3D object recognition based on a geometrical topology model and extreme learning machine

A fast training algorithm for extreme learning machine based on matrix decomposition

A robust single hidden layer feed forward neural network (SLFN) is used in this study to model the in-flight particle characteristics of the atmospheric plasma spray (APS) process with regard to the input processing parameters. The in-flight particle characteristics influence the structure and properties of the APS coating and, thus, are considered important parameters to comprehend the manufacturing process. The training times of traditional back propagation algorithms, mostly used to model such processes, are far slower than desired for implementation of an on-line control system. Use of slow gradient based learning methods and iterative tuning of all network parameters during the learning process are the two major causes for such slower learning speed. An extreme learning machine (ELM) algorithm, which randomly selects the input weights and biases and analytically determines the output weights, is used in this work to train the SLFNs. Performance comparisons of the networks trained with ELM algorithm and standard error back propagation algorithms are presented. It is found that networks trained with ELM have good generalization performance, much shorter training times and stable performance with regard to the changes in number of hidden layer neurons. The trends represent robustness of the trained networks and enhance reliability of the application of the artificial neural network in modelling APS processes.

The main objective of this works is to study and improve the performances of the Single hidden Layer Feedforward Neural network (SLFN) for the application of Remaining Useful Life (RUL) prediction of aircraft engines. The most common problems in SLFNs based old training algorithms such as backpropagation are time consuming, over-fitting and the appropriate network architecture identification. In this paper a new incremental constructive learning algorithm based on Extreme Learning Machine algorithm is proposed for founding the appropriate architecture of a neural network under less computational costs. The aim of the proposed training approach is to study its maximum capabilities during RUL prediction by reducing over-fitting and human intervention. The performances of the proposed approach which are evaluated on C-MAPPS dataset and compared with its original variant from the literature. Experimental results proved that the new algorithm outperforms the old one in many metrics evaluations.

This paper presents a new optimal controller design for the sensorless variable speed wind turbine (SVSWT) based on the extreme learning machine (ELM) and the adaptive particle swarm optimisation (APSO) algorithms. The two main objectives of this command are to maximize the conversion of wind energy below the rated wind speed and to maintain the safety of the wind turbine system (WT) by minimizing stress on the drive train shafts. The proposed technique is based on the efficiency of the ELM for single hidden layer feed forward neural networks (SLFN) combined to sliding mode control (SMC) to respectively, improve the used model and stabilize the operation of the WT. ELM algorithm with high learning speed is used to approximate the nonlinear unmodelled dynamics while SMC is used to compensate the external disturbances and modelling errors. APSO algorithm is introduced to adapt and optimize the gain of the SMC. The efficiency of the proposed method is illustrated in simulations by the comparison with traditional SMC.

Extreme learning machine (ELM) has been developed for single hidden layer feedforward neural networks (SLFNs). In ELM algorithm, the connections between the input layer and the hidden neurons are randomly assigned and remain unchanged during the learning process. The output connections are then tuned via minimizing the cost function through a linear system. The computational burden of ELM has been significantly reduced as the only cost is solving a linear system. The low computational complexity attracted a great deal of attention from the research community, especially for high dimensional and large data applications. This paper provides an up-to-date survey on the recent developments of ELM and its applications in high dimensional and large data. Comprehensive reviews on image processing, video processing, medical signal processing, and other popular large data applications with ELM are presented in the paper.

Extreme Learning Machine for large-scale graph classification based on MapReduce

An improved classification approach is proposed to solve the hot research problem of some complex multiclassification samples based on extreme learning machine (ELM). ELM was proposed based on the single‐hidden layer feed‐forward neural network (SLFNN). ELM is characterized by the easier parameter selection rules, the faster converge speed, the less human intervention, and so on. In order to further improve the classification precision of ELM, an improved generation method of the network structure of ELM is developed by dynamically adjusting the number of hidden nodes. The number change of the hidden nodes can serve as the computational updated step length of the ELM algorithm. In this paper, the improved algorithm can be called the variable step incremental extreme learning machine (VSI‐ELM). In order to verify the effect of the hidden layer nodes on the performance of ELM, an open‐source machine learning database (University of California, Irvine (UCI)) is provided by the performance test data sets. The regression and classification experiments are used to study the performance of the VSI‐ELM model, respectively. The experimental results show that the VSI‐ELM algorithm is valid. The classification of different degrees of broken wires is now still a problem in the nondestructive testing of hoisting wire rope. The magnetic flux leakage (MFL) method of wire rope is an efficient nondestructive method which plays an important role in safety evaluation. Identifying the proposed VSI‐ELM model is effective and reliable for actually applying data, and it is used to identify the classification problem of different types of samples from MFL signals. The final experimental results show that the VSI‐ELM algorithm is of faster classification speed and higher classification accuracy of different broken wires.

