采集到的数据进行预处理,将处理后的数据输入PSO−ELM误差补偿模型中,得到解算误差预测值;同时通过卡尔曼滤波融合进行液压支架姿态解算,得到解算值;再用误差预测值对解算值进行误差补偿,从而求得更加准确的顶梁支护姿态数据。该方法只考虑加速度和角速度数据与解算误差的关系,不依赖具体的物理模型,可有效降低姿态解算累计误差。实验结果表明:液压支架顶梁俯仰角平均绝对误差由补偿前的1.4208°减少到0.0580°,且误差曲线具有良好的收敛性,验证了所提方法可持续稳定地监测液压支架的支护姿态。

一种基于PSO-ELM的低渗透砂岩水淹层测井识别方法

水淹层测井识别对油田开发方案部署及提高采收率有着重要意义。新疆陆梁油田作业区某区块油层水淹类型主要为污水水淹,测井响应特征复杂多变,传统识别图版方法难以对水淹层有效识别。文中基于测井、地质、试油等资料,在水淹层测井响应特征分析基础上,提出了一种利用改进粒子群优化算法(Particle Swarm Optimization,PSO)及极限学习机(Extreme Learning Machine,ELM)的水淹层识别方法。首先,利用相关系数优选6个主控因素:RD,RS,GR,SP,DEN,AC。其次,采用改进粒子群算法对极限学习机模型进行参数寻优;最后,利用优化后的模型对研究区水淹层进行预测。结果表明,利用PSO-ELM模型识别水淹层,识别符合率达到91.7%,应用效果优于ELM模型及传统识别图版,为水淹层测井识别提供了新思路。

基于RCMFME和AO-ELM的齿轮箱损伤识别策略

针对模糊熵只考虑信号的局部特征而忽略信号的全局特征,导致齿轮箱故障识别的准确率不佳的问题,提出了一种基于精细复合多尺度模糊测度熵(RCMFME)、天鹰优化器(AO)优化极限学习机(ELM)的齿轮箱故障诊断方法。首先,在精细复合多尺度模糊熵的基础上,对矢量的构造方式进行了改进,提出了能够同时考虑时间序列局部特征和全局特征的RCMFME方法;随后,利用RCMFME指标提取了齿轮箱振动信号的熵值,组建了故障特征向量;接着,利用AO算法对极限学习机的参数进行了自适应搜索,生成了参数最优的多类别分类器;最后,将训练样本的故障特征向量输入至AO-ELM分类模型中进行了模型训练,以构造性能最优的分类器,并实现了对齿轮箱测试样本的故障识别目的;利用两种齿轮箱振动数据集进行了实验,在识别准确率和识别稳定性方面,与相关的特征提取方法进行了对比。研究结果表明:采用基于RCMFME和AO-ELM的故障诊断方法能够分别取得100%和98%的分类准确率,平均识别准确率分别达到了100%和98%,优于精细复合多尺度全局模糊熵(RCMGFE)、精细复合多尺度模糊熵(RCMFE)、精细复合多尺度样本熵(RCMSE)。该方法具有显著的应用潜力。

基于ELM神经网络的高速公路隧道运营风险评估模型

为克服传统高速公路隧道运营安全风险评估方法计算过程繁琐、运算效率低及泛化能力差等问题,采用极限学习机(Extreme Learning Machine,ELM)神经网络模型对高速公路隧道运营风险进行评估。首先,基于系统工程理论,分析了高速公路隧道运营风险影响因素,构建了运营风险评估指标体系。然后,以全国126个隧道典型运营事故数据为样本集,基于ELM神经网络算法,对比不同激活函数模型的分类准确率和测试时间指标,选定Sigmoid作为激活函数,训练得到高速公路隧道运营风险评估模型。最后,以该模型为核心算法开发了隧道运营风险评估系统,并依托广东省某高速公路隧道路段开展了工程应用。结果表明,所构建的风险评估模型简化了人工计算过程,可提升高速公路隧道运营风险评估的及时性和有效性。

Online Sequential Extreme Multilayer Perception with Time Series Learning Machine Based Output Self Feedback for Prediction

This study presents a time series prediction model with output self feedback which is implemented based on online sequential extreme learning machine. The output variables derived from multilayer perception can feedback to the network input layer to create a temporal relation between the current node inputs and the lagged node outputs while overcoming the limitation of memory which is a vital port for any time-series prediction application. The model can overcome the static prediction problem with most time series prediction models and can effectively cope with the dynamic properties of time series data. A linear and a nonlinear forecasting algorithms based on online extreme learning machine are proposed to implement the output feedback forecasting model. They are both recursive estimator and have two distinct phases： Predict and Update. The proposed model was tested against different kinds of time series data and the results indicate that the model outperforms the original static model without feedback.