Evolutionary Extreme Learning Machine Research Articles

Discrete ripplet-II transform and modified PSO based improved evolutionary extreme learning machine for pathological brain detection

Recently there has been remarkable advances in computer-aided diagnosis (CAD) system development for detection of the pathological brain through MR images. Feature extractors like wavelet and its variants, and classifiers like feed-forward neural network (FNN) and support vector machine (SVM) are very often used in these systems despite the fact that they suffer from many limitations. This paper presents an efficient and improved pathological brain detection system (PBDS) that overcomes the problems faced by other PBDSs in the recent literature. First, we support the use of contrast limited adaptive histogram equalization (CLAHE) to enhance the quality of the input MR images. Second, we use discrete ripplet-II transform (DR2T) with degree 2 as the feature extractor. Third, in order to reduce the huge number of coefficients obtained from DR2T, we employ PCA+LDA approach. Finally, an improved hybrid learning algorithm called MPSO-ELM has been proposed that combines modified particle swarm optimization (MPSO) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. In MPSO-ELM, MPSO is utilized to optimize the hidden node parameters (input weights and hidden biases) of single-hidden-layer feedforward neural networks (SLFN) and the output weights are determined analytically. The proposed method is contrasted with the current state-of-the-art methods on three benchmark datasets. Experimental results indicate that our proposed scheme brings potential improvements in terms of classification accuracy and number of features. Additionally, it is observed that the proposed MPSO-ELM algorithm achieves higher accuracy and obtains compact network architecture compared to conventional ELM and BPNN classifier.

An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine.

Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the necessity of real world diagnostic situations. In this paper, an efficient PBDS based on MR images is proposed that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) to enhance the quality of the input MR images. Thereafter, two-dimensional PCA (2DPCA) strategy is employed to extract the features and subsequently, a PCA+LDA approach is used to generate a compact and discriminative feature set. Finally, a new learning algorithm called MDE-ELM is suggested that combines modified differential evolution (MDE) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The MDE is utilized to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks (SLFN), whereas an analytical method is used for determining the output weights. The proposed algorithm performs optimization based on both the root mean squared error (RMSE) and norm of the output weights of SLFNs. The suggested scheme is benchmarked on three standard datasets and the results are compared against other competent schemes. The experimental outcomes show that the proposed scheme offers superior results compared to its counterparts. Further, it has been noticed that the proposed MDE-ELM classifier obtains better accuracy with compact network architecture than conventional algorithms.

An evolutionary extreme learning machine based on chemical reaction optimization

This paper considers an evolutionary extreme learning machine (ELM) based on chemical reaction optimization (CRO) to overcome the drawbacks of ELM, such as the unavoidable existence of a set of unnecessary or non-optimal hidden biases and input weights. By using CRO algorithm to determine the hidden biases and input weights according to both the norm of output weights and the root mean squared error, the classification performance of optimized ELM can be improved. The experimental results on some real benchmark problems show that the proposed method can achieve higher classification accuracy than both other compared evolutionary ELMs and original ELM.

A comparison of the performance of some extreme learning machine empirical models for predicting daily horizontal diffuse solar radiation in a region of southern Iran

ABSTRACTThis study uses the empirical models of extreme learning machine (ELM) method to predict daily horizontal diffuse solar radiation (HDSR). As a possibility for modification, the recent hybrid ELM methods such as complex ELM (C-ELM), self-adaptive evolutionary ELM (SaE-ELM), and online sequential ELM (OS-ELM) have been developed for the prediction of the daily HDSR. The empirical model of ELM predicts the HDSR using clearness index as the sole predictor. For this aim, two types of correlations are evaluated: (1) the diffuse fraction-clearness index and (2) the diffuse coefficient-clearness index. The measured diffuse and global solar radiation data sets of southern Iranian cities (Yazd, Shiraz, Bandar Abbas, Bushehr, and Zahedan) are utilized to evaluate the models. The precision of the C-ELM, SaE-ELM, OS-ELM, and ELM models is evaluated for different regions on the basis of five statistical performance evaluation parameters. The results confirm that the performance of hybrid ELM is pretty accurate and trustworthy. Fully complex ELM exhibits the best performance among these hybrid methods, having values of 0.87 and 0.30 for R2 and RMSE measures, respectively, in the testing phase. Therefore, it is able to be recognized as an appropriate tool for daily solar radiation forecasting issues.

