Fast detection of impact location using kernel extreme learning machine

Damage location detection has direct relationship with the field of aerospace structure as the detection system can inspect any exterior damage that may affect the operations of the equipment. In the literature, several kinds of learning algorithms have been applied in this field to construct the detection system and some of them gave good results. However, most learning algorithms are time-consuming due to their computational complexity so that the real-time requirement in many practical applications cannot be fulfilled. Kernel extreme learning machine (kernel ELM) is a learning algorithm, which has good prediction performance while maintaining extremely fast learning speed. Kernel ELM is originally applied to this research to predict the location of impact event on a clamped aluminum plate that simulates the shell of aerospace structures. The results were compared with several previous work, including support vector machine (SVM), and conventional back-propagation neural networks (BPNN). The comparison result reveals the effectiveness of kernel ELM for impact detection, showing that kernel ELM has comparable accuracy to SVM but much faster speed on current application than SVM and BPNN.     

基于极速神经网络的协作过滤方法研究

协作过滤是一种有效的个性化推荐技术,针对该技术随着用户和资源的增多,数据的高维稀疏特性严重导致推荐质量的下降和计算速度减慢的问题,研究并实现了一种基于极速神经网络的协作过滤方法。采用主成分分析解决数据高维稀疏性问题,采用极速神经网络技术解决计算速度慢的问题。实验结果表明,该方法具有良好的泛化性能和学习速度,能很好的满足个性化资源推荐的需求。     

RTS game strategy evaluation using extreme learning machine

The fundamental game of real-time strategy (RTS) is collecting resources to build an army with military units to kill and destroy enemy units. In this research, an extreme learning machine (ELM) model is proposed for RTS game strategy evaluation. Due to the complicated game rules and numerous playable items, the commonly used tree-based decision models become complex, sometimes even unmanageable. Since complex interactions exist among unit types, the weighted average model usually cannot be well used to compute the combined power of unit groups, which results in misleading unit generation strategy. Fuzzy measures and integrals are often used to handle interactions among attributes, but they cannot handle the predefined unit production sequence which is strictly required in RTS games. In this paper, an ELM model is trained based on real data to obtain the combined power of units in different types. Both the unit interactions and the production sequence can be implicitly and simultaneously handled by this model. Warcraft III battle data from real players are collected and used in our experiments. Experimental results show that ELM is fast and effective in evaluating the unit generation strategies.     

Evolutionary extreme learning machine

Extreme learning machine (ELM) [G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN2004), Budapest, Hungary, 25-29 July 2004], a novel learning algorithm much faster than the traditional gradient-based learning algorithms, was proposed recently for single-hidden-layer feedforward neural networks (SLFNs). However, ELM may need higher number of hidden neurons due to the random determination of the input weights and hidden biases. In this paper, a hybrid learning algorithm is proposed which uses the differential evolutionary algorithm to select the input weights and Moore-Penrose (MP) generalized inverse to analytically determine the output weights. Experimental results show that this approach is able to achieve good generalization performance with much more compact networks.     

Symmetric extreme learning machine

Extreme learning machine (ELM) can be considered as a black-box modeling approach that seeks a model representation extracted from the training data. In this paper, a modified ELM algorithm, called symmetric ELM (S-ELM), is proposed by incorporating a priori information of symmetry. S-ELM is realized by transforming the original activation function of hidden neurons into a symmetric one with respect to the input variables of the samples. In theory, S-ELM can approximate N arbitrary distinct samples with zero error. Simulation results show that, in the applications where there exists the prior knowledge of symmetry, S-ELM can obtain better generalization performance, faster learning speed, and more compact network architecture.     

Self-adaptive extreme learning machine

In order to overcome the disadvantage of the traditional algorithm for SLFN (single-hidden layer feedforward neural network), an improved algorithm for SLFN, called extreme learning machine (ELM), is proposed by Huang et al. However, ELM is sensitive to the neuron number in hidden layer and its selection is a difficult-to-solve problem. In this paper, a self-adaptive mechanism is introduced into the ELM. Herein, a new variant of ELM, called self-adaptive extreme learning machine (SaELM), is proposed. SaELM is a self-adaptive learning algorithm that can always select the best neuron number in hidden layer to form the neural networks. There is no need to adjust any parameters in the training process. In order to prove the performance of the SaELM, it is used to solve the Italian wine and iris classification problems. Through the comparisons between SaELM and the traditional back propagation, basic ELM and general regression neural network, the results have proven that SaELM has a faster learning speed and better generalization performance when solving the classification problem.     

Classifying imbalanced data using BalanceCascade-based kernelized extreme learning machine

Pattern Analysis and Applications - Imbalanced learning is one of the substantial challenging problems in the field of data mining. The datasets that have skewed class distribution pose hindrance...     

