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Software bug prediction is mainly used for testing and code inspection. So, software bug prediction is carried out by network measures over the decades. But, the classical fault prediction method failed to obtain the semantic difference among various programs. Thus, it degrades the working of the prediction model, which is designed using these aspects. It is necessary to obtain the semantic difference for designing the prediction model accurately and effectively. In a software defect prediction system, it faces many difficulties in identifying the defect modules like correlation, irrelevance aspects, data redundancy, and missing samples or values. Consequently, many researchers are designed to detect software bug prediction that categorises faulty as well as non-faulty modules with software matrices. But, there are only a few works focussed to mitigate the class imbalance problem in bug prediction. In order to overcome the problem, it is required to develop an efficient software bug prediction method with the enhanced classifier. For this experimentation, the input data are taken from the standard online data sources. Initially, the input data undergo pre-processing phase and then, the pre-processed data are provided as input to the feature extraction by utilising the Auto-Encoder. These obtained features are utilised in getting the optimal fused features with the help of a new Hybrid Honey Badger Cat Swarm Algorithm (HHBCSA). Finally, these features are fed as input to the Optimised Parallel Cascaded Deep Network (OPCDP), where the "Extreme Learning Machine (ELM) and Deep Belief Network (DBN)" are used for the prediction of software bugs, in which the parameters from both classifiers are optimised by proposed HHBCSA algorithm. From the investigations, the recommended software bug prediction method offers a quicker bug prediction result, which helps to detect and remove the software bug easily and accurately. [ABSTRACT FROM AUTHOR]

For maintenance of distributed wind power networks, it remains important to realize intelligent operation scheduling strategies for wind power equipments according to their working status. As a consequence, this paper proposes a deep learning-based optimal scheme for distributed wind power networks. First of all, an adaptive status assessment model is constructed to identify time-varying operation status for unit components. Then, based on the predicted operation risk of unit components, a preventive maintenance decision model is formulated to realize flexible decision-making of maintenance tasks. Finally, a dynamic maintenance task scheduling model based on extreme learning machine (ELM) neural network is designed. The ELM neural network-based scheduling approach is expected to use a historical strategy library to assist in revising realtime voltage control strategy. Also, we conduct some experiments to evaluate the performance of the proposed method through simulation modeling. The obtained results show that real-time voltage control accuracy for wind power networks with an incomplete observation area is improved. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak and the implementation of peak and carbon neutral policies have severely impacted oil price volatility and the industrial sector. Exploring the impact mechanisms between oil prices and industries is particularly important for accurate forecasting of crude oil prices. As one of the world's largest commodity consumers, China's crude oil market is more representative and susceptible to external factors than that of developed countries. In this paper, we propose an analytical forecasting framework based on the causal effects between Shanghai crude oil prices and various industries in China to improve the forecasting accuracy of crude oil prices. Information geometric causal inference (IGCI) is applied to detect causal relationships between 31 different industries in China and Shanghai crude oil prices in the three time periods before, during and after COVID-19, and industries with strong causal information effects on crude oil prices in the long run are screened out as additional features. An oil price forecasting model based on Gray Wolf Optimization and Extreme Learning Machine (GWO-ELM) is proposed. Considering the small amount of data for Shanghai crude oil, this paper proposes a cross-learning data approach to solve the problem. Experimental results show that the GWO-ELM model outperforms RF, LSTM, GRU, and migration learning-based Tr-LSTM and Tr-Adaboost models in the task of Shanghai crude oil futures price prediction, and find that industry characteristics with long-term causal effects on oil prices can improve the model prediction accuracy. Our proposed analytical prediction can capture the oil price trend more accurately through the information of the industry and solve the problem of insufficient training data for the model. The application of this framework is expected to provide new methods and ideas for data mining of crude oil and other futures prices. [ABSTRACT FROM AUTHOR]

Automatic modulation classification (AMC) became an important process in various communication systems including commercial, telecommunication and military applications. Further, the accuracy of AMC impacts the performance of these applications. Various machine learning approaches were developed to improve the performance of AMC. However, they failed to classify the different modulation schemes, which needs to satisfy all the spectrum requirements under multipath fading environment. Further, the conventional methods suffer with computational complexity in training to satisfy the real-time operational requirements. So, this paper focuses on implementation of extreme learning machine (ELM) for reduction of training complexities and improves the classification performance. Initially, deep leaning convolutional neural network (DLCNN) model is introduced for extracting the interdependent modulation features based on different modulation types. Then, red deer optimization algorithm (RDOA) is introduced for selecting the best features from DLCNN extracted features. Further, the hybrid ELM with bagging classifier (HELM-BC) is used to classify the various modulation types, i.e., families. The simulation results show that the performance of the proposed AMC system using RDOA-based HELM-BC approach is superior to the conventional AMC systems. [ABSTRACT FROM AUTHOR]