Dolphin Swarm Extreme Learning Machine

As a novel learning algorithm for a single hidden-layer feedforward neural network, the extreme learning machine has attracted much research attention for its fast training speed and good generalization performances. Instead of iteratively tuning the parameters, the extreme machine can be seen as a linear optimization problem by randomly generating the input weights and hidden biases. However, the random determination of the input weights and hidden biases may bring non-optimal parameters, which have a negative impact on the final results or need more hidden nodes for the neural network. To overcome the above drawbacks caused by the non-optimal input weights and hidden biases, we propose a new hybrid learning algorithm named dolphin swarm algorithm extreme learning machine adopting the dolphin swarm algorithm to optimize the input weights and hidden biases efficiently. Each set of input weights and hidden biases is encoded into one vector, namely the dolphin. The dolphins are evaluated by root mean squared error and updated by the four pivotal phases of the dolphin swarm algorithm. Eventually, we will obtain an optimal set of input weights and hidden biases. To evaluate the effectiveness of our method, we compare the proposed algorithm with the standard extreme learning machine and three state-of-the-art methods, which are the particle swarm optimization extreme learning machine, evolutionary extreme learning machine, and self-adaptive evolutionary extreme learning machine, under 13 benchmark datasets obtained from the University of California Irvine Machine Learning Repository. The experimental results demonstrate that the proposed method can achieve superior generalization performances than all the compared algorithms.

Radar Target Classification Using an Evolutionary Extreme Learning Machine Based on Improved Quantum-Behaved Particle Swarm Optimization

A novel evolutionary extreme learning machine (ELM) based on improved quantum-behaved particle swarm optimization (IQPSO) for radar target classification is presented in this paper. Quantum-behaved particle swarm optimization (QPSO) has been used in ELM to solve the problem that ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. But the method for calculating the characteristic length of Delta potential well of QPSO may reduce the global search ability of the algorithm. To solve this issue, a new method to calculate the characteristic length of Delta potential well is proposed in this paper. Experimental results based on the benchmark functions validate the better performance of IQPSO against QPSO in most cases. The novel algorithm is also evaluated by using real-world datasets and radar data; the experimental results indicate that the proposed algorithm is more effective than BP, SVM, ELM, QPSO-ELM, and so on, in terms of real-time performance and accuracy.

An Optimization Strategy for Weighted Extreme Learning Machine based on PSO

Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous experiences. Among numerous machine learning algorithms, Weighted Extreme Learning Machine (WELM) is one of the famous cases recently. It not only has Extreme Learning Machine (ELM)’s extremely fast training speed and better generalization performance than traditional Neuron Network (NN), but also has the merit in handling imbalance data by assigning more weight to minority class and less weight to majority class. But it still has the limitation of its weight generated according to class distribution of training data, thereby, creating dependency on input data [R. Sharma and A. S. Bist, Genetic algorithm based weighted extreme learning machine for binary imbalance learning, 2015 Int. Conf. Cognitive Computing and Information Processing (CCIP) (IEEE, 2015), pp. 1–6; N. Koutsouleris, Classification/machine learning approaches, Annu. Rev. Clin. Psychol. 13(1) (2016); G. Dudek, Extreme learning machine for function approximation–interval problem of input weights and biases, 2015 IEEE 2nd Int. Conf. Cybernetics (CYBCONF) (IEEE, 2015), pp. 62–67; N. Zhang, Y. Qu and A. Deng, Evolutionary extreme learning machine based weighted nearest-neighbor equality classification, 2015 7th Int. Conf. Intelligent Human-Machine Systems and Cybernetics (IHMSC), Vol. 2 (IEEE, 2015), pp. 274–279]. This leads to the lack of finding optimal weight at which good generalization performance could be achieved [R. Sharma and A. S. Bist, Genetic algorithm based weighted extreme learning machine for binary imbalance learning, 2015 Int. Conf. Cognitive Computing and Information Processing (CCIP) (IEEE, 2015), pp. 1–6; N. Koutsouleris, Classification/machine learning approaches, Annu. Rev. Clin. Psychol. 13(1) (2016); G. Dudek, Extreme learning machine for function approximation–interval problem of input weights and biases, 2015 IEEE 2nd Int. Conf. Cybernetics (CYBCONF) (IEEE, 2015), pp. 62–67; N. Zhang, Y. Qu and A. Deng, Evolutionary extreme learning machine based weighted nearest-neighbor equality classification, 2015 7th Int. Conf. Intelligent Human-Machine Systems and Cybernetics (IHMSC), Vol. 2 (IEEE, 2015), pp. 274–279]. To solve it, a hybrid algorithm which composed by WELM algorithm and Particle Swarm Optimization (PSO) is proposed. Firstly, it distributes the weight according to the number of different samples, determines weighted method; Then, it combines the ELM model and the weighted method to establish WELM model; finally it utilizes PSO to optimize WELM’s three parameters (input weight, bias, the weight of imbalanced training data). Experiment data from both prediction and recognition show that it has better performance than classical WELM algorithms.