Rapid building detection using machine learning

This work describes algorithms for performing discrete object detection, specifically in the case of buildings, where usually only low quality RGB-only geospatial reflective imagery is available. We utilize new candidate search and feature extraction techniques to reduce the problem to a machine learning (ML) classification task. Here we can harness the complex patterns of contrast features contained in training data to establish a model of buildings. We avoid costly sliding windows to generate candidates; instead we innovatively stitch together well known image processing techniques to produce candidates for building detection that cover 80–85 % of buildings. Reducing the number of possible candidates is important due to the scale of the problem. Each candidate is subjected to classification which, although linear, costs time and prohibits large scale evaluation. We propose a candidate alignment algorithm to boost classification performance to 80–90 % precision with a linear time algorithm and show it has negligible cost. Also, we propose a new concept called a Permutable Haar Mesh (PHM) which we use to form and traverse a search space to recover candidate buildings which were lost in the initial preprocessing phase. All code and datasets from this paper are made available online (http://kdl.cs.umb.edu/w/datasets/ and https://github.com/caitlinkuhlman/ObjectDetectionCLUtility).     

Bot detection using unsupervised machine learning

Microsystem Technologies - This research focuses on bot detection through implementation of techniques such as traffic analysis, unsupervised machine learning, and similarity analysis between...     

Convex incremental extreme learning machine

Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Transactions on Neural Networks 17(4) (2006) 879–892] have recently proposed a new theory to show that single-hidden-layer feedforward networks (SLFNs) with randomly generated additive or radial basis function (RBF) hidden nodes (according to any continuous sampling distribution) can work as universal approximators and the resulting incremental extreme learning machine (I-ELM) outperforms many popular learning algorithms. I-ELM randomly generates the hidden nodes and analytically calculates the output weights of SLFNs, however, I-ELM does not recalculate the output weights of all the existing nodes when a new node is added. This paper shows that while retaining the same simplicity, the convergence rate of I-ELM can be further improved by recalculating the output weights of the existing nodes based on a convex optimization method when a new hidden node is randomly added. Furthermore, we show that given a type of piecewise continuous computational hidden nodes (possibly not neural alike nodes), if SLFNs can work as universal approximators with adjustable hidden node parameters, from a function approximation point of view the hidden node parameters of such “generalized” SLFNs (including sigmoid networks, RBF networks, trigonometric networks, threshold networks, fuzzy inference systems, fully complex neural networks, high-order networks, ridge polynomial networks, wavelet networks, etc.) can actually be randomly generated according to any continuous sampling distribution. In theory, the parameters of these SLFNs can be analytically determined by ELM instead of being tuned.     

Variational Bayesian extreme learning machine

Extreme learning machine (ELM) randomly generates parameters of hidden nodes and then analytically determines the output weights with fast learning speed. The ill-posed problem of parameter matrix of hidden nodes directly causes unstable performance, and the automatical selection problem of the hidden nodes is critical to holding the high efficiency of ELM. Focusing on the ill-posed problem and the automatical selection problem of the hidden nodes, this paper proposes the variational Bayesian extreme learning machine (VBELM). First, the Bayesian probabilistic model is involved into ELM, where the Bayesian prior distribution can avoid the ill-posed problem of hidden node matrix. Then, the variational approximation inference is employed in the Bayesian model to compute the posterior distribution and the independent variational hyperparameters approximately, which can be used to select the hidden nodes automatically. Theoretical analysis and experimental results elucidate that VBELM has stabler performance with more compact architectures, which presents probabilistic predictions comparison with traditional point predictions, and it also provides the hyperparameter criterion for hidden node selection.     

A wavelet extreme learning machine

Extreme learning machine (ELM) has been widely used in various fields to overcome the problem of low training speed of the conventional neural network. Kernel extreme learning machine (KELM) introduces the kernel method to ELM model, which is applicable in Stat ML. However, if the number of samples in Stat ML is too small, perhaps the unbalanced samples cannot reflect the statistical characteristics of the input data, so that the learning ability of Stat ML will be influenced. At the same time, the mix kernel functions used in KELM are conventional functions. Therefore, the selection of kernel function can still be optimized. Based on the problems above, we introduce the weighted method to KELM to deal with the unbalanced samples. Wavelet kernel functions have been widely used in support vector machine and obtain a good classification performance. Therefore, to realize a combination of wavelet analysis and KELM, we introduce wavelet kernel functions to KELM model, which has a mix kernel function of wavelet kernel and sigmoid kernel, and introduce the weighted method to KELM model to balance the sample distribution, and then we propose the weighted wavelet–mix kernel extreme learning machine. The experimental results show that this method can effectively improve the classification ability with better generalization. At the same time, the wavelet kernel functions perform very well compared with the conventional kernel functions in KELM model.     