A new approach for one-class fault detection trained only by normal samples has been proposed in this paper. The approach contains multi-anterior-layers for feature extraction and one post-layer for one-class classification. The multi-anterior-layers are based on extreme learning machine-based auto-encoder (ELM-AE). Multi-ELM-AEs are stacked in the front hidden layers to extract abstract features from the raw input. The post-layer is based on the reconstruction error-based ELM-AE (Re-ELM-AE) to act as one-class classifier. As the extension of ELM-AE, the decision threshold and function are given in the Re-ELM-AE, which are utilized to identify whether the test sample is faulty. The efficacy of the presented algorithm is demonstrated on a mathematic example and fault dataset from motor bearing. The method has been compared with shallow learning methods such as one-class support vector machine (OCSVM), the Re-ELM-AE, and one multi-layer neural network named stacked auto-encoder (SAE). The experiment results show that the proposed method outperforms OCSVM and Re-ELM-AE in classification accuracy. Though the classification accuracy of the proposed method and SAE is similar, the training and testing time of the proposed method is much lower than SAE. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated system neutrosophic C-means-based attribute weighting-kernel extreme learning machine (NCMAW-KELM) for medical data classification using NCM clustering and KELM. To do that, NCMAW is developed, and then combined with classification method in classification of medical data. The proposed approach contains two steps. In the first step, input attributes are weighted using NCMAW method. The purpose of the weighting method is twofold: (i) to improve the classification performance in the classification of the medical data, (ii) to transform from nonlinearly separable dataset to linearly separable dataset. Finally, KELM algorithm is used for medical data classification purpose. In KELM algorithm, four types of kernels, such as Polynomial, Sigmoid, Radial basis function and Linear, are used. The simulation result on our three datasets demonstrates that the sigmoid kernel is outperformed to ELM in most cases. From the results, NCMAW-KELM approach may be a promising method in medical data classification problem. [ABSTRACT FROM AUTHOR]

Automated recognition and classification of fishes are useful for studies dealing with counting of fishes for population assessments, discovering association between fishes and ecosystem, and monitoring of the ecosystem. This paper proposes a model which classifies the fishes belonging to the family Labridae in the genus and the species level. Features computed in the spatial and frequency domains are used in this work. All the images are preprocessed before feature extraction. Preprocessing step involves image segmentation for background elimination, de-noising and image enhancement. A combination of color, local binary pattern (LBP), histogram of oriented gradients (HOG), and wavelet features forms the feature vector. An ensemble feature reduction technique is used to reduce the attribute size. Performances of the system using combined as well as reduced feature sets are evaluated using seven popular classifiers. Among the classifiers, wavelet kernel extreme learning machine (ELM) showed higher classification accuracy of 96.65% in genus level and polynomial kernel ELM showed an accuracy of 92.42% in species level with the reduced feature set. [ABSTRACT FROM AUTHOR]

Among other machine learning techniques, the extreme learning machine has evidently proved its diagnostic accuracy on many cases in medical domain. Its accuracy mainly depends on the optimal parameters that are used in training. The proposed work is based on optimizing the extreme learning machine using the recently proposed meta-heuristic optimization technique named artificial algae algorithm with multi-light source. In this work, two experiments are conducted using four binary classification datasets related to medical domain. The feasible number of hidden neurons is found from the first experiment using relevant performance parameters. In the second experiment, the classifier with feasible number of hidden neurons is further evaluated with the ten-fold cross-validation method based on its computation time and classification accuracy. In both the experiments, the proposed classifier performance compared with that of other four similar hybrid approaches. It is also statistically compared using Friedman test and Wilcoxon signed rank test based on the area under curve and accuracy values respectively. It is found that the proposed classifier produces better results than the other classifiers. [ABSTRACT FROM AUTHOR]