Prediction of scour depth around bridge piers using self-adaptive extreme learning machine

Accurate prediction of pier scour can lead to economic design of bridge piers and prevent catastrophic incidents. This paper presents the application of self-adaptive evolutionary extreme learning machine (SAELM) to develop a new model for the prediction of local scour around bridge piers using 476 field pier scour measurements with four shapes of piers: sharp, round, cylindrical, and square. The model network parameters are optimized using the differential evolution algorithm. The best SAELM model calculates the scour depth as a function of pier dimensions and the sediment mean diameter. The developed SAELM model had the lowest error indicators when compared to regression-based prediction models for root mean square error (RMSE) (0.15, 0.65, respectively) and mean absolute relative error (MARE) (0.50, 2.0, respectively). The SAELM model was found to perform better than artificial neural networks or support vector machines on the same dataset. Parametric analysis showed that the new model predictions are influenced by pier dimensions and bed-sediment size and produce similar trends of variations of scour-hole depth as reported in literature and previous experimental measurements. The prediction uncertainty of the developed SAELM model is quantified and compared with existing regression-based models and found to be the least, ±0.03 compared with ±0.10 for other models.

Rational and self-adaptive evolutionary extreme learning machine for electricity price forecast

Electricity price forecast is of great importance to electricity market participants. Given the sophisticated time-series of electricity price, various approaches of extreme learning machine (ELM) have been identified as effective prediction approaches. However, in high dimensional space, evolutionary extreme learning machine (E-ELM) is time-consuming and difficult to converge to optimal region when just relying on stochastic searching approaches. In the meanwhile, due to the complicated functional relationship, objective function of E-ELM seems difficult also to be mined directly for some useful mathematical information to guide the optimum exploring. This paper proposes a new differential evolution (DE) like algorithm to enhance E-ELM for more accurate and reliable prediction of electricity price. An approximation model for producing DE-like trail vector is the key mechanism, which can use simpler mathematical mapping to replace the original yet complicated functional relationship within a small region. Thus, the evolutionary procedure frequently dealt with some rational searching directions can make the E-ELM more robust and faster than supported only by the stochastic methods. Experimental results show that the new method can improve the performance of E-ELM more efficiently.

A soft sensor for industrial melt index prediction based on evolutionary extreme learning machine

In propylene polymerization (PP) process, the melt index (MI) is one of the most important quality variables for determining different brands of products and different grades of product quality. Accurate prediction of MI is essential for efficient and professional monitoring and control of practical PP processes. This paper presents a novel soft sensor based on extreme learning machine (ELM) and modified gravitational search algorithm (MGSA) to estimate MI from real PP process variables, where the MGSA algorithm is developed to find the best parameters of input weights and hidden biases for ELM. As the comparative basis, the models of ELM, APSO-ELM and GSA-ELM are also developed respectively. Based on the data from a real PP production plant, a detailed comparison of the models is carried out. The research results show the accuracy and universality of the proposed model and it can be a powerful tool for online MI prediction.