The study was intended to eliminate the noise in three-dimensional transvaginal ultrasound (3D-TVS) images and improve the diagnostic accuracy in intrauterine adhesion (IUA). The extreme learning machine (ELM) algorithm was introduced first for statement. One hundred and thirty cases of suspected IUA patients were taken as the research subjects. The denoising effects of ELM algorithm were evaluated in terms of mean square errors (MSE), peak signal-to-noise ratio (PSNR), and running time, and its diagnostic efficiency of IUA was identified from precise, specificity, and sensitivity. Furthermore, the support vector machine (SVM) algorithm was introduced for comparison. It was found that the MSE and PSNR of the ELM algorithm were 0.0021 and 64.5, respectively, and its average operation time was 11.22 ± 0.89s, that the MSE values of SVM algorithm and ELM algorithm were 0.0045 and 0.0021 and the PSNR values were 52.3 and 64.5, respectively, and that the average running time of SVM algorithm was 16.35 ± 1.33s, and the average running time of ELM algorithm was 11.22 ± 0.89s, superior to the SVM algorithm in denoising effects. Moreover, the ELM algorithm showed excellent diagnostic efficiency for patients with various degrees of IUA. In conclusion, ELM can effectively eliminate noise in 3D-TVS images and demonstrates excellent diagnostic efficiency on IUA, which is worthy of clinical application.

Extreme learning machine (ELM), which was originally proposed for "generalized" single-hidden layer feedforward neural networks (SLFNs), provides efficient unified learning solutions for the applications of feature learning, clustering, regression and classification. Different from the common understanding and tenet that hidden neurons of neural networks need to be iteratively adjusted during training stage, ELM theories show that hidden neurons are important but need not be iteratively tuned. In fact, all the parameters of hidden nodes can be independent of training samples and randomly generated according to any continuous probability distribution. And the obtained ELM networks satisfy universal approximation and classification capability. The fully connected ELM architecture has been extensively studied. However, ELM with local connections has not attracted much research attention yet. This paper studies the general architecture of locally connected ELM, showing that: 1) ELM theories are naturally valid for local connections, thus introducing local receptive fields to the input layer; 2) each hidden node in ELM can be a combination of several hidden nodes (a subnetwork), which is also consistent with ELM theories. ELM theories may shed a light on the research of different local receptive fields including true biological receptive fields of which the exact shapes and formula may be unknown to human beings. As a specific example of such general architectures, random convolutional nodes and a pooling structure are implemented in this paper. Experimental results on the NORB dataset, a benchmark for object recognition, show that compared with conventional deep learning solutions, the proposed local receptive fields based ELM (ELM-LRF) reduces the error rate from 6.5% to 2.7% and increases the learning speed up to 200 times.

The Extreme Learning Machine (ELM) is a fast and straightforward learning algorithm designed for single hidden layer feed-forward neural networks (SLFNs). The ELM randomly assigns input weights and calculates the output weights analytically using the Moore-Penrose generalized inverse (MP) (also called Pseudo inverse). However, the MP is not preferable for ill-posed systems as it is sensitive to perturbation and may produce inaccurate solutions. Furthermore, it is computationally expensive as it involves matrix inversion. Regularized ELM (RELM) was proposed to address the limitations of the MP. In RELM, the regularization parameter is the primary factor that controls the classifier's performance. Though the RELM is stable and produces accurate results, finding a n a ppropriate regularization parameter value is tedious and increases the computation. This work proposes a variant of ELM named LSQR-ELM algorithm, which balances the ELM and RELM by maintaining stability and accuracy and avoiding the regularization entirely. The LSQR-ELM is based on the Least Squares QR (LSQR) iterative technique. Since the LSQR finds p a rtial Q R rather than full QR, it reduces unnecessary computation. To verify the proposed algorithm's ability, comparisons are made with several existing methods including ELM, Minimum Residual Method (MT-ELM), SVD-based SG-ELM, Optimally Pruned ELM (OP-ELM), and K-nearest neighbor (KNN). Extensive experimen-tation on various datasets demonstrates that the LSQR-ELM algorithm outperforms ELM, SG-ELM, OP-ELM, and KNN on most datasets. Moreover, it exhibits comparable performance to MT-ELM while requiring significantly f e wer computations.