Manually inspecting bugs to determine their severity is often an enormous but essential software development task, especially when many participants generate a large number of bug reports in a crowdsourced software testing context. Therefore, boosting the capabilities of methods of predicting bug report severity is critically important for determining the priority of fixing bugs. However, typical classification techniques may be adversely affected when the severity distribution of the bug reports is imbalanced, leading to performance degradation in a crowdsourcing environment. In this study, we propose an enhanced oversampling approach called CR-SMOTE to enhance the classification of bug reports with a realistically imbalanced severity distribution. The main idea is to interpolate new instances into the minority category that are near the center of existing samples in that category. Then, we use an extreme learning machine (ELM) — a feedforward neural network with a single layer of hidden nodes — to predict the bug severity. Several experiments were conducted on three datasets from real bug repositories, and the results statistically indicate that the presented approach is robust against real data imbalance when predicting the severity of bug reports. The average accuracies achieved by the ELM in predicting the severity of Eclipse, Mozilla, and GNOME bug reports were 0.780, 0.871, and 0.861, which are higher than those of classifiers by 4.36%, 6.73%, and 2.71%, respectively. [ABSTRACT FROM AUTHOR]

The two-dimensional morphology of the cladding layer has an important influence on the quality of the cladding layer and the crack tendency. Using the powerful nonlinear processing ability of the single hidden layer feedforward neural network, a prediction model between the cladding technological parameters and the two-dimensional morphology of the cladding layer is established. Taking the cladding parameters as the input and the two-dimensional morphology of the cladding as the output, the experimental data is used to train the network to achieve a high-level mapping of the input and output. On this basis, the algorithm of extreme learning machine is used to optimize the single hidden layer feedforward neural network to overcome the problems of slow convergence speed, more network training parameters and easy local convergence in back-propagation algorithm. The results show that the relationship between the cladding process parameters and the two-dimensional morphology of the cladding layer can be roughly reflected by the back-propagation algorithm. However, the prediction results are not stable and the error rate is between 10% and 40%. The neural network optimized by the extreme learning machine is utilized to get a better prediction result. The error rate is 10–20%. [ABSTRACT FROM AUTHOR]

Knowledge society blockchain is one of the most powerful and recent tools to make the internet environment safer and reliable. Manufacturing has traditionally been dominated by standard designs that are mass-produced, due to the fact, that custom production causes additional costs that make it less affordable than mass production. This paper proposes to develop a designer expert system for IoT installation layout designs, using blockchain distributed system based on a machine learning, with users entering data to the expert system by a smart bot software. This expert system will work using extreme learning machine as inference engine; therefore, this is a shell to develop any expert system with fast learning. The whole system is represented by a smart contract with a value linked to the value of the expert system, the more this expert system be quoted on the web, the more the shares of the smart contract will cost. [ABSTRACT FROM AUTHOR]

Magnetorheological elastomer (MRE) isolator has been proved as a promising semi-active control device for structural vibration control. For its engineering application, developing an accurate and robust model is definitely necessary and also a challenging task. Most of the present models, belonging to parametric models, need to identify various model parameters and sometimes are not capable of perfectly capturing the unique characteristics of the device. In this work, a novel nonparametric model is proposed to characterize the inherent dynamics of the MRE isolator with the features of hysteresis and nonlinearity. Initially, dynamic tests are conducted to evaluate the performance of the isolator under various loading conditions, including harmonic, random, and seismic excitations. Then, on the basis of the captured experimental results, a hybrid learning method is designed to forecast the nonlinear responses of the device with known external inputs. In this method, a type of single hidden layer feed-forward network, called extreme learning machine (ELM), is developed to forecast the nonlinear responses (shear force) of the device with captured velocity, displacement, and current level. To obtain optimal performance of the developed model, an improved binary-coded discrete cat swarm optimization (BCDCSO) method is adopted to select optimal inputs and neuron number in the hidden layer for the network development. The performance of the proposed method is verified through the comparison between experimental results and model predictions. Due to the noise influence in the practical condition, the robustness of the proposed method is also validated via adding noise disturbance into the supplying currents. The results show that the proposed method outperforms the standard ELM in terms of characterization of the MRE isolator, even though the captured responses are polluted with external measurement noises. [ABSTRACT FROM AUTHOR]

Community detection has long been a fascinating topic in complex networks since the community structure usually unveils valuable information of interest. The prevalence and evolution of deep learning and neural networks have been pushing forward the advancement in various research fields and also provide us numerous useful and off the shelf techniques. In this paper, we put the cascaded stacked autoencoders and the unsupervised extreme learning machine (ELM) together in a two-level embedding process and propose a novel community detection algorithm. Extensive comparison experiments in circumstances of both synthetic and real-world networks manifest the advantages of the proposed algorithm. On one hand, it outperforms the k-means clustering in terms of the accuracy and stability thus benefiting from the determinate dimensions of the ELM block and the integration of sparsity restrictions. On the other hand, it endures smaller complexity than the spectral clustering method on account of the shrinkage in time spent on the eigenvalue decomposition procedure. [ABSTRACT FROM AUTHOR]