Evolutionary extreme learning machine for energy price forecasting

Evolutionary extreme learning machine for energy price forecasting

Interpretation of DGA for transformer fault diagnosis with complementary SaE-ELM and arctangent transform

This paper presents a novel approach for power transformer incipient fault diagnosis through the analysis of dissolved gas in oil. The proposed approach is implemented for improving the diagnosis accuracy by dissolved gas analysis (DGA) of power transformer based on the combined use of a multi-classification algorithm self-adaptive evolutionary extreme learning machine (SaE-ELM) and a simple arctangent transform (AT). On the one hand, the SaE-ELM algorithm has the ability to approximate any nonlinear functions with its structure parameters, i.e. hidden node biases and output weights, optimized self-sufficiently. On the other hand, the AT can alter the data structure of the experiment data, which will enhance the generalization capability for SaE-ELM as well as other machine learning algorithms. Thus, the combination of SaEELM and AT can complement each other and improve the diagnosis accuracy from the aspect of both algorithm and data structure. The performances of the proposed approach are compared with that derived from ANN, SVM, and ELM methods, respectively. Experimental results with both published and power utility provided data indicate that the developed approach can significantly improve the accuracies for power transformer fault diagnosis.

Differential evolution-based feature selection and parameter optimisation for extreme learning machine in tool wear estimation

Cutting tool wear degrades the product quality in manufacturing processes. Hence, real-time online estimation of tool wear is important for suggesting a tool replacement before the wear limit is reached, in order to protect the workpiece and the CNC machine from damage and breakdown. In this study, using both statistical features and wavelet features extracted from sensor signals, an adaptive evolutionary extreme learning machine (ELM) learning paradigm is developed for tool wear estimation in high-speed milling process. In the proposed method, a discrete differential evolution (DE) algorithm is used to select input features for the ELM, and a continuous DE algorithm is used for parameter optimisation of the mixed kernel function for the ELM. The experimental results indicate that the proposed adaptive evolutionary ELM-based tool wear estimation model can effectively estimate the tool wear in high-speed milling process. Empirical comparisons show that the proposed model performs better than existing approaches in estimating the tool wear.

Short-term load forecasting by wavelet transform and evolutionary extreme learning machine

This paper proposes a novel short-term load forecasting (STLF) method based on wavelet transform, extreme learning machine (ELM) and modified artificial bee colony (MABC) algorithm. The wavelet transform is used to decompose the load series for capturing the complicated features at different frequencies. Each component of the load series is then separately forecasted by a hybrid model of ELM and MABC (ELM-MABC). The global search technique MABC is developed to find the best parameters of input weights and hidden biases for ELM. Compared to the conventional neuro-evolution method, ELM-MABC can improve the learning accuracy with fewer iteration steps. The proposed method is tested on two datasets: ISO New England data and North American electric utility data. Numerical testing shows that the proposed method can obtain superior results as compared to other standard and state-of-the-art methods.

Vehicle speed prediction via a sliding-window time series analysis and an evolutionary least learning machine: A case study on San Francisco urban roads

The main goal of the current study is to take advantage of advanced numerical and intelligent tools to predict the speed of a vehicle using time series. It is clear that the uncertainty caused by temporal behavior of the driver as well as various external disturbances on the road will affect the vehicle speed, and thus, the vehicle power demands. The prediction of upcoming power demands can be employed by the vehicle powertrain control systems to improve significantly the fuel economy and emission performance. Therefore, it is important to systems design engineers and automotive industrialists to develop efficient numerical tools to overcome the risk of unpredictability associated with the vehicle speed profile on roads. In this study, the authors propose an intelligent tool called evolutionary least learning machine (E-LLM) to forecast the vehicle speed sequence. To have a practical evaluation regarding the efficacy of E-LLM, the authors use the driving data collected on the San Francisco urban roads by a private Honda Insight vehicle. The concept of sliding window time series (SWTS) analysis is used to prepare the database for the speed forecasting process. To evaluate the performance of the proposed technique, a number of well-known approaches, such as auto regressive (AR) method, back-propagation neural network (BPNN), evolutionary extreme learning machine (E-ELM), extreme learning machine (ELM), and radial basis function neural network (RBFNN), are considered. The performances of the rival methods are then compared in terms of the mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), median absolute percentage error (MDAPE), and absolute fraction of variances (R2) metrics. Through an exhaustive comparative study, the authors observed that E-LLM is a powerful tool for predicting the vehicle speed profiles. The outcomes of the current study can be of use for the engineers of automotive industry who have been seeking fast, accurate, and inexpensive tools capable of predicting vehicle speeds up to a given point ahead of time, known as prediction horizon (HP), which can be used for designing efficient predictive powertrain controllers.