Local coupled extreme learning machine

Due to the significant efficiency and simple implementation, extreme learning machine (ELM) algorithms enjoy much attention in regression and classification applications recently. Many efforts have been paid to enhance the performance of ELM from both methodology (ELM training strategies) and structure (incremental or pruned ELMs) perspectives. In this paper, a local coupled extreme learning machine (LC-ELM) algorithm is presented. By assigning an address to each hidden node in the input space, LC-ELM introduces a decoupler framework to ELM in order to reduce the complexity of the weight searching space. The activated degree of a hidden node is measured by the membership degree of the similarity between the associated address and the given input. Experimental results confirm that the proposed approach works effectively and generally outperforms the original ELM in both regression and classification applications.     

Manifold regularized extreme learning machine

Extreme learning machine (ELM) works for generalized single-hidden-layer feedforward networks (SLFNs), and its essence is that the hidden layer of SLFNs need not be tuned. But ELM only utilizes labeled data to carry out the supervised learning task. In order to exploit unlabeled data in the ELM model, we first extend the manifold regularization (MR) framework and then demonstrate the relation between the extended MR framework and ELM. Finally, a manifold regularized extreme learning machine is derived from the proposed framework, which maintains the properties of ELM and can be applicable to large-scale learning problems. Experimental results show that the proposed semi-supervised extreme learning machine is the most cost-efficient method. It tends to have better scalability and achieve satisfactory generalization performance at a relatively faster learning speed than traditional semi-supervised learning algorithms.     

Direct adaptive neural control of nonlinear systems with extreme learning machine

A direct adaptive neural control scheme for a class of nonlinear systems is presented in the paper. The proposed control scheme incorporates a neural controller and a sliding mode controller. The neural controller is constructed based on the approximation capability of the single-hidden layer feedforward network (SLFN). The sliding mode controller is built to compensate for the modeling error of SLFN and system uncertainties. In the designed neural controller, its hidden node parameters are modified using the recently proposed neural algorithm named extreme learning machine (ELM), where they are assigned random values. However, different from the original ELM algorithm, the output weight is updated based on the Lyapunov synthesis approach to guarantee the stability of the overall control system. The proposed adaptive neural controller is finally applied to control the inverted pendulum system with two different reference trajectories. The simulation results demonstrate good tracking performance of the proposed control scheme.     

Real-time transient stability status prediction using cost-sensitive extreme learning machine

Real-time transient stability status prediction (RTSSP) is very important to maintain the safety and stability of electrical power systems, where any unstable contingency will be likely to cause large-scale blackout. Most of machine learning methods used for RTSSP attempt to attain a low classification error, which implies that the misclassification costs of different categories are the same. However, misclassifying an unstable case as stable one usually leads to much higher costs than misclassifying a stable case as unstable one. In this paper, a new RTSSP method based on cost-sensitive extreme learning machine (CELM) is proposed, which recognizes the RTSSP as a cost-sensitive classification problem. The CELM is constructed pursuing the minimum misclassification costs, and its detailed implementation procedures for RSSTP are also researched in this work. The proposed method is implemented on the New England 39-bus electrical power system. Compared with three cost-blind methods (ELM, SVM and DT) and two cost-sensitive methods (cost-sensitive DT, cost-sensitive SVM), the simulation results have proved that the lower total misclassification costs and false dismissal rate with low computational complexity can be achieved by the proposed method, which meets the demands for the computation speed and the reliability of RTSSP.     

Intelligent fingerprint quality analysis using online sequential extreme learning machine

Because the quality of fingerprints can be degraded by diverse factors, recognizing the quality of fingerprints in advance can be beneficial for improving the performance of fingerprint authentication systems. This paper proposes an effective fingerprint quality analysis approach based on the online sequential extreme learning machine (OS-ELM). The proposed method is based not only on basic fingerprint properties, but also on the physical properties of the various sensors. Instead of splitting a fingerprint image into traditional small blocks, direction-based segmentation using the Gabor filter is used. From the segmented image, a feature set which consists of four selected independent local or global features: orientation certainty, local orientation quality, consistency, and ridge distance, is extracted. The selected feature set is robust against various factors responsible for quality degradation and can satisfy the requirements of different types of capture sensors. With the contribution of the OS-ELM classifier, the extracted feature set is used to determine whether or not a fingerprint image should be accepted as an input to the recognition system. Experimental results show that the proposed method performs better in terms of accuracy and time consumed than BPNN-based and SVM-based methods. An obvious improvement to the fingerprint recognition system is achieved by adding a quality analysis system. Other comparisons to traditional methods also show that the proposed method outperforms others.     

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images (‘No Reference’ metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.