An effective method for damage detection of plate structures using the extreme learning machine (ELM) is proposed in this study. With the ELM, the mode shapes and natural frequencies of a damaged plate are treated as the input and the damage states in the plate elements as the output. The proposed method was applied to two numerical examples, namely, a cantilever and a plate with four-fixed supports containing one or several damages with and without noise in the modal data. The results obtained reveal that the methodology can be used as an effective technique for the damage identification of plate structures using the modal data and ELM. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop an effective method to identify upper limb motions based on EMG signal for community rehabilitation. The method will be applicable to the control system in the rehabilitation equipment and provide objective data for quantitative assessment. The recognition goal sets of upper limb motion are constructed by decomposing assessment activities of activity of daily living scale (ADL). The recognition feature vector space is established by Variance (VAR), Mean Absolute Value (MAV), the fourth-order Autoregressive (the 4thAR), Zero Crossings (ZC's), integral EMG (IEMG), and Root Mean Square (RMS), and various feature sets are extracted to get the best classification. Locally linear embedding (LLE) algorithm is used to reduce the computational complexity, and upper limb motions about shoulder, elbow and wrist are quickly classified through extreme leaving machine (ELM), which obtained the average accuracy of 98.14%, 98.61% and 94.77%, respectively. Furthermore, when ELM is compared with Back-propagation (BP) and Support vector machine (SVM), it has performed relatively better than BP and SVM. The results show that the validity of the mixed model for recognition is verified. In addition, the method can also provide a basis for recognition and assessment of the angle of upper limb joint in the next study. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) is an efficient training algorithm for single-hidden layer feed-forward neural networks (SLFNs). Two pruned-ELM named P-ELM1 and P-ELM2 are proposed by Rong et al. P-ELM1 and P-ELM2 employ and information gain to measure the association between the class labels and individual hidden node respectively. But for the continuous value data sets, it is inevitable for P-ELM1 and P-ELM2 to evaluate the probability distributions of the data sets with discretization methods for calculating and information gain, while the discretization will lead to information loss. Furthermore, the discretization will result in high computational complexity. In order to deal with the problems, based on tolerance rough sets, this paper proposed an improved pruned-ELM algorithm, which can overcome the drawbacks mentioned above. Experimental results along with statistical analysis on 8 UCI data sets show that the improved algorithm outperforms the pruned-ELM in computational complexity and testing accuracy. [ABSTRACT FROM AUTHOR]

Due to the uncertainty in oil markets, this paper proposes a novel approach for oil purchasing and distribution optimization by incorporating price and demand prediction, i.e., the prediction-based oil purchasing-and-distribution optimization model. In particular, the proposed method bridges the latest information technology and decision-making technique by introducing the recently proposed information technology (i.e., extreme learning machine (ELM)) into the oil purchasing-and-distribution optimization model. Two main steps are involved: market prediction and planning optimization in the proposed model. In market prediction, the ELM technique is employed to provide fast training time and accurate forecasting results for oil prices and demands. In planning optimization, two objectives of general profit maximization and inventory risk minimization are considered; and the most popular multi-objective evolutionary algorithm (MOEA), nondominated sorting genetic algorithm II (NSGA-II), is implemented to search approximate Pareto optimal solutions. For illustration and verification, the motor gasoline market in the US is focused on as the study sample, and the experimental results demonstrate the superiority of the proposed prediction-based optimization approach over its benchmark models (without market prediction and/or planning optimization), in terms of the highest profit and the lowest risk. [ABSTRACT FROM AUTHOR]