Multi-Level Forecasting Model of Coal Mine Water Inrush based on Self-Adaptive Evolutionary Extreme Learning Machine

This paper constructs a forecasting model for coal mining water inrush from the floor through the analysis and careful study of the mechanism of water inrush in coal mining and the Self-Adaptive Evolutionary Extreme Learning Machine (SaE-ELM) which obtains self-learning, generalization performance and speediness is used. In SaE-ELM, the network hidden node parameters are optimized by the self-adaptive differential evolution algorithm, whose trial vector generation strategies and th eir associated control parameters are self-adapted in a strategy pool by learning f rom their previous experiences in generating promising solutions. Large amounts of historical data of mining water inrush is collected and the main controlling factors are extracted as sample data to train and test the forecasting model by the SaE-ELM, which can forecast both the existence of a water inrush from the floor and the lev el of the water inrush. Experiments are given to prove that the proposed method reduces the time of model construction and computation, and improves the speed and accuracy of the forecast of coal mining water inrush.

Multifault Diagnosis for Rolling Element Bearings Based on Intrinsic Mode Permutation Entropy and Ensemble Optimal Extreme Learning Machine

This paper presented a novel procedure based on the ensemble empirical mode decomposition and extreme learning machine. Firstly, EEMD was utilized to decompose the vibration signals into a number of IMFs adaptively and the permutation entropy of each IMF was calculated to generate the fault feature matrix. Secondly, a new extreme learning machine was proposed by combining ensemble extreme learning machine and the evolutionary extreme learning machine which used an artificial bee colony algorithm to optimize the input weights and hidden bias. The proposed diagnosis algorithm was applied on the three rolling bearing fault diagnosis experiments. The numerical experimental results demonstrated that the proposed method had an improved generalization performance than traditional extreme and other variants.

Microarray Gene Expression and Multiclass Cancer Classification Using Improved PSO Based Evolutionary Fuzzy ELM Classifier with ICGA Gene Selection

Cancer has become one of the dreadful and most widely spreading diseases in recent years. Cancer diagnosis has become an active research area in the field of medical image processing. DNA microarrays are considered as an effectual tool used in molecular biology and cancer diagnosis. As the superiority of this method has been recognized, a variety of open queries occur about appropriate test of microarray data. As the numbers of cancer victims are increasing tremendously, the requirement of an efficient and accurate cancer classification system has become essential. For the above impenetrability and to obtain better consequences of the system with accuracy a combination of Integer-Coded Genetic Algorithm (ICGA) and Improved Particle Swarm Optimization (IPSO), coupled with an evolutionary fuzzy extreme learning machine (E-FELM), is used for gene selection and cancer classification. ICGA is used with IPSO based E-FELM classifier to chose an optimal set of genes which results in an efficient hybrid algorithm that can handle sparse data and sample imbalance. The performance of the proposed approach is evaluated and the results are compared with existing methods.

Multi-fault classification based on the two-stage evolutionary extreme learning machine and improved artificial bee colony algorithm

Extreme Learning Machine (ELM) is a novel single-hidden-layer feed forward neural network with fast learning speed and better generalization performance compared with the traditional gradient-based learning algorithms. However, ELM has two issues: the hidden node number of ELM needs to be predefined and the random determination of the input weights and hidden biases lead to ill-condition problem. In this paper, a two-stage evolutionary extreme learning machine (TSE-ELM) algorithm was proposed to overcome the drawbacks of original ELM, which used an improved artificial bee colony (ABC) algorithm to optimize the input weights and hidden biases. The proposed TSE-ELM algorithm was applied on the UCI benchmark datasets and rolling bearing fault diagnosis. The numerical experimental results demonstrated that TSE-ELM had an improved generalization performance than traditional ELM and other evolutionary ELMs.

Quantitative Analysis of Digitized Mammograms Using Nonsubsampled Contourlets and Evolutionary Extreme Learning Machine

Quantitative Analysis of Digitized Mammograms Using Nonsubsampled Contourlets and Evolutionary Extreme Learning Machine