Extreme learning machine (ELM) is an emergent method for train- ing single hidden layer feedforward neural networks (SLFNs) with extremely fast training speed, easy implementation and good generalization performance. This work presents effective ensemble procedures for combining ELMs by exploiting di- versity. A large number of ELMs are initially trained in three different scenarios: the original feature input space, the obtained feature subset by forward selection and different random subsets of features. The best combination of ELMs is con- structed according to an exact ranking of the trained models and the useless net- works are discarded. The experimental results on several regression problems show that robust ensemble approaches that exploit diversity can effectively improve the performance compared with the standard ELM algorithm and other recent ELM extensions.

Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural networks. In theory, this algorithm is able to provide good generalization capability at extremely fast learning speed. Comparative studies of benchmark function approximation problems revealed that ELM can learn thousands of times faster than conventional neural network (NN) and can produce good generalization performance in most cases. Unfortunately, the research on damage localization using ELM is limited in the literature. In this chapter, the ELM is extended to the domain of damage localization of plate structures. Its effectiveness in comparison with typical neural networks such as back-propagation neural network (BPNN) and least squares support vector machine (LSSVM) is illustrated through experimental studies. Comparative investigations in terms of learning time and localization accuracy are carried out in detail. It is shown that ELM paves a new way in the domain of plate structure health monitoring. Both advantages and disadvantages of using ELM are discussed.

In this brief, an efficient approach using extreme learning machine (ELM) is first proposed for the behavioral modeling of radio frequency power amplifiers (RF PAs). As a single-hidden layer feedforward neural network algorithm, ELM offers significant speed advantages over conventional neural network learning algorithms. Compared to the existing behavioral modeling based on ANN, the proposed method also requires minimal human intervention. A Class-E PA is taken as an example for comparing ELM against traditional neural network learning algorithm. The modeling results of ELM for AM/AM and IMD3 agree well with the simulation results, and the speed advantage of the proposed method has also been confirmed.

Extreme learning machine (ELM) is an easy-to use and effective learning algorithm of single-hidden layer feed-forward neural networks (SLFNs). The classical learning algorithm in neural network, e. g. backpropagation, requires setting several user-defined parameters and may get into local minimum. However, ELM only requires setting the number of hidden neurons and the activation function. It does not require adjusting the input weights and hidden layer biases during the implementation of the algorithm, and it produces only one optimal solution. Therefore, ELM has the advantages of fast learning speed and good generalization performance. In this paper, ELM is introduced in predicting reservoir permeability. By comparing to SVM, we analyze its feasibility and advantages in reservoir permeability prediction. The experimental results show that ELM has similar accuracy compared to SVR, but it has obvious advantages in parameter selection and learning speed.

Extreme learning machine (ELM) is a fast algorithm to train single-hidden layer feedforward neural networks (SLFNs). Like the traditional classification algorithms, such as decision tree, Naive Bayes classifier and support vector machine, ELM also tends to provide biased classification results when the classification tasks are imbalanced. In this article, we first analyze the relationship between ELM and Naive Bayes classifier, and then take the decision outputs of all training instances in ELM as probability density representation by kernel probability density estimation method. Finally, the optimal classification hyperplane can be determined by finding the intersection point of two probability density distribution curves. Experimental results on thirty-two imbalanced data sets indicate that the proposed algorithm can address class imbalance problem effectively, as well outperform some existing class imbalance learning algorithms in the context of ELM.