In this paper, we employ multiple Single-hidden Layer Feedforward Neural Networks for multi-view action recognition. We propose an extension of the Extreme Learning Machine algorithm that is able to exploit multiple action representations and scatter information in the corresponding ELM spaces for the calculation of the networks' parameters and the determination of optimized network combination weights. The proposed algorithm is evaluated by using two state-of-the-art action video representation approaches on five publicly available action recognition databases designed for different application scenarios. Experimental comparison of the proposed approach with three commonly used video representation combination approaches and relating classification schemes illustrates that ELM networks employing a supervised view combination scheme generally outperform those exploiting unsupervised combination approaches, as well as that the exploitation of scatter information in ELM-based neural network training enhances the network's performance. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of efficiency in reinforcement learning of Single Robot Hose Transport (SRHT) by training an Extreme Learning Machine (ELM) from the state-action value Q-table, obtaining large reduction in data space requirements because the number of ELM parameters is much less than the Q-table's size. Moreover, ELM implements a continuous map which can produce compact representations of the Citable, and generalizations to increased space resolution and unknown situations. In this paper we evaluate empirically three strategies to formulate ELM learning to provide approximations to the Q-table, namely as classification, multi-variate regression and several independent regression problems. [ABSTRACT FROM AUTHOR]

Extreme Learning Machine (ELM) is an emergent technique for training Single-hidden Layer Feedforward Networks (SLFNs). It attracts significant interest during the recent years, but the randomly assigned network parameters might cause high learning risks. This fact motivates our idea in this paper to propose an evolving ELM paradigm for classification problems. In this paradigm, a Differential Evolution (DE) variant, which can online select the appropriate operator for offspring generation and adaptively adjust the corresponding control parameters, is proposed for optimizing the network. In addition, a 5-fold cross validation is adopted in the fitness assignment procedure, for improving the generalization capability. Empirical studies on several real-world classification data sets have demonstrated that the evolving ELM paradigm can generally outperform the original ELM as well as several recent classification algorithms. [ABSTRACT FROM AUTHOR]

The techniques of fuzzy measure and fuzzy integral have been successfully applied in various real-world applications. The determination of fuzzy measures is the most difficult part in problem solving. Signed efficiency measure, which is a special kind of fuzzy measure with the best representation ability but the highest complexity, is even harder to determine. Some methodologies have been developed for solving this problem such as artificial neural networks (ANNs) and genetic algorithms (GAs). However, none of the existing methods can outperform the others with unique advantages. Thus, there is a strong need to develop a new technique for learning distinct signed efficiency measures from data. Extreme learning machine (ELM) is a new learning paradigm for training single hidden layer feed-forward networks (SLFNs) with randomly chosen input weights and analytically determined output weights. In this paper, we propose an ELM based algorithm for signed efficiency measure determination. Experimental comparisons demonstrate the effectiveness of the proposed method in both time and accuracy. [ABSTRACT FROM AUTHOR]

To predict the performance of a diesel engine, current practice relies on the use of black-box identification where numerous experiments must be carried out in order to obtain numerical values for model training. Although many diesel engine models based on artificial neural networks (ANNs) have already been developed, they have many drawbacks such as local minima, user burden on selection of optimal network structure, large training data size and poor generalization performance, making themselves difficult to be put into practice. This paper proposes to use extreme learning machine (ELM), which can overcome most of the aforementioned drawbacks, to model the emission characteristics and the brake-specific fuel consumption of the diesel engine under scarce and exponential sample data sets. The resulting ELM model is compared with those developed using popular ANNs such as radial basis function neural network (RBFNN) and advanced techniques such as support vector machine (SVM) and its variants, namely least squares support vector machine (LS-SVM) and relevance vector machine (RVM). Furthermore, some emission outputs of diesel engines suffer from the problem of exponentiality (i.e., the output y grows up exponentially along input x) that will deteriorate the prediction accuracy. A logarithmic transformation is therefore applied to preprocess and post-process the sample data sets in order to improve the prediction accuracy of the model. Evaluation results show that ELM with the logarithmic transformation is better than SVM, LS-SVM, RVM and RBFNN with/without the logarithmic transformation, regardless the model accuracy and training time. [ABSTRACT FROM AUTHOR]

Recently, extreme learning machines (ELMs) have been a promising tool in solving a wide range of regression and classification applications. However, when modeling multiple related tasks in which only limited training data per task are available and the dimension is low, ELMs are generally hard to get impressive performance due to little help from the informative domain knowledge across tasks. To solve this problem, this paper extends ELM to the scenario of multi-task learning (MTL). First, based on the assumption that model parameters of related tasks are close to each other, a new regularization-based MTL algorithm for ELM is proposed to learn related tasks jointly via simple matrix inversion. For improving the learning performance, the algorithm proposed above is further formulated as a mixed integer programming in order to identify the grouping structure in which parameters are closer than others, and finally an alternating minimization method is presented to solve this optimization. Experiments conducted on a toy problem as well as real-life data set demonstrate the effectiveness of the proposed MTL algorithm compared to the classical ELM and the standard MTL algorithm. [ABSTRACT FROM AUTHOR]

The design of fuzzy cognitive maps (FCMs) mainly relies on human knowledge, which implies subjectivity of the developed model. This affects the accuracy of an FCM significantly. In order to address this issue, we propose a novel learning model for FCMs in this paper. It achieves efficient learning by automatically adjusting the system parameters according to the environment. The learning model consists of extreme learning machine (ELM) and a curious model, where ELM learns from the modeled system and the curious model helps to further improve the performance of ELM. We use an example to illustrate the effectiveness of our model. The simulation results show that our model helps to improve the accuracy of FCMs. [ABSTRACT FROM AUTHOR]

The application of machine learning methods in the engineering of intelligent technical systems often requires the integration of continuous constraints like positivity, monotonicity, or bounded curvature in the learned function to guarantee a reliable performance. We show that the extreme learning machine is particularly well suited for this task. Constraints involving arbitrary derivatives of the learned function are effectively implemented through quadratic optimization because the learned function is linear in its parameters, and derivatives can be derived analytically. We further provide a constructive approach to verify that discretely sampled constraints are generalized to continuous regions and show how local violations of the constraint can be rectified by iterative re-learning. We demonstrate the approach on a practical and challenging control problem from robotics, illustrating also how the proposed method enables learning from few data samples if additional prior knowledge about the problem is available. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) is an efficient and practical learning algorithm used for training single hidden layer feed-forward neural networks (SLFNs). ELM can provide good generalization performance at extremely fast learning speed. However, ELM suffers from instability and over-fitting, especially on relatively large datasets. Based on probabilistic SLFNs, an approach of fusion of extreme learning machine (F-ELM) with fuzzy integral is proposed in this paper. The proposed algorithm consists of three stages. Firstly, the bootstrap technique is employed to generate several subsets of original dataset. Secondly, probabilistic SLFNs are trained with ELM algorithm on each subset. Finally, the trained probabilistic SLFNs are fused with fuzzy integral. The experimental results show that the proposed approach can alleviate to some extent the problems mentioned above, and can increase the prediction accuracy. [ABSTRACT FROM AUTHOR]

It is experimentally observed that the approximate errors of extreme learning machine (ELM) are dependent on the uniformity of training samples after the network architecture is fixed, and the uniformity, which is usually measured by the variance of distances among samples, varies with the linear transformation induced by the random weight matrix. By analyzing the dimension increase process in ELM, this paper gives an approximate relation between the uniformities before and after the linear transformation. Furthermore, by restricting ELM with a two-dimensional space, it gives an upper bound of ELM approximate error which is dependent on the distributive uniformity of training samples. The analytic results provide some useful guidelines to make clear the impact of random weights on ELM approximate ability and improve ELM prediction accuracy. [ABSTRACT FROM AUTHOR]

Recently, a novel learning algorithm called extreme learning machine (ELM) was proposed for efficiently training single-hidden-layer feedforward neural networks (SLFNs). It was much faster than the traditional gradient-descent-based learning algorithms due to the analytical determination of output weights with the random choice of input weights and hidden layer biases. However, this algorithm often requires a large number of hidden units and thus slowly responds to new observations. Evolutionary extreme learning machine (E-ELM) was proposed to overcome this problem; it used the differential evolution algorithm to select the input weights and hidden layer biases. However, this algorithm required much time for searching optimal parameters with iterative processes and was not suitable for data sets with a large number of input features. In this paper, a new approach for training SLFNs is proposed, in which the input weights and biases of hidden units are determined based on a fast regularized least-squares scheme. Experimental results for many real applications with both small and large number of input features show that our proposed approach can achieve good generalization performance with much more compact networks and extremely high speed for both learning and testing. [ABSTRACT FROM AUTHOR]

In this paper, a new effective model is proposed to forecast how long the postoperative patients suffered from non-small cell lung cancer will survive. The new effective model which is based on the extreme learning machine (ELM) and principal component analysis (PCA) can forecast successfully the postoperative patients' survival time. The new model obtains better prediction accuracy and faster convergence rate which the model using backpropagation (BP) algorithm and the Levenberg-Marquardt (LM) algorithm to forecast the postoperative patients' survival time can not achieve. Finally, simulation results are given to verify the efficiency and effectiveness of our proposed new model. [ABSTRACT FROM AUTHOR]